Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems.
Endogenous substances, usually proteins, which are effective in the initiation, stimulation, or termination of the genetic transcription process.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action during the developmental stages of an organism.
Computer-based representation of physical systems and phenomena such as chemical processes.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.
Somewhat flattened, globular echinoderms, having thin, brittle shells of calcareous plates. They are useful models for studying FERTILIZATION and EMBRYO DEVELOPMENT.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control (induction or repression) of gene action at the level of transcription or translation.
A species of SEA URCHINS in the family Strongylocentrotidae found on the Pacific coastline from Alaska to Mexico. This species serves as a major research model for molecular developmental biology and other fields.
The intracellular transfer of information (biological activation/inhibition) through a signal pathway. In each signal transduction system, an activation/inhibition signal from a biologically active molecule (hormone, neurotransmitter) is mediated via the coupling of a receptor/enzyme to a second messenger system or to an ion channel. Signal transduction plays an important role in activating cellular functions, cell differentiation, and cell proliferation. Examples of signal transduction systems are the GAMMA-AMINOBUTYRIC ACID-postsynaptic receptor-calcium ion channel system, the receptor-mediated T-cell activation pathway, and the receptor-mediated activation of phospholipases. Those coupled to membrane depolarization or intracellular release of calcium include the receptor-mediated activation of cytotoxic functions in granulocytes and the synaptic potentiation of protein kinase activation. Some signal transduction pathways may be part of larger signal transduction pathways; for example, protein kinase activation is part of the platelet activation signal pathway.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
Databases devoted to knowledge about specific genes and gene products.
A phylum of the most familiar marine invertebrates. Its class Stelleroidea contains two subclasses, the Asteroidea (the STARFISH or sea stars) and the Ophiuroidea (the brittle stars, also called basket stars and serpent stars). There are 1500 described species of STARFISH found throughout the world. The second class, Echinoidea, contains about 950 species of SEA URCHINS, heart urchins, and sand dollars. A third class, Holothuroidea, comprises about 900 echinoderms known as SEA CUCUMBERS. Echinoderms are used extensively in biological research. (From Barnes, Invertebrate Zoology, 5th ed, pp773-826)
The developmental entity of a fertilized egg (ZYGOTE) in animal species other than MAMMALS. For chickens, use CHICK EMBRYO.
Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites.
Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.
Sequential operating programs and data which instruct the functioning of a digital computer.
The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.
The systematic study of the complete DNA sequences (GENOME) of organisms.
Small double-stranded, non-protein coding RNAs, 21-25 nucleotides in length generated from single-stranded microRNA gene transcripts by the same RIBONUCLEASE III, Dicer, that produces small interfering RNAs (RNA, SMALL INTERFERING). They become part of the RNA-INDUCED SILENCING COMPLEX and repress the translation (TRANSLATION, GENETIC) of target RNA by binding to homologous 3'UTR region as an imperfect match. The small temporal RNAs (stRNAs), let-7 and lin-4, from C. elegans, are the first 2 miRNAs discovered, and are from a class of miRNAs involved in developmental timing.
The pattern of GENE EXPRESSION at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.
Cellular processes, properties, and characteristics.
Commonly observed BASE SEQUENCE or nucleotide structural components which can be represented by a CONSENSUS SEQUENCE or a SEQUENCE LOGO.
The inner of the three germ layers of an embryo.
The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION.
A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
A mechanism of communication with a physiological system for homeostasis, adaptation, etc. Physiological feedback is mediated through extensive feedback mechanisms that use physiological cues as feedback loop signals to control other systems.
Any of the processes by which cytoplasmic or intercellular factors influence the differential control of gene action in bacteria.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Proteins encoded by homeobox genes (GENES, HOMEOBOX) that exhibit structural similarity to certain prokaryotic and eukaryotic DNA-binding proteins. Homeodomain proteins are involved in the control of gene expression during morphogenesis and development (GENE EXPRESSION REGULATION, DEVELOPMENTAL).
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in plants.
Nucleotide sequences of a gene that are involved in the regulation of GENETIC TRANSCRIPTION.
A species of the genus SACCHAROMYCES, family Saccharomycetaceae, order Saccharomycetales, known as "baker's" or "brewer's" yeast. The dried form is used as a dietary supplement.
Methods for determining interaction between PROTEINS.
The protein complement of an organism coded for by its genome.
A plant genus of the family BRASSICACEAE that contains ARABIDOPSIS PROTEINS and MADS DOMAIN PROTEINS. The species A. thaliana is used for experiments in classical plant genetics as well as molecular genetic studies in plant physiology, biochemistry, and development.
The processes occurring in early development that direct morphogenesis. They specify the body plan ensuring that cells will proceed to differentiate, grow, and diversify in size and shape at the correct relative positions. Included are axial patterning, segmentation, compartment specification, limb position, organ boundary patterning, blood vessel patterning, etc.
The middle germ layer of an embryo derived from three paired mesenchymal aggregates along the neural tube.
Progressive restriction of the developmental potential and increasing specialization of function that leads to the formation of specialized cells, tissues, and organs.
Genes which regulate or circumscribe the activity of other genes; specifically, genes which code for PROTEINS or RNAs which have GENE EXPRESSION REGULATION functions.
The genetic complement of an organism, including all of its GENES, as represented in its DNA, or in some cases, its RNA.
The characteristic properties and processes involved in IMMUNITY and an organism's immune response.
A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.
The parts of a macromolecule that directly participate in its specific combination with another molecule.
A technique for identifying specific DNA sequences that are bound, in vivo, to proteins of interest. It involves formaldehyde fixation of CHROMATIN to crosslink the DNA-BINDING PROTEINS to the DNA. After shearing the DNA into small fragments, specific DNA-protein complexes are isolated by immunoprecipitation with protein-specific ANTIBODIES. Then, the DNA isolated from the complex can be identified by PCR amplification and sequencing.
The outer of the three germ layers of an embryo.
The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics.
A process of complicated morphogenetic cell movements that reorganizes a bilayer embryo into one with three GERM LAYERS and specific orientation (dorsal/ventral; anterior/posterior). Gastrulation describes the germ layer development of a non-mammalian BLASTULA or that of a mammalian BLASTOCYST.
The developmental history of specific differentiated cell types as traced back to the original STEM CELLS in the embryo.
DNA sequences which are recognized (directly or indirectly) and bound by a DNA-dependent RNA polymerase during the initiation of transcription. Highly conserved sequences within the promoter include the Pribnow box in bacteria and the TATA BOX in eukaryotes.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in fungi.
The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.
A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
The only species of a cosmopolitan ascidian.
Tabular numerical representations of sequence motifs displaying their variability as likelihood values for each possible residue at each position in a sequence. Position-specific scoring matrices (PSSMs) are calculated from position frequency matrices.
Animals having a vertebral column, members of the phylum Chordata, subphylum Craniata comprising mammals, birds, reptiles, amphibians, and fishes.
The rigid framework of connected bones that gives form to the body, protects and supports its soft organs and tissues, and provides attachments for MUSCLES.
An early non-mammalian embryo that follows the MORULA stage. A blastula resembles a hollow ball with the layer of cells surrounding a fluid-filled cavity (blastocele). The layer of cells is called BLASTODERM.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.
Echinoderms having bodies of usually five radially disposed arms coalescing at the center.
Cells derived from the BLASTOCYST INNER CELL MASS which forms before implantation in the uterine wall. They retain the ability to divide, proliferate and provide progenitor cells that can differentiate into specialized cells.
In eukaryotes, a genetic unit consisting of a noncontiguous group of genes under the control of a single regulator gene. In bacteria, regulons are global regulatory systems involved in the interplay of pleiotropic regulatory domains and consist of several OPERONS.
The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.
Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The construction or arrangement of a task so that it may be done with the greatest possible efficiency.
A genus of SEA URCHINS in the family Toxopneustidae possessing trigeminate ambulacral plating.
The functional hereditary units of PLANTS.
A form of gene interaction whereby the expression of one gene interferes with or masks the expression of a different gene or genes. Genes whose expression interferes with or masks the effects of other genes are said to be epistatic to the effected genes. Genes whose expression is affected (blocked or masked) are hypostatic to the interfering genes.
Graphs representing sets of measurable, non-covalent physical contacts with specific PROTEINS in living organisms or in cells.
A species of nematode that is widely used in biological, biochemical, and genetic studies.
A genus of small, two-winged flies containing approximately 900 described species. These organisms are the most extensively studied of all genera from the standpoint of genetics and cytology.
The two longitudinal ridges along the PRIMITIVE STREAK appearing near the end of GASTRULATION during development of nervous system (NEURULATION). The ridges are formed by folding of NEURAL PLATE. Between the ridges is a neural groove which deepens as the fold become elevated. When the folds meet at midline, the groove becomes a closed tube, the NEURAL TUBE.
Proteins that originate from plants species belonging to the genus ARABIDOPSIS. The most intensely studied species of Arabidopsis, Arabidopsis thaliana, is commonly used in laboratory experiments.
The relationships of groups of organisms as reflected by their genetic makeup.
The portion of an interactive computer program that issues messages to and receives commands from a user.
Morphological and physiological development of EMBRYOS.
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
A sequence of amino acids in a polypeptide or of nucleotides in DNA or RNA that is similar across multiple species. A known set of conserved sequences is represented by a CONSENSUS SEQUENCE. AMINO ACID MOTIFS are often composed of conserved sequences.
RNA sequences that serve as templates for protein synthesis. Bacterial mRNAs are generally primary transcripts in that they do not require post-transcriptional processing. Eukaryotic mRNA is synthesized in the nucleus and must be exported to the cytoplasm for translation. Most eukaryotic mRNAs have a sequence of polyadenylic acid at the 3' end, referred to as the poly(A) tail. The function of this tail is not known for certain, but it may play a role in the export of mature mRNA from the nucleus as well as in helping stabilize some mRNA molecules by retarding their degradation in the cytoplasm.
Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.
A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.
A species of fruit fly much used in genetics because of the large size of its chromosomes.
Proteins that originate from insect species belonging to the genus DROSOPHILA. The proteins from the most intensely studied species of Drosophila, DROSOPHILA MELANOGASTER, are the subject of much interest in the area of MORPHOGENESIS and development.
A technique that localizes specific nucleic acid sequences within intact chromosomes, eukaryotic cells, or bacterial cells through the use of specific nucleic acid-labeled probes.
A subclass of SOX transcription factors that are expressed in neuronal tissue where they may play a role in the regulation of CELL DIFFERENTIATION. Members of this subclass are generally considered to be transcriptional activators.
The condition in which reasonable knowledge regarding risks, benefits, or the future is not available.
Elements of limited time intervals, contributing to particular results or situations.
Proteins found in any species of bacterium.
A family of DNA-binding transcription factors that contain a basic HELIX-LOOP-HELIX MOTIF.
Proteins obtained from the species SACCHAROMYCES CEREVISIAE. The function of specific proteins from this organism are the subject of intense scientific interest and have been used to derive basic understanding of the functioning similar proteins in higher eukaryotes.
A species of gram-positive, asporogenous, non-pathogenic, soil bacteria that produces GLUTAMIC ACID.
A complex signaling pathway whose name is derived from the DROSOPHILA Wg gene, which when mutated results in the wingless phenotype, and the vertebrate INT gene, which is located near integration sites of MOUSE MAMMARY TUMOR VIRUS. The signaling pathway is initiated by the binding of WNT PROTEINS to cells surface WNT RECEPTORS which interact with the AXIN SIGNALING COMPLEX and an array of second messengers that influence the actions of BETA CATENIN.
The reproductive organs of plants.
Proteins which maintain the transcriptional quiescence of specific GENES or OPERONS. Classical repressor proteins are DNA-binding proteins that are normally bound to the OPERATOR REGION of an operon, or the ENHANCER SEQUENCES of a gene until a signal occurs that causes their release.
A meshlike structure composed of interconnecting nerve cells that are separated at the synaptic junction or joined to one another by cytoplasmic processes. In invertebrates, for example, the nerve net allows nerve impulses to spread over a wide area of the net because synapses can pass information in any direction.
Proteins which bind to DNA. The family includes proteins which bind to both double- and single-stranded DNA and also includes specific DNA binding proteins in serum which can be used as markers for malignant diseases.
The process in which substances, either endogenous or exogenous, bind to proteins, peptides, enzymes, protein precursors, or allied compounds. Specific protein-binding measures are often used as assays in diagnostic assessments.
Formation of differentiated cells and complicated tissue organization to provide specialized functions.
Directed modification of the gene complement of a living organism by such techniques as altering the DNA, substituting genetic material by means of a virus, transplanting whole nuclei, transplanting cell hybrids, etc.
The development of anatomical structures to create the form of a single- or multi-cell organism. Morphogenesis provides form changes of a part, parts, or the whole organism.
The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species.
The simultaneous analysis, on a microchip, of multiple samples or targets arranged in an array format.
A mechanism of communication within a system in that the input signal generates an output response which returns to influence the continued activity or productivity of that system.
A set of genes descended by duplication and variation from some ancestral gene. Such genes may be clustered together on the same chromosome or dispersed on different chromosomes. Examples of multigene families include those that encode the hemoglobins, immunoglobulins, histocompatibility antigens, actins, tubulins, keratins, collagens, heat shock proteins, salivary glue proteins, chorion proteins, cuticle proteins, yolk proteins, and phaseolins, as well as histones, ribosomal RNA, and transfer RNA genes. The latter three are examples of reiterated genes, where hundreds of identical genes are present in a tandem array. (King & Stanfield, A Dictionary of Genetics, 4th ed)
An exotic species of the family CYPRINIDAE, originally from Asia, that has been introduced in North America. They are used in embryological studies and to study the effects of certain chemicals on development.
The complex series of phenomena, occurring between the end of one CELL DIVISION and the end of the next, by which cellular material is duplicated and then divided between two daughter cells. The cell cycle includes INTERPHASE, which includes G0 PHASE; G1 PHASE; S PHASE; and G2 PHASE, and CELL DIVISION PHASE.
The founding member of the nodal signaling ligand family of proteins. Nodal protein was originally discovered in the region of the mouse embryo primitive streak referred to as HENSEN'S NODE. It is expressed asymmetrically on the left side in chordates and plays a critical role in the genesis of left-right asymmetry during vertebrate development.
A multistage process that includes cloning, physical mapping, subcloning, sequencing, and information analysis of an RNA SEQUENCE.
The functional hereditary units of INSECTS.
Nucleic acid sequences involved in regulating the expression of genes.
An octamer transcription factor that is expressed primarily in totipotent embryonic STEM CELLS and GERM CELLS and is down-regulated during CELL DIFFERENTIATION.
The phenotypic manifestation of a gene or genes by the processes of GENETIC TRANSCRIPTION and GENETIC TRANSLATION.
The unfavorable effect of environmental factors (stressors) on the physiological functions of an organism. Prolonged unresolved physiological stress can affect HOMEOSTASIS of the organism, and may lead to damaging or pathological conditions.
A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
The physiological mechanisms that govern the rhythmic occurrence of certain biochemical, physiological, and behavioral phenomena.
Cis-acting DNA sequences which can increase transcription of genes. Enhancers can usually function in either orientation and at various distances from a promoter.
The usually underground portions of a plant that serve as support, store food, and through which water and mineral nutrients enter the plant. (From American Heritage Dictionary, 1982; Concise Dictionary of Biology, 1990)
Proteins containing a region of conserved sequence, about 200 amino acids long, which encodes a particular sequence specific DNA binding domain (the T-box domain). These proteins are transcription factors that control developmental pathways. The prototype of this family is the mouse Brachyury (or T) gene product.
The study of chance processes or the relative frequency characterizing a chance process.
Warm-blooded vertebrate animals belonging to the class Mammalia, including all that possess hair and suckle their young.
Cells that can give rise to cells of the three different GERM LAYERS.
Screening techniques first developed in yeast to identify genes encoding interacting proteins. Variations are used to evaluate interplay between proteins and other molecules. Two-hybrid techniques refer to analysis for protein-protein interactions, one-hybrid for DNA-protein interactions, three-hybrid interactions for RNA-protein interactions or ligand-based interactions. Reverse n-hybrid techniques refer to analysis for mutations or other small molecules that dissociate known interactions.
Proteins from the nematode species CAENORHABDITIS ELEGANS. The proteins from this species are the subject of scientific interest in the area of multicellular organism MORPHOGENESIS.
A gene silencing phenomenon whereby specific dsRNAs (RNA, DOUBLE-STRANDED) trigger the degradation of homologous mRNA (RNA, MESSENGER). The specific dsRNAs are processed into SMALL INTERFERING RNA (siRNA) which serves as a guide for cleavage of the homologous mRNA in the RNA-INDUCED SILENCING COMPLEX. DNA METHYLATION may also be triggered during this process.
A variation of the PCR technique in which cDNA is made from RNA via reverse transcription. The resultant cDNA is then amplified using standard PCR protocols.
The genetic complement of a BACTERIA as represented in its DNA.
A family of transcription factors that control EMBRYONIC DEVELOPMENT within a variety of cell lineages. They are characterized by a highly conserved paired DNA-binding domain that was first identified in DROSOPHILA segmentation genes.
Diffusible gene products that act on homologous or heterologous molecules of viral or cellular DNA to regulate the expression of proteins.
The artificial induction of GENE SILENCING by the use of RNA INTERFERENCE to reduce the expression of a specific gene. It includes the use of DOUBLE-STRANDED RNA, such as SMALL INTERFERING RNA and RNA containing HAIRPIN LOOP SEQUENCE, and ANTI-SENSE OLIGONUCLEOTIDES.
Wnt proteins are a large family of secreted glycoproteins that play essential roles in EMBRYONIC AND FETAL DEVELOPMENT, and tissue maintenance. They bind to FRIZZLED RECEPTORS and act as PARACRINE PROTEIN FACTORS to initiate a variety of SIGNAL TRANSDUCTION PATHWAYS. The canonical Wnt signaling pathway stabilizes the transcriptional coactivator BETA CATENIN.
Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.
Changes in biological features that help an organism cope with its ENVIRONMENT. These changes include physiological (ADAPTATION, PHYSIOLOGICAL), phenotypic and genetic changes.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
Processes that stimulate the GENETIC TRANSCRIPTION of a gene or set of genes.
A system containing any combination of computers, computer terminals, printers, audio or visual display devices, or telephones interconnected by telecommunications equipment or cables: used to transmit or receive information. (Random House Unabridged Dictionary, 2d ed)
Proteins obtained from the ZEBRAFISH. Many of the proteins in this species have been the subject of studies involving basic embryological development (EMBRYOLOGY).
The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.
ANIMALS whose GENOME has been altered by GENETIC ENGINEERING, or their offspring.
The entity of a developing mammal (MAMMALS), generally from the cleavage of a ZYGOTE to the end of embryonic differentiation of basic structures. For the human embryo, this represents the first two months of intrauterine development preceding the stages of the FETUS.
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
Genetic loci associated with a QUANTITATIVE TRAIT.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.
The complex processes of initiating CELL DIFFERENTIATION in the embryo. The precise regulation by cell interactions leads to diversity of cell types and specific pattern of organization (EMBRYOGENESIS).
The complete gene complement contained in a set of chromosomes in a fungus.
Proteins found in plants (flowers, herbs, shrubs, trees, etc.). The concept does not include proteins found in vegetables for which VEGETABLE PROTEINS is available.
A deoxyribonucleotide polymer that is the primary genetic material of all cells. Eukaryotic and prokaryotic organisms normally contain DNA in a double-stranded state, yet several important biological processes transiently involve single-stranded regions. DNA, which consists of a polysugar-phosphate backbone possessing projections of purines (adenine and guanine) and pyrimidines (thymine and cytosine), forms a double helix that is held together by hydrogen bonds between these purines and pyrimidines (adenine to thymine and guanine to cytosine).
The process of pictorial communication, between human and computers, in which the computer input and output have the form of charts, drawings, or other appropriate pictorial representation.
Multicellular, eukaryotic life forms of kingdom Plantae (sensu lato), comprising the VIRIDIPLANTAE; RHODOPHYTA; and GLAUCOPHYTA; all of which acquired chloroplasts by direct endosymbiosis of CYANOBACTERIA. They are characterized by a mainly photosynthetic mode of nutrition; essentially unlimited growth at localized regions of cell divisions (MERISTEMS); cellulose within cells providing rigidity; the absence of organs of locomotion; absence of nervous and sensory systems; and an alternation of haploid and diploid generations.
Organizations and individuals cooperating together toward a common goal at the local or grassroots level.
A species of gram-negative, rod-shaped bacteria belonging to the K serogroup of ESCHERICHIA COLI. It lives as a harmless inhabitant of the human LARGE INTESTINE and is widely used in medical and GENETIC RESEARCH.
The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.
Relatively undifferentiated cells that retain the ability to divide and proliferate throughout postnatal life to provide progenitor cells that can differentiate into specialized cells.
Sets of structured vocabularies used for describing and categorizing genes, and gene products by their molecular function, involvement in biological processes, and cellular location. These vocabularies and their associations to genes and gene products (Gene Ontology annotations) are generated and curated by the Gene Ontology Consortium.
Any method used for determining the location of and relative distances between genes on a chromosome.
A general term for single-celled rounded fungi that reproduce by budding. Brewers' and bakers' yeasts are SACCHAROMYCES CEREVISIAE; therapeutic dried yeast is YEAST, DRIED.
A field of biological research combining engineering in the formulation, design, and building (synthesis) of novel biological structures, functions, and systems.

Where are we in genomics? (1/4873)

Genomic studies provide scientists with methods to quickly analyse genes and their products en masse. The first high-throughput techniques to be developed were sequencing methods. A great number of genomes from different organisms have thus been sequenced. Genomics is now shifting to the study of gene expression and function. In the past 5-10 years genomics, proteomics and high-throughput microarray technologies have fundamentally changed our ability to study the molecular basis of cells and tissues in health and diseases, giving a new comprehensive view. For example, in cancer research we have seen new diagnostic opportunities for tumour classification, and prognostication. A new exciting development is metabolomics and lab-on-a-chip techniques (which combine miniaturization and automation) for metabolic studies. However, to interpret the large amount of data, extensive computational development is required. In the coming years, we will see the study of biological networks dominating the scene in Physiology. The great accumulation of genomics information will be used in computer programs to simulate biologic processes. Originally developed for genome analysis, bioinformatics now encompasses a wide range of fields in biology from gene studies to integrated biology (i.e. combination of different data sets from genes to metabolites). This is systems biology which aims to study biological organisms as a whole. In medicine, scientific results and applied biotechnologies arising from genomics will be used for effective prediction of diseases and risk associated with drugs. Preventive medicine and medical therapy will be personalized. Widespread applications of genomics for personalized medicine will require associations of gene expression pattern with diagnoses, treatment and clinical data. This will help in the discovery and development of drugs. In agriculture and animal science, the outcomes of genomics will include improvement in food safety, in crop yield, in traceability and in quality of animal products (dairy products and meat) through increased efficiency in breeding and better knowledge of animal physiology. Genomics and integrated biology are huge tasks and no single lab can pursue this alone. We are probably at the end of the beginning rather than at the beginning of the end because Genomics will probably change Biology to a greater extent than previously forecasted. In addition, there is a great need for more information and better understanding of genomics before complete public acceptance.  (+info)

A novel C. elegans zinc finger transcription factor, lsy-2, required for the cell type-specific expression of the lsy-6 microRNA. (2/4873)

The two Caenorhabditis elegans gustatory neurons, ASE left (ASEL) and ASE right (ASER) are morphologically bilaterally symmetric, yet left/right asymmetric in function and in the expression of specific chemosensory signaling molecules. The ASEL versus ASER cell-fate decision is controlled by a complex gene regulatory network composed of microRNAs (miRNAs) and transcription factors. Alterations in the activities of each of these regulatory factors cause a complete lateral cell-fate switch. Here, we describe lsy-2, a novel C2H2 zinc finger transcription factor that is required for the execution of the ASEL stable state. In lsy-2 null mutants, the ASEL neuron adopts the complete ASER gene expression profile, including both upstream regulatory and terminal effector genes. The normally left/right asymmetric ASE neurons are therefore ;symmetrized' in lsy-2 mutants. Cell-specific rescue experiments indicate that lsy-2 is required autonomously in ASEL for the activation of ASEL-specifying factors and the repression of ASER-specifying factors. Genetic epistasis experiments demonstrate that lsy-2 exerts its activity by regulating the transcription of the lsy-6 miRNA in the ASEL neuron, thereby making lsy-2 one of the few factors known to control the cell-type specificity of miRNA gene expression.  (+info)

Identification of novel transcriptional networks in response to treatment with the anticarcinogen 3H-1,2-dithiole-3-thione. (3/4873)

3H-1,2-dithiole-3-thione (D3T), an inducer of antioxidant and phase 2 genes, is known to enhance the detoxification of environmental carcinogens, prevent neoplasia, and elicit other protective effects. However, a comprehensive view of the regulatory pathways induced by this compound has not yet been elaborated. Fischer F344 rats were gavaged daily for 5 days with vehicle or D3T (0.3 mmol/kg). The global changes of gene expression in liver were measured with Affymetrix RG-U34A chips. With the use of functional class scoring, a semi-supervised method exploring both the expression pattern and the functional annotation of the genes, the Gene Ontology classes were ranked according to the significance of the impact of D3T treatment. Two unexpected functional classes were identified for the D3T treatment, cytosolic ribosome constituents with 90% of those genes increased, and cholesterol biosynthesis with 91% of the genes repressed. In another novel approach, the differentially expressed genes were evaluated by the Ingenuity computational pathway analysis tool to identify specific regulatory networks and canonical pathways responsive to D3T treatment. In addition to the known glutathione metabolism pathway (P = 0.0011), several other significant pathways were also revealed, including antigen presentation (P = 0.000476), androgen/estrogen biosynthesis (P = 0.000551), fatty acid (P = 0.000216), and tryptophan metabolism (P = 0.000331) pathways. These findings showed a profound impact of D3T on lipid metabolism and anti-inflammatory/immune-suppressive response, indicating a broader cytoprotective effect of this compound than previously expected.  (+info)

A genome-scale assessment of peripheral blood B-cell molecular homeostasis in patients with rheumatoid arthritis. (4/4873)

OBJECTIVE: While rheumatoid arthritis (RA) is considered a prototypical autoimmune disease, the specific roles of B-cells in RA pathogenesis is not fully delineated. METHODS: We performed microarray expression profiling of peripheral blood B-cells from RA patients and controls. Data were analysed using differential gene expression analysis and 'gene networking' analysis (characterizing clusters of functionally inter-relelated genes) to identify both regulatory genes and the pathways in which they participate. Results were confirmed by quantitative real-time polymerase chain reaction and by measuring the levels of 10 serum cytokines involved in the pathways identified. RESULTS: Genes regulating and effecting the cell-cycle, proliferation, apoptosis, autoimmunity, cytokine networks, angiogenesis and neuro-immune regulation were differentially expressed in RA B-cells. Moreover, the serum levels of several soluble factors that modulate these pathways, including IL-1beta, IL-5, IL-6, IL-10, IL-12p40, IL-17 and VEGF were significantly increased in this cohort of RA patients. CONCLUSIONS: These results outline aspects of the multifaceted role B-cells play in RA pathogenesis in which immune dysregulation in RA modulates B-cell biology and thereby contributes to the induction and perpetuation of a pathogenic humoral immune response.  (+info)

Network regulation of calcium signal in stomatal development. (5/4873)

AIM: Each cell is the production of multiple signal transduction programs involving the expression of thousands of genes. This study aims to gain insights into the gene regulation mechanisms of stomatal development and will investigate the relationships among some signaling transduction pathways. METHODS: Nail enamel printing was conducted to observe the stomatal indices of wild type and 10 mutants (plant hormone mutants, Pi-starvation induced CaM mutants and Pi-starvation-response mutant) in Arabidopsis, and their stomatal indices were analyzed by ANOVA. We analyzed the stomatal indices of 10 Arabidopsis mutants were analyzed by a model PRGE (potential relative effect of genes) to research relations among these genes. RESULTS: In wild type and 10 mutants, the stomatal index did not differ with respect to location on the lower epidermis. Compared with wild type, the stomatal indices of 10 mutants all decreased significantly. Moreover, significant changes and interactions might exist between some mutant genes. CONCLUSION: It was the stomatal intensity in Arabidopsis might be highly sensitive to most mutations in genome. While the effect of many gene mutations on the stomatal index might be negative, we also could assume the stomatal development was regulated by a signal network in which one signal transduction change might influence the stomatal development more or less, and the architecture might be reticulate. Furthermore, we could speculate that calcium was a hub in stomatal development signal regulation network, and other signal transduction pathways regulated stomatal development by influencing or being influenced by calcium signal transduction pathways.  (+info)

Versatility and connectivity efficiency of bipartite transcription networks. (6/4873)

The modulation of promoter activity by DNA-binding transcription regulators forms a bipartite network between the regulators and genes, in which a smaller number of regulators control a much lager number of genes. To facilitate representation of gene expression data with the simplest possible network structure, we have characterized the ability of bipartite networks to describe data. This has led to the classification of two types of bipartite networks, versatile and nonversatile. Versatile networks can describe any data of the same rank, and are indistinguishable from one another. Nonversatile networks require constraints to be present in data they describe, which may be used to distinguish between different network topologies. By quantifying the ability of bipartite networks to represent data we were able to define connectivity efficiency, which is a measure of how economic the use of connections is within a network with respect to data representation and generation. We postulated that it may be desirable for an organism to maximize its gene expression range per network edge, since development of a regulatory connection may have some evolutionary cost. We found that the transcriptional regulatory networks of both Saccharomyces cerevisiae and Escherichia coli lie close to their respective connectivity efficiency maxima, suggesting that connectivity efficiency may have some evolutionary influence.  (+info)

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators. (7/4873)

Deciphering the molecular basis for human erythropoiesis should yield information benefiting studies of the hemoglobinopathies and other erythroid disorders. We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34+ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation (days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using real-time RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up- and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.  (+info)

Discovering antibiotic efficacy biomarkers: toward mechanism-specific high content compound screening. (8/4873)

As current antibiotic therapy is increasingly challenged by emerging drug-resistant bacteria, new technologies are required to identify and develop novel classes of antibiotics. A major bottleneck in today's discovery efforts, however, is a lack of an efficient and standardized method for assaying the efficacy of a drug candidate. We propose a new high content screening approach for identifying efficacious molecules suitable for development of antibiotics. Key to our approach is a new microarray-based efficacy biomarker discovery strategy. We first produced a large dataset of transcriptional responses of Bacillus subtilis to numerous structurally diverse antibacterial drugs. Second we evaluated different protocols to optimize drug concentration and exposure time selection for profiling compounds of unknown mechanism. Finally we identified a surprisingly low number of gene transcripts (approximately 130) that were sufficient for identifying the mechanism of novel substances with reasonable accuracy (approximately 90%). We show that the statistics-based approach reveals a physiologically meaningful set of biomarkers that can be related to major bacterial defense mechanisms against antibiotics. We provide statistical evidence that a parallel measurement of the expression of the biomarkers guarantees optimal performance when using expression systems for screening libraries of novel substances. The general approach is also applicable to drug discovery for medical indications other than infectious diseases.  (+info)

Gene co-expression network analysis of transcriptome data has enabled the identification of key genes and important networks underlying complex production and disease traits. This study used weighted gene co-expression network analysis (WGCNA) approach to (1) detect modules or clusters of differentially expressed genes (DEG) with similar expression patterns in calf rumen transcriptome during pre- and post-weaning periods and (2) identify regulatory mechanisms linking gene modules to relevant phenotypes during the pre-weaning period (day 33 [d33]): weight gain (BWT_d33), average daily gain (ADG_d33), blood glucose (Glucose_d33) and β-hydroxybutyrate (BHB_d33) concentrations and post-weaning period (d96): weight gain (BWT_d96), average daily gain (ADG_d96), blood glucose (Glucose_d96) and β-hydroxybutyrate (BHB_d96) concentrations, dry matter intake (DMI_d96) and feed efficiency (FE_d96). Rumen tissues were collected from 16 calves on d33 and another 16 on d96 for whole transcriptome sequencing ...
Cervical cancer is the most common gynecologic malignant tumor, with a high incidence in 50-55-year-olds. This study aims to investigate the potential molecular mechanism of RRM2 for promoting the development of cervical cancer based on The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RRM2 was found to be significant upregulated in cervical tissue (P,0.05) by extracting the expression of RRM2 from TCGA, GSE63514, GSE7410, GSE7803 and GSE9750. Survival analysis indicated that the overall survival was significantly worse in the patients with high-expression of RRM2 (P,0.05). The top 1000 positively/negatively correlated genes with RRM2 by Pearson Correlation test were extracted. The gene co-expression network by Weighted Gene Co-Expression Network Analysis (WGCNA) with these genes and the clinical characteristics (lymphocyte infiltration, monocyte infiltration, necrosis, neutrophil infiltration, the number of normal/stromal/tumor cells and the number of tumor nuclei) was ...
Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find
Table_6_Weighted Gene Co-expression Network Analysis Identifies Critical Genes for the Production of Cellulase and Xylanase in Penicillium oxalicum.XLSX
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our
TY - JOUR. T1 - Co-expression network analysis of peripheral blood transcriptome identifies dysregulated protein processing in endoplasmic reticulum and immune response in recurrent MDD in older adults. AU - Ciobanu, Liliana G.. AU - Sachdev, Perminder S.. AU - Trollor, Julian N.. AU - Reppermund, Simone. AU - Thalamuthu, Anbupalam. AU - Mather, Karen A.. AU - Cohen-Woods, Sarah Louise. AU - Stacey, David. AU - Toben, Catherine. AU - Schubert, Klaus Oliver. AU - Baune, Bernhard T.. PY - 2018/12. Y1 - 2018/12. N2 - The molecular factors involved in the pathophysiology of major depressive disorder (MDD) remain poorly understood. One approach to examine the molecular basis of MDD is co-expression network analysis, which facilitates the examination of complex interactions between expression levels of individual genes and how they influence biological pathways affected in MDD. Here, we applied an unsupervised gene-network based approach to a prospective experimental design using microarray ...
Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological
To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in ...
Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or periods during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where
This video was recorded at 6th International Workshop on Machine Learning in Systems Biology (MLSB), Basel 2012. Motivation: Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re- implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment, and equally well performing as existing methods. Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard E. coli and yeast reference datasets showed that this ...
Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues
Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic
Anxiety disorders are the most common psychiatric disorders, and the change in the activity of the prefrontal cortex (PFC) is considered as the underlying pathological mechanism. Parvalbumin-expressing (PV+) inhibition contributes to the overall activity of the PFC. However, the molecular mechanism underlying the excitation-inhibition imbalance of PV+ neurons in the PFC is unknown. Efnb2 is a membrane-bound molecule that plays an important role in the nervous system through binding the Eph receptor. To investigate whether the loss of Efnb2 in PV+ affects anxiety, we examined the behavior of wild type and Efnb2 in PV+ neurons knockout (KO) mice. We monitored the defensive responses to aversive stimuli of elevated plus maze (EPM) and found that KO mice exhibited obvious fearless and anxiolytic behaviors. To further investigate the underlying regulatory mechanism, we performed RNA sequencing, analyzed the differentially expressed genes (DEGs), and constructed the weighted gene co-expression network
Gene regulation is accomplished mainly by the interplay of multiple transcription factors. This gives rise to highly complex and cell-type specific, interwoven structures of regulatory interactions summarized in gene regulatory networks. In this thesis, I address two approaches of computational analysis of such networks, forward modeling and reverse engineering. The first part of this thesis is about the Web application GEne Network GEnerator (GeNGe) which I have developed as a framework for automatic generation of gene regulatory network models. I have developed a novel algorithm for the generation of network structures featuring important biological properties. In order to model the transcriptional kinetics, I have modified an existing non-linear kinetic. This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. GeNGe supports also the generation of various in silico experiments for predicting effects of perturbations as theoretical counterparts of ...
Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using
TY - JOUR. T1 - A genome-wide cis-regulatory element discovery method based on promoter sequences and gene co-expression networks. AU - Gao, Zhen. AU - Zhao, Ruizhe. AU - Ruan, Jianhua. PY - 2013/1/21. Y1 - 2013/1/21. N2 - Background: Deciphering cis-regulatory networks has become an attractive yet challenging task. This paper presents a simple method for cis-regulatory network discovery which aims to avoid some of the common problems of previous approaches. Results: Using promoter sequences and gene expression profiles as input, rather than clustering the genes by the expression data, our method utilizes co-expression neighborhood information for each individual gene, thereby overcoming the disadvantages of current clustering based models which may miss specific information for individual genes. In addition, rather than using a motif database as an input, it implements a simple motif count table for each enumerated k-mer for each gene promoter sequence. Thus, it can be used for species where ...
BACKGROUND: Psoriatic arthritis (PsA) is inflammatory arthritis associated with psoriasis, which involves the axial joint and the distal interphalangeal joints. Its clinical features are varied, often resulting in delayed diagnosis and treatment. Improved knowledge about disease mechanisms will catalyze the rapid development of effective targeted therapies for this disease. The perturbations in the gene co-expression network may not be detected by the differential expression analysis of the microarray. This study aims to identify key modules and hub genes in psoriatic arthritis-applied WGCNA (weighted gene co-expression network analysis) on a microarray. METHODS: This study downloaded the array data of GSE61281 from the gene expression overview (GEO) database, which includes 20 psoriatic arthritis samples and 12 healthy controls. The analysis was performed with the WGCNA package. Gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were ...
GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying
Title: Validation of Inference Procedures for Gene Regulatory Networks. VOLUME: 8 ISSUE: 6. Author(s):Edward R. Dougherty. Affiliation:Department of Electrical and Computer Engineering, Texas A University, College Station, TX 77843-3128, USA.. Keywords:Epistemology, gene network, inference, validation. Abstract: The availability of high-throughput genomic data has motivated the development of numerous algorithms to infer gene regulatory networks. The validity of an inference procedure must be evaluated relative to its ability to infer a model network close to the ground-truth network from which the data have been generated. The input to an inference algorithm is a sample set of data and its output is a network. Since input, output, and algorithm are mathematical structures, the validity of an inference algorithm is a mathematical issue. This paper formulates validation in terms of a semi-metric distance between two networks, or the distance between two structures of the same kind deduced from ...
Gene expression network analysis and applications to immunology - We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.
This function fits iRafNet, a flexible unified integrative algorithm that allows information from prior data, such as protein-protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference. This function takes as input only one set of sampling scores, computed considering one prior data such as protein-protein interactions or gene expression from knock-out experiments. Note that some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).
There are various factors that alter physiological characteristics in skin. Elucidating the underlying mechanism of transcriptional alterations by intrinsic and extrinsic factors may lead us to understand the aging process of skin. To identify the transcriptomic changes of the aging skin, we analyzed publicly available RNA sequencing data from Genotype-Tissue Expression (GTEx) project. GTEx provided RNA sequencing data of suprapubic (n=228) and lower leg (n=349) skins, which are photo-protected and photo-damaged. Using differentially expressed gene analysis and weighted gene co-expression network analysis, we characterized transcriptomic changes due to UV exposure and aging. Genes involved in skin development such as epidermal differentiation complex component (SPRR and LCE families), vasculature development (TGFBR1, TGFBR2, TGFBR3, KDR, FGF2, and VEGFC), and matrix metalloproteinase (MMP2, MMP3, MMP8, MMP10, and MMP13) were up
TY - JOUR. T1 - Inflammatory, regulatory, and autophagy co-expression modules and hub genes underlie the peripheral immune response to human intracerebral hemorrhage. AU - Durocher, Marc. AU - Ander, Bradley P.. AU - Jickling, Glen Clifford. AU - Hamade, Farah. AU - Hull, Heather. AU - Knepp, Bodie. AU - Liu, Da Zhi. AU - Zhan, Xinhua. AU - Tran, Anh. AU - Cheng, Xiyuan. AU - Ng, Kwan. AU - Yee, Alan. AU - Sharp, Frank R.. AU - Stamova, Boryana. PY - 2019/3/5. Y1 - 2019/3/5. N2 - Background: Intracerebral hemorrhage (ICH) has a high morbidity and mortality. The peripheral immune system and cross-talk between peripheral blood and brain have been implicated in the ICH immune response. Thus, we delineated the gene networks associated with human ICH in the peripheral blood transcriptome. We also compared the differentially expressed genes in blood following ICH to a prior human study of perihematomal brain tissue. Methods: We performed peripheral blood whole-transcriptome analysis of ICH and matched ...
Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF. Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database. We performed weighted correlation network and differential
This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then
Enriched gene interaction network detected with opposite expression patterns in PZQ-treated paired or unpaired mature females.This gene interaction network is s
In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the
Animals consist of a wide variety of cells that serve different functions depending on their location in the body. Cells with similar functions, or cell types, in different animal species are related both by an evolutionary line of descentÐsimilar to the relatedness of species themselvesÐand by a developmental line of descent in the embryo. Networks of interacting genes, or gene regulatory networks, control gene expression in the cell, thereby specifying cell type identity. Understanding how new cell types arise by changing gene regulatory networks is critical both to comprehending fundamental aspects of human biology and to manipulating cell types in the laboratory. We approached this question by studying endometrial stromal fibroblast (ESF) cells from the uterus of humans and opossums, two distantly related mammals. We showed that the distantly related cell type in opossum expresses a similar set of regulatory genes as the human cell, but in response to pregnancy-related signals, the opossum ...
We data-analyzed and constructed the high-expression CAMK1 phosphoinositide signal-mediated protein sorting and transport network in human hepatocellular carcinoma (HCC) compared with low-expression (fold change ≥ 2) no-tumor hepatitis/cirrhotic tissues (HBV or HCV infection) in GEO data set, using integration of gene regulatory network inference method with gene ontology (GO). Our result showed that CAMK1 transport subnetwork upstream KCNQ3, LCN2, NKX2_5, NUP62, SORT1, STX1A activated CAMK1, and downstream CAMK1-activated AFP, ENAH, KPNA2, SLC4A3; CAMK1 signal subnetwork upstream BRCA1, DKK1, GPSM2, LEF1, NR5A1, NUP62, SORT1, SSTR5, TBL3 activated CAMK1, and downstream CAMK1-activated MAP2K6, SFRP4, SSTR5, TSHB, UBE2C in HCC. We proposed that CAMK1 activated network enhanced endosome to lysosome transport, endosome transport via multivesicular body sorting pathway, Golgi to endosome transport, intracellular protein transmembrane transport, intracellular protein transport, ion transport, mRNA ...
Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae (Balcan et al., 2007). Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in
A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included
A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or
Author Summary A regulatory protein can activate the expression of a target gene either directly, i.e., by binding to the genes promoter, or indirectly, i.e., by altering the expression of regulators, which, in turn, bind to the target genes promoter and induce or inhibit its transcription. Indirect regulatory circuits can contain multiple components and functional elements, such as feedforward and feedback loops. The complex structure of indirect regulation raises the question of its evolutionary origins. Here, we study the dynamic and evolutionary properties of regulatory architectures that involve members of the recently emerged class of bacterial proteins termed connectors. Such proteins post-translationally modulate the activity of two-component systems and phosphorelays, which constitute the prevalent form of bacterial signal transduction. We describe a novel connector-mediated regulatory circuit that combines the structural and functional properties of direct and indirect regulation. Our
Root nodule symbioses (nodulation) and whole genome duplication (WGD, polyploidy) are both important phenomena in the legume family (Leguminosae). Recently, it has been proposed that polyploidy may have played a critical role in the origin or refinement of nodulation. However, while nodulation and polyploidy have been studied independently, there have been no direct studies of mechanisms affecting the interactions between these phenomena in symbiotic, nodule-forming species. Here, we examined the transcriptome-level responses to inoculation in the young allopolyploid Glycine dolichocarpa (T2) and its diploid progenitor species to identify underlying processes leading to the enhanced nodulation responses previously identified in T2. We assessed the differential expression of genes and, using weighted gene co-expression network analysis (WGCNA), identified modules associated with nodulation and compared their expression between species. These transcriptomic analyses revealed patterns of non-additive
This chapter presents a survey of recent methods for reconstruction of time-varying biological networks such as gene interaction networks based on time series node observations (e.g. gene expressions) from a modeling perspective. Time series gene expression data has been extensively used for analysis of gene interaction networks, and studying the influence of regulatory relationships on different phenotypes. Traditional correlation and regression based methods have focussed on identifying a single interaction network based on time series data. However, interaction networks vary over time and in response to environmental and genetic stress during the course of the experiment. Identifying such time-varying networks promises new insight into transient interactions and their role in the biological process. A key challenge in inferring such networks is the problem of high-dimensional data i.e. the number of unknowns p is much larger than the number of observations n. We discuss the computational aspects of
It is crucial to grasp the characteristics of tumour immune microenvironment to improve effects of immunotherapy. In this study, the immune and stromal scores of 371 cases were calculated for quantitative analysis of immune and stromal cell infiltration in the tumour microenvironment of hepatocellular carcinoma (HCC). The weighted gene co-expression network analysis and protein–protein interaction network were analysed to identify immune microenvironment-related genes. The results showed that patients with high immune scores had a higher 4-year recurrence-free rate. TP53, CTNNB1, and AXIN1 mutations significantly varied with immune scores. In immune score-related modules analysis, Kyoto encyclopaedia of genes and genomes pathways and gene ontology terms were closely related to immune processes, tumorigenesis, and metastasis. Twelve new immune microenvironment-related genes were identified and had significantly positive correlations with seven immune checkpoint genes. In prognostic analysis,
Gene co-epxression network analyses are common in the study of large scale biological data sets. In this study, we have developed a methodology for the comparison of pairs of co-expression networks using the s-core network peeling approach. We apply the methodology to gene-expression data for human and mouse. ...
Endosperm is an absorptive structure that supports embryo development or seedling germination in angiosperms. The endosperm of cereals is a main source of food, feed, and industrial raw materials worldwide. However, the genetic networks that regulate endosperm cell differentiation remain largely unclear. As a first step toward characterizing these networks, we profiled the mRNAs in five major cell types of the differentiating endosperm and in the embryo and four maternal compartments of the maize (Zea mays) kernel. Comparisons of these mRNA populations revealed the diverged gene expression programs between filial and maternal compartments and an unexpected close correlation between embryo and the aleurone layer of endosperm. Gene coexpression network analysis identified coexpression modules associated with single or multiple kernel compartments including modules for the endosperm cell types, some of which showed enrichment of previously identified temporally activated and/or imprinted genes. ...
Mason MJ, Fan G, Plath K, Zhou Q, Horvath S (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells BMC Genomics 2009, 10:327. ...
Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. ...
It is now well established that the study of biological complexity has shifted from gene level to interaction networks and this shift from components to associated interactions has gained increasing interest in network biology. Gene Regulatory Networks (GRNs) depict the functioning circuitry in organisms at the gene level and represent an abstract mapping of the more complicated biochemical network which includes other components such as proteins, metabolites, etc. Understanding GRNs can provide new ideas for treating complex diseases and offer novel candidate drug targets. A commonly accepted top-down approach is to reverse engineer GRNs from experimental data generated by microarray technology [1-5].. Early computational approaches for inferring GRNs from gene expression data employed classical methods. Boolean network modeling considers the gene expression to be in a binary state (either switched on or off), and display via a Boolean function the impact of other genes on a specific target ...
Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best
Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, … with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species ...
In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Although the individual networks represent different levels of cellular interactions with global structural and
Eric H. Davidson, PhD, has been awarded the International Prize for Biology from the Japan Society for the Promotion of Science for his research related to understanding gene regulatory networks, particularly as it pertains to embryonic development. Dr. Davidson is Norman Chandler Professor of Cell Biology in the Division of Biology at the California Institute of Technology in Pasadena.. Instead of focusing on individual genes, Dr. Davidson has focused his studies on a broad approach to understanding how groups of genes are regulated and interact to establish and maintain the developmental program that underlies developmental processes. This approach has proven critical to understanding how a single cell can ultimately give rise to multiple specialized cells and tissues with an array of diverse functions.. As their model system, Dr. Davidson and his colleagues have used the sea urchin to define and study these developmental gene regulatory networks. The production of hundreds of thousands of ...
TY - JOUR. T1 - Functional clustering of time series gene expression data by Granger causality. AU - Fujita, André. AU - Severino, Patricia. AU - Kojima, Kaname. AU - Sato, João R.. AU - Patriota, Alexandre G.. AU - Miyano, Satoru. PY - 2012/10/30. Y1 - 2012/10/30. N2 - Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its ...
TY - CHAP. T1 - Determining the properties of gene regulatory networks from expression data. AU - Liebovitch, Larry S.. AU - Shehadeh, Lina A.. AU - Jirsa, Viktor K.. AU - Hütt, Marc Thorsten. AU - Marr, Carsten. N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.. PY - 2009. Y1 - 2009. N2 - The expression of genes depends on the physical structure of DNA, how the function of DNA is regulated by the transcription factors expressed by other genes, RNA regulation, such as that through RNA interference, and protein signals mediated by protein-protein interaction networks. We illustrate different approaches to determining information about the network of gene regulation from experimental data. First, we show that we can use statistical information of the mRNA expression values to determine the global topological properties of the gene regulatory network. Second, we show that analyzing the changes in expression due to mutations or different environmental conditions can give us ...
A human disease network is a network of human disorders and diseases with reference to their genetic origins or other features. More specifically, it is the map of human disease associations referring mostly to disease genes. For example, in a human disease network, two diseases are linked if they share at least one associated gene. A typical human disease network usually derives from bipartite networks which consist of both diseases and genes information. Additionally, some human disease networks use other features such as symptoms and proteins to associate diseases. In 2007, Goh et al. constructed a disease-gene bipartite graph using information from OMIM database and termed human disease network. In 2009, Barrenas et al. derived complex disease-gene network using GWAs (Genome Wide Association studies). In the same year, Hidalgo et al. published a novel way of building human phenotypic disease networks in which diseases were connected according to their calculated distance. In 2011, Cusick et ...
Downloadable! In order to overcome the problem of free-riding in current P2P system, we suggest applying social network theory. Based on our exploration of the overlapping research fields of social networks and peer-to-peer networks, we propose a new P2P framework within this paper. It specifies social network information that can be used in a P2P system to avoid performance inefficiencies caused by free-riding or by policies to overcome free-riding. To identify this specific social network information, we conduct a survey among a small group of students, who use Skype, a popular P2P system. We use descriptive analysis and multiple regression analysis to analyze the survey data. The results of the analyses provide an indication that the idea of using social network information in P2P systems is valid and that it is supported by P2P users. Based on our findings, we make recommendations for a successful implementation of social-network-information-based P2P systems that can overcome free-riding issues and
Background: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. Methods: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. ...
Brachypodium distachyon is a close relative of many important cereal crops. Abiotic stress tolerance has a significant impact on productivity of agriculturally important food and feedstock crops. Analysis of the transcriptome of Brachypodium after chilling, high-salinity, drought, and heat stresses revealed diverse differential expression of many transcripts. Weighted Gene Co-Expression Network Analysis revealed 22 distinct gene modules with specific profiles of expression under each stress. Promoter analysis implicated short DNA sequences directly upstream of module members in the regulation of 21 of 22 modules. Functional analysis of module members revealed enrichment in functional terms for 10 of 22 network modules. Analysis of condition-specific correlations between differentially expressed gene pairs revealed extensive plasticity in the expression relationships of gene pairs. Photosynthesis, cell cycle, and cell wall expression modules were down-regulated by all abiotic stresses. Modules ...
The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution. We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the
Title:Novel Study of Model-based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process. VOLUME: 12 Author(s):Farzane Ahmadi, Ali-Reza Abadi, Zahra Bazi and Abolfazl Movafagh*. Affiliation:Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Department of Community Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Department of Medical Genetics, School of Medicine, Cancer Research Center, Shohada Referral hospital, Shahid Beheshti University of Medical Sciences, Tehran. Keywords:Clustering; Mixtures of Matrix-Variate Normal Distributions; Aging; Time Series. Abstract:Background: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. ...
Autism spectrum disorder (ASD) is a common, highly heritable neuro-developmental condition characterized by marked genetic heterogeneity 1-3 . Thus, a fundamental question is whether autism represents an etiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain 4 . Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1/FOX1, and a module enriched for immune genes and glial markers. Using high-throughput RNA-sequencing we
In late-onset Alzheimers disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early ...
In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs) are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at gives full access to all transcriptome data, to the reconstructed network and to the ...
The combination of genomics, genetics, and systems-level computational methods provides a powerful approach toward insight into complex biological systems. Of particular significance is the discovery of genetic interactions that lead to desirable agricultural and economic traits in the Poaceae family (grasses). The Poaceae includes valuable crops such as rice (Oryza sativa), maize (Zea mays), wheat (Triticum spp.), and sugarcane (Saccharum officinarum), which are globally some of the most agriculturally and economically important crops (FAOSTAT, 2007). Understanding complex interactions underlying agronomic traits within these species, therefore, is of great significance, in particular to help with crop improvements to meet the challenges of plant and human health but also for basic understanding of complex biological systems.. In addition to their pivotal role in agriculture, grasses offer a powerful model system in that their genomes are closely conserved and functional genomic knowledge ...
1. Mirabello L, Troisi RJ, Savage SA. Osteosarcoma incidence and survival rates from 1973 to 2004: data from the Surveillance, Epidemiology, and End Results Program. Cancer-Am Cancer Soc. 2009;115:1531-1543 2. Bacci G, Longhi A, Versari M. et al. Prognostic factors for osteosarcoma of the extremity treated with neoadjuvant chemotherapy - 15-year experience in 789 patients treated at a single institution. Cancer-Am Cancer Soc. 2006;106:1154-1161 3. Ritter J, Bielack SS. Osteosarcoma. Ann Oncol. 2010;217:320-325 4. Lewis IJ, Nooij MA, Whelan J. et al. Improvement in histologic response but not survival in osteosarcoma patients treated with intensified chemotherapy: A randomized phase III trial of the European Osteosarcoma Intergroup. Journal of The National Cancer Institute. 2007;99:112-128 5. Ta HT, Dass CR, Choong PFM, Dunstan DE. Osteosarcoma treatment: state of the art. Cancer Metast Rev. 2009;28:247-263 6. Daw NC, Chou AJ, Jaffe N. et al. Recurrent osteosarcoma with a single pulmonary ...
Postdocatoral Research Fellow National University of Singapore NUS Graduate School for Integrative Sciences and Engineering and Department of Mathematics Singapore Description We are looking for three highly motivated postdoctoral researchers in computational analysis of gene regulatory networks (network robustness and topological properties) and its applications in cancer biology. Our research objective is to develop graph-based algorithms to study important biological questions in stem cell and cancer biology ...
Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect
TY - JOUR. T1 - Comparison and evaluation of network clustering algorithms applied to genetic interaction networks. AU - Hou, Lin. AU - Wang, Lin. AU - Berg, Arthur. AU - Qian, Minping. AU - Zhu, Yunping. AU - Li, Fangting. AU - Deng, Minghua. PY - 2012/1/1. Y1 - 2012/1/1. N2 - The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian ...
Pathway Tools is a comprehensive symbolic systems biology software system that supports several use cases in bioinformatics and systems biology: *Development of organism-specific databases called Pathway/Genome Databases (PGDBs) that integrate many bioinformatics datatypes, from genomes to pathways to regulatory networks. *Development of metabolic-flux models using flux-balance analysis *Scientific visualization, web publishing, and dissemination of those organism-specific databases, including: **Automatic display of metabolic pathways and full metabolic networks; generation of metabolic map diagram and of metabolic map poster ([ example]). **Genome browser; comparative genome browser; generation of genome poster ([ example]). **Display of operons, regulons, and full transcriptional regulatory networks *Analysis of omics datasets, including painting omics data on to diagrams of the ...
Maize leaves have distinct tissues that serve specific purposes. The blade tilts back to photosynthesize and the sheath wraps around the stem to provide structural support and protect young leaves. At the junction between blade and sheath are the ligule and auricles, both of which are absent in the recessive liguleless1 (lg1) mutant. Using an antibody against LG1, we reveal LG1 accumulation at the site of ligule formation and in the axil of developing tassel branches. The dominant mutant Wavy auricle in blade1 (Wab1-R) produces ectopic auricle tissue in the blade and increases the domain of LG1 accumulation. We determined that wab1 encodes a TCP transcription factor by positional cloning and revertant analysis. Tassel branches are few and upright in the wab1 revertant tassel and have an increased branch angle in the dominant mutant. wab1 mRNA is expressed at the base of branches in the inflorescence and is necessary for LG1 expression. wab1 is not expressed in leaves, except in the dominant ...
Grasses are characterized by a distinct leaf morphology that includes an enclosing sheath, a photosynthetic blade, two auricles and a ligule fringe. The first sign of a preligule band is a zone of division that occurs in a P6-P7 leaf primordium. These divisions are parallel and perpendicular to the long axis of the leaf. At about P7-P8, divisions occur that are periclinal to the surface, causing growth out of the plane of the leaf (Sharman, 1941; Sylvester et al., 1990). The periclinal divisions initiate the actual ligule and do not occur in the lg1-R leaf, leading to a failure of ligule and auricle formation (Sylvester et al., 1990). Previous work, in which RT-PCR (Foster et al., 2004; Moreno et al., 1997) and whole-mount in situ hybridization (Moon et al., 2013) was used, has shown that lg1 is expressed at the ligule. We used an antibody to follow the timing of LG1 accumulation at a cellular resolution. LG1 protein is visible in a band of ∼20 cells coincident with the first divisions. The ...
PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.
In this article, we proposed the methodology of analyzing the distribution of gene functional properties in the context of statistical epistasis networks. The gene interaction network was constructed by first identifying the network of strong and significant pairwise SNP epistatic interactions and then building gene network on top of the SNP interaction network. After annotating genes as vertices based on their functional Gene Ontology, dyadicity and heterophilicity analysis was performed for each GO term to investigate to what degree the vertex characteristics correlate with the underlying interaction network topology. Using a population-based bladder cancer dataset and its previously identified SNP statistical epistasis network, we performed the dyadicity and heterophilicity analysis on enriched GO terms for the genes in the gene interaction network associated with bladder cancer. We were able to find 12 GO categories with significant dyadicity or heterophilicity, which indicated the ...
Bibliographic details on RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions.
Dr. Ganesh Sriram is Assistant Professor of Chemical and Biomolecular Engineering at University of Maryland. After earning his bachelors and masters degrees in chemical engineering from Indian Institute of Technology (IIT) Bombay, he graduated a Ph.D. in chemical engineering from Iowa State University in 2004, working in the lab of Dr. Jacqueline Shanks. From 2004 to 2007, he was a postdoctoral researcher in the labs of Dr. Katrina Dipple and Dr. James Liao at UCLA. Dr. Srirams research interests are metabolic engineering and systems biology, especially metabolic flux analysis and gene regulatory network analysis. See lab research page for more information. ...
Dr. Ganesh Sriram is Assistant Professor of Chemical and Biomolecular Engineering at University of Maryland. After earning his bachelors and masters degrees in chemical engineering from Indian Institute of Technology (IIT) Bombay, he graduated a Ph.D. in chemical engineering from Iowa State University in 2004, working in the lab of Dr. Jacqueline Shanks. From 2004 to 2007, he was a postdoctoral researcher in the labs of Dr. Katrina Dipple and Dr. James Liao at UCLA. Dr. Srirams research interests are metabolic engineering and systems biology, especially metabolic flux analysis and gene regulatory network analysis. See lab research page for more information. ...
No, not this Edison. Im talking about my software package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression)*.. What it does. Network inference. More precisely, network inference from time series data.. Imagine you have a dataset to analyse where a number of variables have been measured at regular intervals. You might ask, what are the relationships between these variables. In other words, if you were to link every two variables that depend on each other, what would the network you obtain look like. EDISON uses the observed data to infer the underlying network.. Now there are plenty of packages for network inference. What makes EDISON stand out is that it can also answer a second question: do the relationships between the variables change over time. The software package can identify changepoints in the time series of measurements where the structure of the underlying network changes.. Under the Hood. The underlying model for EDISON is a dynamic ...
Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA)
Erich Grotewold, a professor of molecular genetics and horticulture and crop science at The Ohio State University, is leveraging the resources of the Ohio Supercomputer Center as part of his studies to address fundamentally important questions in plant research.. He and project co-principle investigators Andrea Doseff, an Ohio State Medical Center and Department of Molecular Genetics associate professor, and John Gray, an associate professor at the University of Toledo, are the recipients of a $4.23 million grant from the National Science Foundation Plant Genome Program to study Systems Approaches to Identify Gene Regulatory Networks in the Grasses.. Establishing gene regulatory networks and linking system components to agronomic traits is an important emerging theme in plant systems biology, Grotewold said. This is the first concerted effort to comprehensively dissect the gene regulatory networks that target the metabolism of phenolic compounds, found in maize, other cereal crops and ...
Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis. This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed
TY - JOUR. T1 - Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells. AU - Linde, Jörg. AU - Wilson, Duncan. AU - Hube, Bernhard. AU - Guthke, Reinhard. PY - 2010/11/4. Y1 - 2010/11/4. N2 - Reverse engineering of gene regulatory networks can be used to predict regulatory interactions of an organism faced with environmental changes, but can prove problematic, especially when focusing on complicated multi-factorial processes. Candida albicans is a major human fungal pathogen. During the infection process, this fungus is able to adapt to conditions of very low iron availability. Such adaptation is an important virulence attribute of virtually all pathogenic microbes. Understanding the regulation of iron acquisition genes will extend our knowledge of the complex regulatory changes during the infection process and might identify new potential drug targets. Thus, there is a need for efficient modelling approaches predicting key regulatory events of ...
Reductionist philosophy has directed biological research for decades [1, 2]. A significant amount of information has been generated so far in the field of biological sciences as enrichment of human knowledgebase to understand life [1]. Despite enormous success of reductionism to decode the structural and functional attributes at cellular and molecular levels of life-organization, it is progressively becoming clearer that biological functions can rarely be credited to discrete perception of individual molecules. Alternatively, most biological phenomena emerge due to extremely interactive complexity derived from functional integrity of cells numerous constituents [2]. Various recent approaches have been initiated and accomplished to study biological systems in more integrative and comprehensive way. Network model can play an important role to understand the complex network system based on multiple sets of interactions and to make plain and clear analysis of the origin of observed network ...
Current network analysis packages tend to be standalone desktop tools which rely on local resources and whose operations are not easily integrated with other software and databases. A key contribution of this thesis is an extensible toolkit of biological network construction and analysis operations, developed as web services. Web services are a distributed technology that enable machine-to-machine interaction over a network, and promote interoperability by allowing tools deployed on heterogeneous systems to interface. A conceptual framework has been created, which is realised practically through the proposal of a common graph format to standardise network data, and the investigation of open-source deployment technologies. Workflows are a graph of web services, allowing analyses to be carried out as part of a bigger software pipeline. They may be constructed using web services within the toolkit together with those from other providers, and can be saved, shared and reused, allowing biologists to ...
One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be
TY - JOUR. T1 - Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments. AU - Cai, Hong. AU - Lilburn, Timothy G.. AU - Hong, Changjin. AU - Gu, Jianying. AU - Kuang, Rui. AU - Wang, Yufeng. PY - 2015/6/11. Y1 - 2015/6/11. N2 - Background: Malaria is a major health threat, affecting over 40% of the worlds population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis. Results: Our module-based subnetwork alignment approach identified 24 ...
If you have a question about this talk, please contact Xin Wang.. Differences in the gene regulatory network are hypothesized to contribute significantly to phenotypic divergence between and within species. Non-coding sequences with bursts of lineage-specific changes are promising candidates, because clusters of nearby substitutions are a hallmark of selection potentially modify evolutionarily conserved regulatory elements. Performing a comprehensive, genome-wide analysis, we find that genomic loci with high substitution rates in the human-chimp lineage are over-represented near genes that duplicated in the human-chimp ancestor. We also developed a method to screen for nucleotide substitutions predicted to affect transcription factor binding. Rates of binding site divergence are elevated in non-coding sequences near duplicated loci with accelerated substitution rates. Finally, GC-biased gene conversion (gBGC) is a non-adaptive, recombination-associated explanation for accelerated substitution ...
While accurate annotations of protein-coding regions in the human genome have been available for many years, annotation and interpretation of regulatory sequenc...
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Microarray technology allows the simultaneous measurement of RNA levels in a cell culture or tissue sample. This has resulted in the generation of vast amounts of data, and the methods to analyze this onslaught are only just now catching up. In this thesis, we present three novel methods for the algorithmic and statistical analysis of microarray data.; First, in joint work with Michael R. Mehan, we systematically annotated over 300 microarray datasets with phenotype information, then used these annotations to find gene coexpression modules that are specific to particular phenotypes. Though previous methods existed to find recurrent coexpression modules, ours is the first to find phenotype-specific ones, avoiding the problem past methods faced of finding mostly general, cell-cycle related modules. Second, we studied the statistics of gene networks. Although the algorithms to study gene coexpression networks have become increasingly sophisticated, the statistics have failed to keep up. As a ...
Author(s): Bonham, Luke W; Steele, Natasha ZR; Karch, Celeste M; Manzoni, Claudia; Geier, Ethan G; Wen, Natalie; Ofori-Kuragu, Aaron; Momeni, Parastoo; Hardy, John; Miller, Zachary A; Hess, Christopher P; Lewis, Patrick; Miller, Bruce L; Seeley, William W; Baranzini, Sergio E; Desikan, Rahul S; Ferrari, Raffaele; Yokoyama, Jennifer S; International FTD-Genomics Consortium (IFGC) | Abstract: Objective:The neuroanatomical profile of behavioral variant frontotemporal dementia (bvFTD) suggests a common biological etiology of disease despite disparate pathologic causes; we investigated the genetic underpinnings of this selective regional vulnerability to identify new risk factors for bvFTD. Methods:We used recently developed analytical techniques designed to address the limitations of genome-wide association studies to generate a protein interaction network of 63 bvFTD risk genes. We characterized this network using gene expression data from healthy and diseased human brain tissue, evaluating regional
Pan-Cancer pattern of lncRNAs, miRNAs and their regulatory networks - posted in siRNA, microRNA and RNAi: Dysregulation of miRNAs and lncRNAs can contribute to tumor formation and progression. Little is known about the expression profiles of lncRNAs and miRNAs across diverse cancer types. Pan-Cancer miRNA and lncRNA resources: (1) panCancerMine: explore Pan-Cancer lncRNA, miRNA and their regulatory networks (ceRNA, coexpression) by mining expression profiles of miRNAs, lncRNAs...
Systems biology approaches can be used to study the regulatory interactions occurring between many components of the biological system at the whole-genome level and decipher the circuitries implicated in the regulation of cellular processes, including those imparting virulence to opportunistic fungi. Candida albicans (C. albicans) is a leading human fungal pathogen. It undergoes morphological switching between a budding yeast form and an elongated multicellular hyphal form. This transition is required for C. albicans ability to cause disease and is regulated through highly interconnected regulatory interactions between transcription factors (TFs) and target genes. The chromatin immunoprecipitation (ChIP)-High-throughput sequencing (Seq) technology (ChIP-Seq) is a powerful approach for decoding transcriptional regulatory networks. This protocol was optimized for the preparation of ChIP DNA from filamenting C. albicans cells followed by high-throughput sequencing to identify the targets of TFs that
March 2, 2010 - The underlying causes of the debilitating psychiatric disorder schizophrenia remain poorly understood. In a study published online in Genome Research (, scientists have performed a powerful gene network analysis that has revealed surprising new insights into how gene regulation and age play a role in schizophrenia.. Researchers are actively working to identify the direct cause of schizophrenia, likely rooted in interactions between genes and the environment resulting in abnormal gene expression in the central nervous system. Scientists have been studying expression changes in schizophrenia on an individual gene basis, yet this strategy has explained only a portion of the genetic risk.. In this work, a team of researchers led by Elizabeth Thomas of the Scripps Research Institute has taken a novel approach to this problem, performing a gene network-based analysis that revealed surprising insight into schizophrenia development.. The group analyzed gene expression data ...
Ultraviolet light damages the DNA of cells, which prevents duplication and thereby cell division. Bacteria respond to such damage by producing a number of proteins that help to detect, bypass, and repair the damage. This SOS response system displays intricate dynamical behavior-in particular the tightly regulated turn-on and turn-off of error-prone DNA polymerases that result in mutagenesis-and the puzzling resurgence of SOS gene activity 30-40 min after irradiation. The SOS response in the bacterium E. coli encompasses many proteins involved in detecting and repairing DNA damaged by a variety of agents, such as UV radiation, or chemicals such as mitomycin and bleomycin. A complex regulatory network, comprising both transcriptional and post-translational regulators, controls the concentrations and levels of activity of these proteins. The collective actions of this regulatory network are orchestrated so that the SOS response is commensurate with the magnitude of DNA damage. The prokaryotic SOS ...
The gene-disease networks used in this study and part of the analysis are available at
... gene regulatory networks (e.g. Guo et al., 2009), degeneracy (Whitacre et al., 2010), grammatical evolution (de Salabert et al. ... "A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network." ... "A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots", Intelligent Autonomous Systems 8 ...
... and to infer gene regulatory networks from multiple microarray datasets[28] as well as transcriptional regulatory networks from ... Wang, Yong; Joshi, Trupti; Zhang, Xiang-Sun; Xu, Dong; Chen, Luonan (2006-07-24). "Inferring gene regulatory networks from ... Wang, Rui-Sheng; Wang, Yong; Zhang, Xiang-Sun; Chen, Luonan (2007-09-22). "Inferring transcriptional regulatory networks from ... A Neural Network trains via a technique called Back-propagation, in which propagating forward calculating the dot product of ...
The Regulatory Genome: Gene Regulatory Networks In Development And Evolution (2006) ISBN 0-12-088563-8 Hinman, Veronica (2016 ... Hood, L. (2008). "Gene regulatory networks and embryonic specification". Proc. Natl. Acad. Sci. U.S.A. 105 (16): 5955-62. doi: ... Research web page Patent: Gene regulatory networks and methods of interdiction for controlling the differentiation state of a ... which would lead to a long line of investigation that eventually led to his contemporary interest in gene regulatory networks. ...
Thattai, M; A. van Oudenaarden (2001). "Intrinsic noise in gene regulatory networks". PNAS USA. 98 (15): 8614-9. Bibcode: ... and then focused on stochasticity in gene networks biological networks as control systems, and the evolution of small networks ... During his time at MIT his lab started with parallel lines of research in actin dynamics and noise in gene networks, ... Pedraza, J M; A. van Oudenaarden (2005). "Noise propagation in gene networks". Science. 307 (5717): 1965-9. Bibcode:2005Sci... ...
2007). "Innovation and robustness in complex regulatory gene networks". Proceedings of the National Academy of Sciences, USA. ... 2012). "Mutational Robustness of Gene Regulatory Networks". PLOS ONE. 7 (1): e30591. Bibcode:2012PLoSO...730591V. doi:10.1371/ ... or gene networks. Experimental systems for individual genes include enzyme activity of cytochrome P450, B-lactamase, RNA ... For instance, gene expression is intrinsically noisy. This means that two cells in exactly identical regulatory states will ...
Crombach, Anton; Hogeweg, Paulien (11 July 2008). "Evolution of Evolvability in Gene Regulatory Networks". PLOS Computational ... Draghi, J.; Wagner, G. P. (March 2009). "The evolutionary dynamics of evolvability in a gene network model". Journal of ... These regulatory linkages can be made and changed easily, a phenomenon that Kirschner and Gerhart call "weak regulatory linkage ... expressing different Hox genes). Other forms of regulatory compartmentation include different cell types, developmental stages ...
Special Section: Gene Regulatory Networks for Development. 340 (2): 438-49. doi:10.1016/j.ydbio.2010.01.031. PMID 20123092. ... In studies of homozygous mice, it has been found that deletion of the MSX1 gene has resulted in a double cleft palate, ... In addition, encoded proteins by PAX9 genes have a 128 amino acid long DNA binding paired domain. Studies suggest that all ... Furthermore, mutations of other genes have been identified in syndromes and congenital abnormalities in which tooth agenesis is ...
"An overview of the gene regulatory network controlling trichome development in the model plant, Arabidopsis". Frontiers in ... "Homology-Directed Repair of a Defective Glabrous Gene in Arabidopsis With Cas9-Based Gene Targeting". Frontiers in Plant ... Activation of genes that encode specific protein transcription factors (named GLABRA1 (GL1), GLABRA3 (GL3) and TRANSPARENT ... Knockouts of the corresponding gene lead to glabrous plants. This phenotype has already been used in genome editing experiments ...
Erwin, Douglas H.; Davidson, Eric H. (2009). "The evolution of hierarchical gene regulatory networks" (PDF). Nature Reviews ... of gene expression generally flows from few high-level control genes through multiple intermediate genes to peripheral gene ... For instance, genes within GRNs with "optimally pleiotropic" effects, that is, genes that have the most widespread effect on ... This type of architecture implies that high-level control genes tend to be more pleiotropic affecting multiple downstream genes ...
"Gene regulatory network inference resources: A practical overview". Biochimica et Biophysica Acta (BBA) - Gene Regulatory ... thus extending the concept of gene networks to include transcriptional regulatory complexes. Often, gene network reliability is ... "Gene network reverse engineering: The Next Generation". Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1863 ... There are several methods for reverse engineering gene regulatory networks by using molecular biology and data science methods ...
This gene regulatory network can be subdivided into the following four sub-networks described below. First, extracellular ... Rogers, J. M. (2016). "Search for the missing lncs: gene regulatory networks in neural crest development and long non-coding ... Underlying the development of neural crest is a gene regulatory network, described as a set of interacting signals, ... Sakuka-Spengler, Tatjana (2008). "A gene regulatory network orchestrates neural crest formation". Nature Reviews Molecular Cell ...
At MIT, Bar-Joseph's group developed a novel algorithm to discover regulatory networks of gene modules in yeast. These modules ... "Computational discovery of gene modules and regulatory networks". Nature Biotechnology. 21 (11): 1337-42. doi:10.1038/nbt890. ... Michal Linial Bar-Joseph, Ziv (2003). Inferring interactions, expression programs and regulatory networks from high throughput ... Lee, T. I. (2002). "Transcriptional Regulatory Networks in Saccharomyces cerevisiae". Science. 298 (5594): 799-804. Bibcode: ...
Gene regulatory network Niehrs, C. and Pollet, Nicolas. Synexpression groups in eukaryotes. Nature 1999 December 2; 402: 483- ... Thus, changes in the regulatory patterns of these genes would affect the development of both the fore- and hind-limbs, ... Synexpression is a type of non-random eukaryotic gene organization. Genes in a synexpression group may not be physically linked ... Synexpression groups in particular represent genes that are simultaneously up- or down-regulated, often because their gene ...
Interested in gene regulatory networks and developmental biology, Arda joined the laboratory of Seung K. Kim [Wikidata] at ... Arda's laboratory aims to delineate the gene regulatory networks that control the development, expansion and function of human ... During her doctorate training in the laboratory of Marian Walhout [Wikidata], she studied gene regulatory networks that pertain ... Arda, H. (2010-07-30). C. Elegans Metabolic Gene Regulatory Networks: A Dissertation. GSBS Dissertations and Theses (Thesis). ...
Infante, CR; Rasys, AM; Menke, DB (January 2018). "Appendages and gene regulatory networks: Lessons from the limbless". Genesis ... Zhu J, Zhang YT, Alber MS, Newman SA (2010). "Bare bones pattern formation: a core regulatory network in varying geometries ... Hox genes contribute to the specification of the stylopod, zeugopod and autopod. Mutations in Hox genes lead to proximodistal ... The limb field is a region specified by expression of certain Hox genes, a subset of homeotic genes, and T-box transcription ...
Zhou Q, Chipperfield H, Melton DA, Wong WH (Oct 2007). "A gene regulatory network in mouse embryonic stem cells". Proceedings ... gene expression. Sall4 is part of the transcriptional regulatory network that includes other pluripotent factors such as Oct4, ... The SALL genes were identified based on their sequence homology to Spalt, which is a homeotic gene originally cloned in ... Gene. 584 (2): 111-119. doi:10.1016/j.gene.2016.02.019. PMC 4823161. PMID 26892498. Kühnlein RP, Frommer G, Friedrich M, ...
Buckingham, M; Rigby, P (February 2014). "Gene Regulatory Networks and Transcriptional Mechanisms that Control Myogenesis". ... "Entrez Gene: MYOD1 myogenic differentiation 1". Rudnicki MA, Schnegelsberg PN, Stead RH, Braun T, Arnold HH, Jaenisch R (Dec ... Wnt4, Wnt5, and Wnt6 function to increase the expression of both of the regulatory factors but at a more subtle level. ... Setdb1 appears to be necessary to maintain both MyoD expression and also genes that are specific to muscle tissues because ...
"Gene regulatory networks and epigenetic modifications in cell differentiation". International Union of Biochemistry and ... Later focusing his attention on bacteriophage lambda, he studied the gene expression of the bacterial virus using its operator- ... role of differential contact in the transcription regulation mechanism and demonstrated the theory in many genetic regulatory ...
T-cell gene regulatory network from the Rothenberg Lab. Zebrafish developmental gene regulatory network from the Yuh Lab. Limb ... Input Gene Regulatory Networks can be drawn by hand. Networks can be built using lists of interactions entered via dialog boxes ... BioTapestry is an interactive tool for modeling and visualizing gene regulatory networks. Sea urchin endomesoderm network from ... Environment And Gene Regulatory Influence Network (EGRIN) for Halobacterium salinarum NRC-1 from the Baliga Lab. ...
Masuda N, Church GM (May 2003). "Regulatory network of acid resistance genes in Escherichia coli". Molecular Microbiology. 48 ( ... a chromatin-associated protein that influences the gene expression of several environmentally-induced target genes, represses ... HdeA is one of the most abundant proteins found in the periplasmic space of E. coli, where it is one of a network of proteins ...
The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. Academic Press, 2006. M Ptashne and A Gann. Genes ... The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. Academic Press, 2006. S Barolo and JW Posakony. ... "Protein Modularity, Cooperative Binding, and Hybrid Regulatory States Underlie Transcriptional Network Diversification". Cell. ... Thus, common evolutionary rates could be forcing the genes for certain proteins to evolve together while preventing other genes ...
During his doctoral training, his research interests focused on modelling the evolution of gene regulatory networks-he ... Evolutionary modelling of feed forward loops in gene regulatory networks. Biosystems 2008; 91: 231-244. Cooper MB, Loose M, ... The evolutionary influence of binding site organisation on gene regulatory networks. Biosystems 2009; 96: 185-193. "Interview: ...
EMT is determined by a dynamic gene regulatory network (GRN). snail and twist are two key transcription factors that makes up ...
Davidson, Eric H. (2006). The regulatory genome : gene regulatory networks in development and evolution. Amsterdam [Netherlands ... Laubichler, Manfred D.; Renn, Jürgen (2015). "Extended evolution: A Conceptual Framework for Integrating Regulatory Networks ... Newman, Stuart A.; Müller, Gerd B. (2006-01-06), "Genes and Form", Genes in Development, Duke University Press, pp. 38-73, doi: ... "Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype". BMC Evolutionary ...
Yan, Jun; Haifang Wang; Yuting Liu; Chunxuan Shao (October 2008). "Analysis of Gene Regulatory Networks in the Mammalian ... As the E-box is connected to several circadian genes, it is possible that the genes and proteins associated with it are " ... The E-box plays an important role in circadian genes; so far, nine E/E'BOX controlled circadian genes have been identified: ... Gene. 510 (2): 118-125. doi:10.1016/j.gene.2012.08.022. PMID 22960268. Nakahata, Y; Yoshida M; Takano A; Soma H; Yamamoto T; ...
... this is usually referred to as a gene regulatory network. A flurry of interest in gene regulatory networks has been sparked by ... Three models have been proposed to explain the regulatory effects that cis-NATs have on gene expression. The first model ... Predicted gene models using algorithms trained to look for genes gives an increased coverage of the genome at the cost of ... The next step is to use this information to figure out how genes work together and not just in isolation. During the processes ...
Buckingham, M; Rigby, P (February 2014). "Gene Regulatory Networks and Transcriptional Mechanisms that Control Myogenesis". ... Gene ontology. Molecular function. • DNA binding. • sequence-specific DNA binding. • RNA polymerase II regulatory region ... "Entrez Gene: MYOD1 myogenic differentiation 1".. *^ Rudnicki MA, Schnegelsberg PN, Stead RH, Braun T, Arnold HH, Jaenisch R ( ... regulation of gene expression. • striated muscle cell differentiation. • cellular response to oxygen levels. • myoblast fusion ...
... gene regulatory networks, multicellular patterning, chemotaxis, systems neuroscience, the evolution of networks, and the effect ... Systems Biology research includes dynamic gene regulatory networks and systems neuroscience. Some models used are prokaryotes, ... Gene regulation studies include the organization and evolution of the regulatory genome, chromatin composition and ... The Gene Regulation, Stem Cells, and Cancer program focus on mechanisms of gene expression, mechanisms of epigenetic regulation ...
A hierarchical gene regulatory network for adaptive multi-robot pattern formation. IEEE Transactions on Systems, Man, and ... A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. ... a neural controller using a generative coding gene regulatory network model. Artificial life Cognitive robotics Developmental ... Gene networks capable of pattern formation: from induction to reaction-diffusion. Journal of Theoretical Biology, 205:587-603, ...
He proposed the Probabilistic Boolean Network (PBN) model for gene regulatory networks. PBNs have been extensively used for ... Shmulevich, Ilya; Dougherty, Edward (2010). Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory ... and modeling gene regulatory networks. He is the Fellow of IEEE and SPIE. Dougherty is the author of 16 books, whose topics ... A Rule-based Uncertainty Model for Gene Regulatory Networks". Bioinformatics. 18 (2): 261-274. doi:10.1093/bioinformatics/18.2. ...
... gene regulatory network)。 人們之所以能夠出辨認哪些基因序列是調控序列,是因為生物在演化過程中對基因的保留。以大約7千萬年前到9千萬年前分支的人類與老鼠為例[6]:若以電腦比較兩者的基因序列,並且將兩者皆保有的非編碼序列辨識出來, ... 基因劑量(Gene dosage)會對人類的表現型產生龐
These five proteins directly control the timing of expression of over 200 genes. The five master regulatory proteins are ... The genetic network logic responds to signals received from the environment and from internal cell status sensors to adapt the ... The phosphosignaling network monitors the state of progression of the cell cycle and plays an essential role in accomplishing ... The Caulobacter CB15 genome has 4,016,942 base pairs in a single circular chromosome encoding 3,767 genes.[7] The genome ...
If these regulatory networks are disrupted, retinitis pigmentosa, macular degeneration or other visual deficits may result.[18] ... NR2E3 further restricts cells to the rod fate by repressing cone genes. RORbeta is needed for both rod and cone development. ... This structural change causes it to activate a regulatory protein called transducin, which leads to the activation of cGMP ... CRX further defines the photoreceptor specific panel of genes being expressed. NRL expression leads to the rod fate. ...
The Church also established a network of cathedral schools and universities where medicine was studied. The Schola Medica ... Many modern molecular tests such as flow cytometry, polymerase chain reaction (PCR), immunohistochemistry, cytogenetics, gene ... Physiology is the study of the normal functioning of the body and the underlying regulatory mechanisms. ... Since knowledge, techniques, and medical technology continue to evolve at a rapid rate, many regulatory authorities require ...
Regulation of gene expression. *Gene regulatory network. *Developmental-genetic toolkit. *Evolutionary developmental biology ... Such a gene that exhibits multiple phenotypic expression is called a pleiotropic gene . Therefore mutation in a pleiotropic ... Gene pleiotropy occurs when a gene product interacts with multiple other proteins or catalyzes multiple reactions. ... Pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. The underlying mechanism is genes that ...
Some degree of gene flow is normal, and preserves constellations of genes and genotypes.[118][119] An example of this is the ... North American Invasive Species Network, a consortium that uses a coordinated network to advance science-based understanding ... "Biofouling moves up the regulatory agenda - GARD". Retrieved 2018-09-19.. ... "Invasive species" from the Global Legal Information Network Subject Term Index. *Don't Move Firewood - Part of the Continental ...
如同許多疾病,肥胖是基因與環境因子互動所產生的結果。控制食慾與代謝的基因決定了人們在食物不虞匱乏時是否會有肥胖症狀。至2006年為止,已認定人類基因體上有超過41個基因在特定情形下會造成肥胖[117]。擁有兩套肥胖基因(英语:FTO gene)(脂肪與肥 ... 許多國家或組織針對肥
... of a number of genes to stimulate their transcription. Among the genes transcribed are the LDL receptor and HMG-CoA reductase. ... The main regulatory mechanism is the sensing of intracellular cholesterol in the endoplasmic reticulum by the protein SREBP ( ... A small group of scientists, united in The International Network of Cholesterol Skeptics, continues to question the link ... The cleaved SREBP then migrates to the nucleus and acts as a transcription factor to bind to the SRE (sterol regulatory element ...
Gene regulatory network. *cis-regulatory element. *lac operon. *Post-transcriptional *sequestration (P-bodies) ... Watson JD, Baker TA, Bell SP, Gann A, Levine M, Oosick R (2008). Molecular Biology of the Gene. San Francisco: Pearson/Benjamin ...
As this gene is mostly inactive, save for in testis tissue, a methylation mechanism is in place that inactivates this gene in ... 2006). "Global, in vivo, and site-specific phosphorylation dynamics in signaling networks". Cell. 127 (3): 635-48. doi:10.1016/ ... 1999). "Characterization of the regulatory region of the human testis-specific form of the pyruvate dehydrogenase alpha-subunit ... "Entrez Gene: pyruvate dehydrogenase (lipoamide) alpha 2".. *^ Dahl HH, Brown RM, Hutchison WM, Maragos C, Brown GK (October ...
SEEurope Network". SEEurope Network. Retrieved 2017-11-15.. *^ [2] Archived 3 October 2011 at the ... It led to a big push for more regulatory laws which gave workers a lot more power.[37] ... However, in contrast to the countries in the Anglo-Saxon system category, this is a much more widespread network of collective ... Washington, DC, US: Inter-American Development Bank, Research Network Working Paper £R-487, p.5, available at: "Archived copy" ...
... regulatory genes - regulatory T cells - remission - renal - rescue therapy - resistance - retina - retinal detachment - ... HIV prevention trials network (HPTN) - HIV set point - HIV vaccine trials network (HVTN) - HIV-1 - HIV-2 - HIV-associated ... GAG - gamma globulin - gamma interferon - ganglion - GART - gastrointestinal (GI) - gene - gene therapy - genetic engineering ... National Prevention Information Network (NPIN) - natural history study - natural killer cells (NK cells) - NCI - New Drug ...
"Core transcriptional regulatory circuitry in human embryonic stem cells". Cell 122 (6): 947-56. 2005. doi:10.1016/j.cell. ... Gene therapy is first deafness 'cure'». New Scientist. 2005. gada 14. februāris. ... Biocell Center Corporation Partners with New England's Largest Community-Based Hospital Network to Offer a Unique... - MEDFORD ...
Kaplan AS, Levitan RD, Yilmaz Z, Davis C, Tharmalingam S, Kennedy JL (January 2008). "A DRD4/BDNF gene-gene interaction ... to create stable and integrated cytoskeletal networks.[58] Actins have a variety of roles in synaptic functioning. In pre- ... is strongly stimulated by calcium and is primarily under the control of a Cre regulatory component, suggesting a putative role ... Brain-derived neurotrophic factor (BDNF), or abrineurin,[5] is a protein[6] that, in humans, is encoded by the BDNF gene.[7][8] ...
JUSTPAL NetworkEdit. A Justice Sector Peer-Assisted Learning (JUSTPAL) Network was launched in April 2011 by the Poverty ... Rotberg, Eugene (1994). "Financial Operations of the World Bank". Bretton Woods: looking to the future: commission report, ... Carmine Guerriero notices that these reforms have introduced in developing countries regulatory institutions typical of the ... Global Development Learning NetworkEdit. The Global Development Learning Network (GDLN) is a partnership of over 120 learning ...
Lüscher B (2001). "Function and regulation of the transcription factors of the Myc/Max/Mad network". Gene 277 (1-2): 1-14. PMID ... chromosome translocation in a human leukemia T-cell line indicates that putative regulatory regions are not altered". Proc. ... 1991). "Mapping of the MYC gene to band 8q24.12----q24.13 by R-banding and distal to fra(8)(q24.11), FRA8E, by fluorescence in ... "A phosphorylation site located in the NH2-terminal domain of c-Myc increases transactivation of gene expression". J. Biol. Chem ...
Regulation of gene expression. *Gene regulatory network. *Developmental-genetic toolkit. *Evolutionary developmental biology ... Hall, B.K., Hallgrímsson, B. Monroe, W.S. (2008). Strickberger's evolution: the integration of genes, organisms and populations ... the fitness of the individual because the female is producing more offspring and therefore passing on more of her genes.[33] In ...
Regulation of gene expression. *Gene regulatory network. *Developmental-genetic toolkit. *Evolutionary developmental biology ... then gene A is epistatic and gene B is hypostatic. For example, the gene for total baldness is epistatic to the gene for brown ... Epistasis within genes[edit]. Just as mutations in two separate genes can be non-additive if those genes interact, mutations in ... The hair-colour genes are hypostatic to the baldness gene. The baldness phenotype supersedes genes for hair colour and so the ...
... is executed by autophagy-related (Atg) genes. The first autophagy genes were identified by genetic screens conducted ... R. Kang, H. J. Zeh, M. T. Lotze, and D. Tang, 'The Beclin 1 Network Regulates Autophagy and Apoptosis', Cell Death Differ, 18 ( ... The end-products of autophagic digestion may also serve as a negative- feedback regulatory mechanism to stop prolonged activity ... and that at least 15 APG genes are involved in autophagy in yeast.[61] A gene known as ATG7 has been implicated in nutrient- ...
Ckurshumova, Wenzislava (2007). "Regulatory hierarchies in auxin signal transduction and vascular tissue development". ... "Gene function classification using Bayesian models with hierarchy-based priors". BMC Bioinformatics. London, England: BioMed ... Hierarchical clustering of networks. *Hierarchical constraint satisfaction. *Hierarchical linear modeling. *Hierarchical ...
The protein encoded by this gene is a member of the Ser/Thr protein kinase family. This protein is highly similar to the gene ... The activity of this kinase is restricted to the G1-S phase, which is controlled by the regulatory subunits D-type cyclins and ... "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173-8. doi:10.1038/ ... Cyclin-dependent kinase 4 also known as cell division protein kinase 4 is an enzyme that in humans is encoded by the CDK4 gene ...
... is executed by autophagy-related (Atg) genes. The first autophagy genes were identified by genetic screens conducted ... The end-products of autophagic digestion may also serve as a negative- feedback regulatory mechanism to stop prolonged activity ... "The Beclin 1 network regulates autophagy and apoptosis". Cell Death and Differentiation. 18 (4): 571-80. doi:10.1038/cdd. ... and that at least 15 APG genes are involved in autophagy in yeast.[69] A gene known as ATG7 has been implicated in nutrient- ...
"Gene. 570 (1): 25-35. doi:10.1016/J.GENE.2015.06.062. PMC 4519417. PMID 26119091.. CS1 maint: uses authors parameter (link). . ... Figure 1. Interaction network and domain structure scheme of Cass4. SH3 domain (SH3) preceded by a short region with no defined ... "Connecting the dots: potential of data integration to identify regulatory SNPs in late-onset Alzheimer's disease GWAS findings ... Gene[edit]. The chromosomal location of the CASS4 gene is 20q13.31, with genomic coordinates of 20: 56411548-56459340 on the ...
Other downstream pathways are triggered as well (MAPK, NF-κB, NFAT) which results in gene transcription in the nucleus.[18] ... On helper T cells and regulatory T cells, this co-receptor is CD4 that is specific for MHC class II. ... Ionizable residues in the transmembrane domain of each subunit form a polar network of interactions that hold the complex ... Unlike immunoglobulins, however, TCR genes do not undergo somatic hypermutation, and T cells do not express activation-induced ...
Craig A. Lockard, Societies, Networks, and Transitions[73]. The Maharashtra drought in which there were zero deaths and one ... Chang, Gene Hsin; Wen, Guanzhong James (October 1997). "Communal Dining and the Chinese Famine of 1958-1961". Economic ... A systematic attempt at creating the necessary regulatory framework for dealing with famine was developed by the British Raj in ... Craig A. Lockard (2010). "Societies, Networks, and Transitions, Volume 3". Cengage Learning. p. 610. ISBN 1-4390-8534-X ...
Regulation of gene expression. *Gene regulatory network. *Developmental-genetic toolkit. *Evolutionary developmental biology ... Morris KL (2008). "Epigenetic Regulation of Gene Expression". RNA and the Regulation of Gene Expression: A Hidden Layer of ... They control gene expression including virulence genes in pathogens and are viewed as new targets in the fight against drug- ... There are several layers of regulation of gene expression. One way that genes are regulated is through the remodeling of ...
A mutation of the CYP24A1 gene can lead to a reduction in the degradation of vitamin D and to hypercalcaemia (see Vitamin_D: ... Complex regulatory mechanisms control metabolism. Recent epidemiologic evidence suggests that there is a narrow range of ... and Blood Institute Family Heart Study and Hypertension Genetic Epidemiology Network". The American Journal of Cardiology. 97 ( ... All of these affect gene transcription and overwhelm the vitamin D signal transduction process, leading to vitamin D toxicity.[ ...
Loos RJ; Bouchard C (maj 2008). "FTO: the first gene contributing to common forms of human obesity". Obes Rev. Vol. 9 no. 3. ... Christakis, N.A.; Fowler, James H. (2007). "The Spread of Obesity in a Large Social Network over 32 Years". New England Journal ... Bays, HE (marec 2011). "Lorcaserin: drug profile and illustrative model of the regulatory challenges of weight-loss drug ... Študije, ki so se usmerile na dedne vzorce in ne na specifične gene, so ugotovile, da je med otroci, katerih oba starša sta ...
Speed Up and Sit Still, Martin Paul Whitely, Attention Deficit Hyperactivity Disorder Policy, Practice and Regulatory Capture ... Mauricio Arcos-Burgos ja Maximilian Muenke, Toward a better understanding of ADHD: LPHN3 gene variants and the susceptibility ... Scottish Intercollegiate Guidelines Network, Management of attention deficit and hyperkinetic disorders in children and young ... Brookes, K.J., Hawi, Z., Kirley, A. jt, (2008). Association of the steroid sulfatase (STS) gene with attention deficit ...
Rochester, NY: Social Science Research Network. SSRN 2818335.. *^ Chadegani, Arezoo Aghaei; Salehi, Hadi; Yunus, Melor Md; ... The journal impact factor (JIF) was originally designed by Eugene Garfield as a metric to help librarians make decisions about ... regulatory and marketing purposes. In 2010, the US Senate Finance Committee released a report that found this practice was ... About 10,000 journals without APC are listed in DOAJ[36] and the Free Journal Network.[37][38] ...
Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression ... The Special Feature on gene regulatory networks in this issue of PNAS highlights an emerging field in the biosciences: gene ... Network substructure: Gene regulatory networks are inhomogeneous compositions of different kinds of subcircuits, each ... a gene regulatory network consists of assemblages of these information-processing units; thus, it is essentially a network of ...
Gene regulatory networks play a vital role in organismal development and function by controlling gene expression. With the ... Gene regulatory networks play a vital role in organismal development and function by controlling gene expression. With the ... Divided into five convenient sections, Gene Regulatory Networks: Methods and Protocols details how each of these approaches ... Authoritative and accessible, Gene Regulatory Networks: Methods and Protocols aims to provide novices and experienced ...
Gene regulatory networks (GRNs) Boolean networks Biological Modeling Satisfiability Modulo Theories (SMT) Synthesis Self- ... Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually ... Shavit Y., Yordanov B., Dunn SJ., Wintersteiger C.M., Hamadi Y., Kugler H. (2015) Switching Gene Regulatory Networks. In: Lones ... biological programs.We introduce Switching Gene Regulatory Networks to enable the modeling and analysis of network ...
Analysis of various gene regulatory networks. (a) Single gene. (b) Autoregulatory protein. Parameters are the same as in a but ... Regulatory genes such as the cI gene of phage λ (2) and the malT gene of Escherichia coli (19) are often poorly translated. It ... Single Gene.. The single gene is the fundamental module of gene regulatory circuits. Fluctuations in the concentration of a ... groups of genes and proteins work in concert. The introduction of regulatory interactions creates a gene network with complex ...
B) Sub-circuits of pancreas gene regulatory network representing canonical network motifs.. (C) Pancreatic progenitor gene ... Gene regulatory networks governing pancreas development.. Arda HE1, Benitez CM, Kim SK. ... Elucidation of cellular and gene regulatory networks (GRNs) governing organ development will accelerate progress toward tissue ... regulatory network. Pink nodes - transcription factors, yellow nodes - effector genes, black lines - positive/inductive ...
Although live cell imaging has not been often used in the study of gene regulatory networks, it is a method that uniquely ... Although live cell imaging has not been often used in the study of gene regulatory networks, it is a method that uniquely ... Dynamical information about network components and their interactions is critical to predictive modeling of gene regulatory ... ...
In this paper we tried to review the different methods for reconstructing gene regulatory networks. ... Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for ... Modeling of these networks is an important challenge to be addressed in the post genomic era. ... development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics ...
... interwoven structures of regulatory interactions summarized in gene regulatory networks. In this thesis, I address two ... which I have developed as a framework for automatic generation of gene regulatory network models. I have developed a novel ... a method for reverse engineering of gene regulatory networks from temporal data. This computational approach uses a neural ... This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. GeNGe supports also the ...
... a large number of gene expression profiles have been produ.. ... A gene regulatory network is represented by a directed graph, ... Inferring Gene Regulatory Networks Development of high-throughput, next-generation sequencing and other advanced technologies, ... Citation: Jason T. L. Wang (2015) Inferring Gene Regulatory Networks: Challenges and opportunities. . J Data Mining Genomics ... 9] evaluated nine state-of-the-art gene regulatory network inference methods using 38 simulated datasets. These authors ...
"gene regulatory networks"[MeSH Terms] OR Gene Regulatory Networks[Text Word]. Search. ... Gene Regulatory Networks. Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and ... The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations ... All MeSH CategoriesPhenomena and Processes CategoryGenetic PhenomenaGenetic StructuresBase SequenceRegulatory Sequences, ...
... the study of gene regulatory networks," says Davidson. Gene regulatory networks are the complex networks of gene interactions ... DNA sequence »Genetics »Genomics »T cells »embryonic cells »fruit flies »gene regulator »gene regulatory networks »immune ... gene regulatory networks , immune system cells , lampreys , nervous system , postembryonic gene , sea urchins , soil-dwelling ... "These networks lie at the heart of the regulatory apparatus, and they consist of genes that encode proteins that regulate other ...
Purchase Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome - 1st Edition. Print ... Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome 1st Edition. 0.0 star rating ... Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and ... Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource ...
Gene regulatory network inference (that is, determination of the regulatory interactions between a set of genes) provides ... Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory ... Open this publication in new window or tab ,,Functional association networks as priors for gene regulatory network inference. ... GRN, gene regulatory network, network inference, signal to noise ratio, model selection, variable selection, data properties, ...
The gene regulatory networks are modelled with a Bayesian network. The gene expThe use of large scale public microarray data ... The gene regulatory networks are modelled with a Bayesian network. The gene expThe use of large scale public microarray data ... Learning large gene regulatory networks with thousands of genes with any certainty from microarray data is extremely ... Learning large gene regulatory networks with thousands of genes with any certainty from microarray data is extremely ...
Buy the Hardcover Book Gene Regulatory Networks by Bart Deplancke at, Canadas largest bookstore. + Get Free Shipping ... Gene regulatory networks play a vital role in organismal development and function by controlling gene expression. With the ... Gene Regulatory Networks: Methods and Protocols. EditorBart Deplancke, Nele Gheldof. Hardcover , September 22, 2011. ... Title:Gene Regulatory Networks: Methods and ProtocolsFormat:HardcoverDimensions:457 pagesPublished:September 22, 2011Publisher: ...
Optimal Intervention Methods for Markovian Gene Regulatory Networks: 10.4018/978-1-5225-0353-8.ch005: A central problem in ... "Optimal Intervention Methods for Markovian Gene Regulatory Networks." Emerging Research in the Analysis and Modeling of Gene ... "Optimal Intervention Methods for Markovian Gene Regulatory Networks." In Emerging Research in the Analysis and Modeling of Gene ... From a graphical perspective, genes are nodes in this network and edges describe regulatory relationships between genes. A GRN ...
I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell ... I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell ... Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust ... Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust ...
ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory ... Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays. Authors: Ahsen, Mehmet Eren, Özbay, Hitay, ... Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays. Authors. * Mehmet Eren Ahsen ... This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and ...
"Gene Regulatory Networks" by people in Harvard Catalyst Profiles by year, and whether "Gene Regulatory Networks" was a major or ... "Gene Regulatory Networks" is a descriptor in the National Library of Medicines controlled vocabulary thesaurus, MeSH (Medical ... Gene Regulatory Networks*Gene Regulatory Networks. *Gene Regulatory Network. *Network, Gene Regulatory ... Below are the most recent publications written about "Gene Regulatory Networks" by people in Profiles. ...
... genes with a similarly regulated set of genes, also known as the target genes. In order to build such networks, the method is ... 3 LINKER : Generating Gene Regulatory Networks. 3.1 Overview of the proposed method. The aim of the proposed method is to find ... TraRe: Identification of conditions dependant Gene Regulatory Networks. Jesús de la Fuente, Mikel Hernaez and Charles Blatti ... 3 LINKER : Generating Gene Regulatory Networks *3.1 Overview of the proposed method ...
These molecules and their interactions comprise a gene regulatory network. A typical gene regulatory network looks something ... Other work has focused on predicting the gene expression levels in a gene regulatory network. The approaches used to model gene ... Thus gene regulatory networks approximate a hierarchical scale free network topology. This is consistent with the view that ... Another widely cited characteristic of gene regulatory network is their abundance of certain repetitive sub-networks known as ...
Inference of gene regulatory networks via multiple data sources and a recommendation method ... DSR allows the network to be completely self-organizing and self-configuring, without the need for any existing network ... DSR allows the network to be completely self-organizing and self-configuring, without the need for any existing network ... Bayesian Network Classifiers by Nir Friedman, Dan Geiger, Moises Goldszmidt , 1997 "... Recent work in supervised learning has ...
... reconstruction of gene regulatory networks has become a hot topic. Regulatory networks have been intensively studied over the ... In this paper we apply the Dynamic Bayesian network (DBN) to construct the Arabidopsis gene regulatory networks by analyzing ... Then the gene regulatory networks constructed based on complete and incomplete data series are compared in terms of statistics ... Two time points are found to play a significant role in the Arabidopsis gene regulatory networks. Pathway analysis of ...
... we provide a detailed analysis of a gene regulatory network exhibiting bistability within a certain region of parameter space. ...
Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here ... we describe the molecular principles of pluripotency gene function and highlight post-trans … ... Deconstructing the pluripotency gene regulatory network Nat Cell Biol. 2018 Apr;20(4):382-392. doi: 10.1038/s41556-018-0067-6. ... Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here ...
Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps ... of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the ... is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major ... is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major ...
3. Modeling and Inferring Gene Regulatory Networks. Gene regulatory networks capture the interactions present among the genes. ... It can be seen that a gene can be a regulator for gene if and only if (iff) . The mutual information in this case is given by ... The gene expression is assumed to follow a dynamic model given by where and denotes the protein activity profile of ... Gene Expression Data. Of all the available datasets, gene expression data is the most widely used for gene regulatory network ...
The gene expression data analysis aims to understand the genes interacting ... The gene expression analysis is an important research area of Bioinformatics. ... Parallel Architecture for Gene Regulatory Network: 10.4018/978-1-5225-2229-4.ch034: ... The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study ...
A gene regulatory network (GRN) approach provides a means to investigate the nature of this conservation and divergence even ... This model provides a morphologically simple system in which to efficiently unravel regulatory connections in an organism that ... This model provides a morphologically simple system in which to efficiently unravel regulatory connections in an organism that ... To experimentally access this regulatory circuitry, we have developed an organism-wide model of gut-associated bacterial ...
Not only does the teams work help explain how links in that gene-regulatory chain are constructed. "Gene-regulatory proteins ... CSHL scientists discover new details of a gene-regulatory network governing metabolism. 25.02.2008 ... A detailed understanding of how gene regulatory proteins are controlled may offer new and unanticipated opportunities to design ... Why The Regulatory Cascade Is Important. "It is becoming increasingly clear that the metabolic state of a cell is linked to the ...
  • 4 ] categorized existing network inference methods into three groups: unsupervised, supervised and semi-supervised. (
  • 5 , 6 ] developed an in silico benchmark suite within the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project [ 7 , 8 ], and assessed the performance of 29 network inference methods. (
  • They concluded that reliable network inference from gene expression data remains an unsolved problem. (
  • 9 ] evaluated nine state-of-the-art gene regulatory network inference methods using 38 simulated datasets. (
  • Indeed, the parameter settings often affect the accuracy of a network inference method, and identifying the optimal parameter values is a very challenging task. (
  • The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. (
  • Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. (
  • The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. (
  • It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. (
  • Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. (
  • The third investigation aimed to understand the influence of system and data properties on network inference accuracy. (
  • L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. (
  • The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. (
  • Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. (
  • Two important problems in this considerably nascent field of computational biology are the inference of gene regulatory networks and the inference of protein-protein interaction networks. (
  • This paper first looks at how the genes and proteins interact with themselves and then discusses the inference of an integrative cellular network of genes and proteins combined. (
  • The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. (
  • Motivation: Transcriptional regulatory network inference methods have been studied for years. (
  • The validity of an inference procedure must be evaluated relative to its ability to infer a model network close to the ground-truth network from which the data have been generated. (
  • The input to an inference algorithm is a sample set of data and its output is a network. (
  • To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. (
  • The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes. (
  • Thus the GRN inference problem investigated in this paper refers to finding the regulatory relationship between the genes of an organism. (
  • There may be other models which could integrate prior knowledge to improve the performance, but we only considered the ab initio network inference approaches here as prior knowledge is able to be integrated into most de novo network reconstruction methods easily. (
  • Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. (
  • Motivation: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. (
  • 2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. (
  • This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. (
  • While homogeneous models, like conventional dynamic Bayesian networks, lack the flexibility to succeed in this task, fully flexible models suffer from inflated inference uncertainty due to the limited amount of available data. (
  • We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. (
  • They explain exactly how genomic sequence encodes the regulation of expression of the sets of genes that progressively generate developmental patterns and execute the construction of multiple states of differentiation. (
  • Here, we have compiled reference GRNs underlying pancreas development from data mining that integrates multiple approaches, including mutant analysis, lineage tracing, cell purification, gene expression and enhancer analysis, and biochemical studies of gene regulation. (
  • Gene regulation is accomplished mainly by the interplay of multiple transcription factors. (
  • This research aims to build around known networks from the literature on gene regulation, and assesses which other genes are likely to play a regulatory role or be in the same regulatory pathways. (
  • Gene regulation is one of the many fascinating processes taking place in a living organism whereby the expression and repression of genes are controlled in a systematic manner. (
  • Scientists at Cold Spring Harbor Laboratory (CSHL) are in the forefront of efforts to demonstrate how the regulation of genes governs fundamental life processes, including metabolism. (
  • The biochemical cascade identified by the team is part of a complex chain of events whose object is regulation of the output of specific genes. (
  • The application of network theory facilitated co-analysis of genetic variation with gene expression, recapitulated aspects of the known molecular biology of skin pigmentation and provided insights into the transcription regulation and epistatic interactions involved in piebald Merino sheep. (
  • The second part of this project will mine two existing murine liver gene expression data sets to find networks of highly correlated genes which share common regulation loci in the genome. (
  • A network of local and redundant gene regulation governs Arabidopsis seed maturation. (
  • Some of these broad sets of data have already been assembled for building networks of gene regulation. (
  • By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. (
  • In this paper a new model for Genetic Regulation Networks, with more biochemical details than previous models, is proposed. (
  • Most of Gene Regulatory Network (GRN) studies are based on crisp and parametric algorithms, despite inherent fuzzy nature of gene co-regulation. (
  • MicroRNAs (miRNAs) play key roles in a variety of cellular processes through regulation of their target gene expression. (
  • The specific binding of a transcription factor to its DNA target site - the cis-element - located in the gene promoter, is considered the pivotal event in gene transcriptional regulation. (
  • Furthermore, efforts to describe the resulting complexity of gene regulation and the relation to whole genome architectural properties are discussed. (
  • Understanding gene expression and regulation is essential for understanding biological mechanisms. (
  • Inferring the topology of gene regulatory networks is fundamental to understand the complexity of interdependencies among gene up and down regulation. (
  • 11. Jaźwińska A, Kirov N, Wieschaus E, Roth S, Rushlow C. The Drosophila gene brinker reveals a novel mechanism of Dpp target gene regulation. (
  • Transcriptional regulation of gene activity is essential for any living organism. (
  • Cis-regulatory modules (CRM) are segments of DNA responsible for tissue- and time- specific regulation of gene expression (1). (
  • This network reveals a new model of PD regulation which we call the "crossover model", because the proximal morphogen (RA) controls the distal boundary of Hoxa11, while conversely the distal morphogens (FGFs) control the proximal boundary. (
  • The proper functioning of any living cell relies on complex networks of gene regulation. (
  • We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. (
  • Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. (
  • Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. (
  • One of the most fundamental and critical functions of embryological development is the control and regulation of differential genes and gene networks. (
  • The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. (
  • Davidson was best known for his pioneering work on the role of gene regulation in evolution, on embryonic specification and for spearheading the effort to sequence the genome of the purple sea urchin, Strongylocentrotus purpuratus. (
  • While at Rockefeller and very early in his career, he and Roy Britten, then at the Carnegie Institution of Washington, speculated on how the products of transcription, e.g. various RNAs or other downstream products, would need to in principle interact in order for cellular differentiation and gene regulation to occur in multicellular organisms. (
  • This research program eventually led him to investigations regarding the role of gene regulation in cell lineage and embryonic territory specification, both endeavors of which contributed substantially to many biological disciplines, including developmental biology, systems biology and evolutionary developmental biology. (
  • Elucidation of cellular and gene regulatory networks (GRNs) governing organ development will accelerate progress toward tissue replacement. (
  • This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. (
  • It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. (
  • Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed. (
  • Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. (
  • Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. (
  • However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. (
  • It includes applications of a stochastic process theory called G-networks and a reverse engineering technique for large-scale GRNs. (
  • Additionally a series of bioinformatics techniques was applied in brain tumor data to detect disease candidate genes along with their large-scale GRNs. (
  • However, the properties of the gene regulatory networks (GRNs) that control the signaling dynamics during epithelial-mesenchymal (E-M) interactions in organogenesis are largely unknown. (
  • Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. (
  • In a previous comparative study of the gene regulatory networks (GRNs) that embody the genomic program for embryogenesis in these animals, we discovered an almost perfectly conserved five-gene network subcircuit required for endoderm specification. (
  • The variation in structure and function of gene regulatory networks (GRNs) participating in organisms development is a key for understanding species-specific evolutionary strategies. (
  • To understand the processes of development and evolution of living organisms, the "gene regulatory networks", or GRNs have to be taken into account. (
  • In genetic regulatory networks (GRNs), the diffusion rate of mRNA and protein play a key role in regulatory mechanisms of gene expression, especially in translation and transcription. (
  • These highly conserved gene-expression patterns point to widespread conservation of GRNs across the animal kingdom. (
  • Conventional methods to construct synthetic gene regulatory networks (GRNs) need time-consuming trial and error steps, preventing their use in synthetic biology. (
  • I've inferred 6 GRNs (Gene Regulatory Networks) using CLR (Context Likelihood Relat. (
  • Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. (
  • The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions. (
  • GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). (
  • A challenging research problem that has drawn a lot of attention in the past is to infer gene regulatory networks from the expression data. (
  • Unsupervised algorithms infer networks based solely on gene expression profiles and do not need any training examples. (
  • Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their. (
  • Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. (
  • The availability of high-throughput genomic data has motivated the development of numerous algorithms to infer gene regulatory networks. (
  • In this paper, we present a two-step approach to tackle this challenge: first, an unbiased cross-correlation is used to determine the probable list of time-delays and then, a penalized regression technique such as the LASSO is used to infer the time-delayed network. (
  • Finally, approaches and available web-based information resources to utilize information on transcription factor - target gene binding events to infer complex gene regulatory networks are presented. (
  • with such abundant high-throughput screening data available, researchers have tried to infer, or reverse-engineer, gene networks. (
  • Because Banjo implements both Bayesian and dynamic Bayesian networks, it can infer gene networks from steady-state gene expression data or from time-series gene expression data. (
  • However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. (
  • In this study, we developed a time-delay correlation algorithm (TDCor) to infer the gene regulatory network ( GRN ) controlling LR primordium initiation and patterning in Arabidopsis from a time-series transcriptomic data set. (
  • Parameter optimization coupled with model selection is a convenient approach to infer gene regulatory networks from experimental gene expression data, but so far it has been limited to single cells or static tissues where growth is not significant. (
  • A challenging objective in computational systems biology is to infer these time-varying gene regulatory networks from typically short time series of transcriptional profiles. (
  • During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology. (
  • Each such module receives and integrates multiple inputs, in the form of regulatory proteins (activators and repressors) that recognize specific sequences within them. (
  • Some regulatory modules control the activities of the genes encoding regulatory proteins. (
  • translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. (
  • In living systems, however, groups of genes and proteins work in concert. (
  • These networks lie at the heart of the regulatory apparatus, and they consist of genes that encode proteins that regulate other genes, and the DNA sequences which control when and where they are expressed," says Davidson, who authored a paper in the special feature about a gene regulatory network found in sea urchin embryos. (
  • A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. (
  • Some proteins though serve only to activate other genes, and these are the transcription factors that are the main players in regulatory networks or cascades. (
  • Each time a cell divides, two cells result which, although they contain the same genome in full, can differ in which genes are turned on and making proteins. (
  • In parallel with this process of building structure, the gene cascade turns on genes that make structural proteins that give each cell the physical properties it needs. (
  • At one level, biological cells can be thought of as "partially mixed bags" of biological chemicals - in the discussion of gene regulatory networks, these chemicals are mostly the messenger RNAs (mRNAs) and proteins that arise from gene expression. (
  • A typical gene regulatory network looks something like this: The nodes of this network can represent genes, proteins, mRNAs, protein/protein complexes or cellular processes. (
  • Edges between nodes represent interactions between the nodes, that can correspond to individual molecular reactions between DNA, mRNA, miRNA, proteins or molecular processes through which the products of one gene affect those of another, though the lack of experimentally obtained information often implies that some reactions are not modeled at such a fine level of detail. (
  • Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. (
  • Genes and proteins interact with themselves and each other and orchestrate the successful completion of a multitude of important tasks. (
  • Since the genes may be coding for TFs and/or other proteins, a complex network of genes and proteins is formed. (
  • Gene-regulatory proteins impact every property of a cell and have long been recognized as possible targets for drugs," said Dr. Joshua-Tor. (
  • A detailed understanding of how gene regulatory proteins are controlled may offer new and unanticipated opportunities to design drugs that would impact this class of proteins. (
  • The human genome encodes some 350 Kruppel-associated box (KRAB) domain-containing zinc-finger proteins (KZFPs), the products of a rapidly evolving gene family that has been traced back to early tetrapods(1,2). (
  • This new model is based on an artificial genome from which several products, genes, mRNA, miRNA, non-coding RNA, and proteins are extracted and connected in an heterogeneous directed graph. (
  • Whole-genome duplicates ( WGD s) have approximately twice as many footprints in their promoters left by potential regulatory proteins than do tandem duplicates ( TD s). (
  • Regulatory networks of living systems include complex and vast interactions between proteins, metabolites, RNA, various signaling molecules and DNA. (
  • genes, proteins, metabolites, while edges represent the presence of interaction activities between such network components. (
  • The reconstruction of the regulatory interactions among DNA, RNAs and proteins in a cell is probably the most important and key challenge in molecular biology. (
  • They recognize specific operator sequences close-by the promoter regions of the controlled target genes, referred to as transcription factor binding sites (TFBSs), and thereby influence the amount of produced proteins. (
  • The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. (
  • Stimulator of IFN genes (STING) are a group of transmembrane proteins that are involved in the induction of type I interferon that is important in the innate immune response. (
  • Dondelinger, F., Lébre, S., Husmeier, D.: Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. (
  • The gene regulatory networks are modelled with a Bayesian network. (
  • In this paper we evaluate approaches for inducing classifiers from data , based on the theory of learning Bayesian networks . (
  • In this paper we apply the Dynamic Bayesian network (DBN) to construct the Arabidopsis gene regulatory networks by analyzing the time-series gene microarray data. (
  • In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. (
  • Four main network model architectures can be distinguished: i) information theory models, ii) boolean network models, iii) differential and difference equation models, iv) Bayesian models. (
  • Bayesian models, or more generally graphical models, make use of Bayes rules and consider gene expressions as random variables. (
  • In the present paper we explore a semi-flexible model based on a piecewise homogeneous dynamic Bayesian network regularized by gene-specific inter-segment information sharing. (
  • A Bayesian network is a model to study the structures of gene regulatory networks. (
  • Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. (
  • A gene regulatory network is represented by a directed graph, in which nodes represent transcription factors or mRNA with edges showing transcriptional regulatory relationships between two nodes. (
  • However, the regulatory network of miRNA-mRNA interactions during viral infection remains largely unknown. (
  • Finally, complex miRNA-mRNA regulatory networks were derived using the RNAseq, small RNAseq and degradome data. (
  • Utilizing both in situ hybridization and quantitative mRNA analysis, we investigated the changes in the gene network state caused by the removal of one or both of the early acting enhancers. (
  • We applied various microarray-based approaches to determine key gene-expression intermediates in exponentially growing fission yeast, providing genome-wide data for translational profiles, mRNA steady-state levels, polyadenylation profiles, start-codon sequence context, mRNA half-lives, and RNA polymerase II occupancy. (
  • A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. (
  • The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. (
  • Pink nodes - transcription factors, yellow nodes - effector genes, black lines - positive/inductive relation, red lines - negative/inhibitory relation. (
  • An example here refers to an edge between two nodes in a network. (
  • A major strength of this network model is the extensive cis -regulatory analyses conducted for many nodes (e.g. (
  • The Dynamic Source Routing protocol (DSR) is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. (
  • Pathway analysis of significant nodes revealed three key regulatory genes. (
  • One of the most encountered representations of gene regulatory networks is in terms of a graph, where the genes are depicted by its nodes and the edges represent the interactions between them. (
  • In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately controlled by transcription factors driven by trans-acting expression quantitative trait loci. (
  • In this paper a GRN is represented as a graph which consists of a set of nodes that represent genes and a set of edges that represent the interactions between genes. (
  • Such models represent biological regulations as a network where nodes represent elements of interactions, eg. (
  • Nodes are rectangular and support gene labels of up to 12 characters. (
  • GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. (
  • Reengineering genomic control systems: To redesign these most potent of all biological control systems, to both intellectual and practical ends, it is necessary to understand thoroughly the flow of causality in a genomically encoded gene regulatory network. (
  • Currently, there are limited tools for the construction and analysis of such self-modifying biological programs.We introduce Switching Gene Regulatory Networks to enable the modeling and analysis of network reconfiguration, and define the synthesis problem of constructing switching networks from observations of cell behavior. (
  • Ahmed, A., Xing, E.: Recovering time-varying networks of dependencies in social and biological studies. (
  • Chaouiya, C.: Petri net modelling of biological networks. (
  • Doursat, R.: The growing canvas of biological development: multiscale pattern generation on an expanding lattice of gene regulatory nets. (
  • The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin. (
  • Here our goal is to quantify the macroscopic statistics of genetic networks given the microscopic rate constants and interactions and to investigate the evolutionary and biological implications of noise. (
  • I have developed a novel algorithm for the generation of network structures featuring important biological properties. (
  • Gene regulatory networks govern the functional development and biological processes of cells in all organisms. (
  • It is becoming increasingly clear that the metabolic state of a cell is linked to the expression of its genes in a way that impacts biological processes of many kinds, ranging from cancer to aging," said Dr. Joshua-Tor. (
  • Topological analysis results are in accordance with the complex network representation of biological processes. (
  • The properties of the network were consistent with the biological features of P. aeruginosa . (
  • The dynamics of the networks are studied and some considerations about the biological meaning are made. (
  • The results conformed to biological knowledge and showed that most of cancer related GRN changes were caused by differentially expressed genes. (
  • Motif statistics of artificially evolved and biological networks. (
  • Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. (
  • Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. (
  • Altogether, the integration of documented transcription factor regulatory associations with subnetworks defined by a system of structural equations using quantitative trait loci data is an effective means to delineate the transcriptional control of biological pathways. (
  • The proposed algorithms were compared to existing approaches on four different biological networks. (
  • In silico methods represent a promising direction that, through a reverse engineering approach, aim to extract gene regulatory networks from prior biological knowledge and available genomic and post-genomic data. (
  • hello, i want to ask if there are databases with already reconstructed biological networks. (
  • A diagnostic strategy is built into the algorithm to evaluate the scores of the triplets so that those with low scores are kept and a regulatory network is constructed based on this information and existing biological knowledge. (
  • When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. (
  • This process can be viewed as a program prescribing the system dynamics, governed by a network of genetic interactions. (
  • Using established computational tools, we integrated and represented these networks in frameworks that should enhance understanding of the surging output of genomic-scale genetic and epigenetic studies of pancreas development and diseases such as diabetes and pancreatic cancer. (
  • In this brief review we discuss the importance of dynamics to network modeling, and recent advances in imaging and genetic engineering technologies that are making the use of imaging for network analysis possible. (
  • An understanding of how genetic polymorphisms, gene expression and toxicity are related in the liver will increase our ability to predict both which environmental chemicals will prove harmful and which human sub-populations are particularly vulnerable to such chemicals. (
  • Third, by examining the interplay between human KZFPs and other transcriptional modulators, we obtained evidence that KZFPs exploit evolutionarily conserved fragments of transposable elements as regulatory platforms long after the arms race against these genetic invaders has ended. (
  • Systems-level approaches for identifying and analyzing genetic interaction networks in Escherichia coli and extensions to other prokaryotes. (
  • Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. (
  • A genetic regulatory network mediated by small RNA with two time delays is investigated. (
  • Xiao, M., Cao, J.: Genetic oscillation deduced from Hopf bifurcation in a genetic regulatory network with delays. (
  • Zhang, D., Yu, L.: Passivity analysis for stochastic Markovian switching genetic regulatory networks with time-varying delays. (
  • A large number of genes involved in lateral root ( LR ) organogenesis have been identified over the last decade using forward and reverse genetic approaches in Arabidopsis thaliana . (
  • The predicted network topology links the very early-activated genes involved in LR initiation to later expressed cell identity markers through a multistep genetic cascade exhibiting both positive and negative feedback loops. (
  • Hi Is there any software to perform Genetic regulatory networks analysis for deep RNA seq data? (
  • They regulate the expression of thousands of genes in any given developmental process. (
  • Learning large gene regulatory networks with thousands of genes with any certainty from microarray data is extremely challenging. (
  • Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. (
  • thus, it is essentially a network of analogue computational devices, the functions of which are conditional on their inputs. (
  • With the availability of complete genome sequences, several novel experimental and computational approaches have recently been developed which promise to significantly enhance our ability to comprehensively characterize these regulatory networks by enabling the identification of respectively their genomic or regulatory state components, or the interactions between these two in unprecedented detail. (
  • In this thesis, I address two approaches of computational analysis of such networks, forward modeling and reverse engineering. (
  • This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. (
  • This computational approach uses a neural network together with a sophisticated learning algorithm (backpropagation through time). (
  • Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. (
  • We propose to use mouse models for the development of computational techniques to find robust transcriptional networks which vary in a genetically heterogeneous populations and to produce candidate gene expression networks for future use in predictive models of liver toxicity. (
  • On the basic computational structure of gene regulatory networks. (
  • 2) the development and the application of a computational approach for the identification of post-translational modulators of transcription factor activity from gene expression profiles. (
  • Computational dynamic analysis shows that our proposed Gene Regulatory Network model recovers exactly three attractors, each of them defined by a specific gene expression profile that corresponds to the epithelial, senescent, and mesenchymal stem-like cellular phenotypes, respectively. (
  • We are looking for three highly motivated postdoctoral researchers in computational analysis of gene regulatory networks (network robustness and topological properties) and its applications in cancer biology. (
  • Here, we present a computational study in which we determine an optimal gene regulatory network from the spatiotemporal dynamics of gene expression patterns in a complex 2D growing tissue (non‐isotropic and heterogeneous growth rates). (
  • Tests of the cis-regulatory predictions of the model are greatly facilitated by interspecific computational sequence comparison, which affords a rapid identification of likely cis-regulatory elements in advance of experimental analysis. (
  • The proposed techniques such as stochastic modeling (bottom-up) and reverse engineering (top-down) could provide a systematic view of a complex system and an efficient guideline to identify candidate genes or pathways triggering a specific phenotype of a cell. (
  • We further found that a major part of pan-cancer-promoting genes and the signal transducers of the pan-cancer-promoting signaling pathways, including the epithelial-to-mesenchymal transition ( EMT ) mesenchymal marker genes, display neural specific expression during embryonic neurulation. (
  • In Chapter 3 and 4, I describe the development of a novel approach named DINA (Differential Network Analysis) for the identification of differentially co-regulated pathways. (
  • I then applied DINA to these networks in order to identify tissue-specific pathways starting from a list of 110 KEGG-annotated pathways. (
  • Using these three cell-type specific networks, I demonstrated that DINA can be used to make hypotheses on dysregulated pathways during disease progression. (
  • We then reduce this initial network by removing simple mediators (i.e., linear pathways), and formalize the resulting regulatory core into logical rules that govern the dynamics of each of the network components as a function of the states of its regulators. (
  • The gene expThe use of large scale public microarray data appears to be a very useful starting point for informing future experiments in order to determine gene regulatory networks.ression levels are quantised and a greedy hill climbing search method is used within a network structure learning algorithm. (
  • The information-theoretic approach was first proposed by Butte and Kohane [ 5 ], with their relevance network algorithm. (
  • REVEAL [ 5 ] is an algorithm that infers a boolean network model from gene expression data. (
  • A smooth response surface algorithm is developed as an elaborate data mining technique for analyzing gene expression data and constructing gene regulatory network. (
  • With the availability of high-throughput gene expression data in the post-genomic era, reconstruction of gene regulatory networks has become a hot topic. (
  • Extending genome wide association analysis by the inclusion of gene expression data may assist in the dissection of complex traits. (
  • We combined these results with gene expression data from five tissue types analysed with a skin-specific microarray. (
  • With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. (
  • Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. (
  • To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment, and equally well performing as existing methods. (
  • We generated large-scale spatiotemporal gene expression data for the developing mouse tooth. (
  • GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. (
  • Thus, information-theoretic approaches can deal with steady-state gene expression data or with time-series data given that the sampling interval is long enough to assume that each point is independent of the previous points. (
  • Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. (
  • But as gene-expression data rolls in from an ever-increasing number of animal phyla, new insights and new questions arise. (
  • Bacterial transcriptional gene regulatory network reconstruction from RNA-seq gene expression data? (
  • To reverse engineer a Gene regulatory network what type of data can I use instead of microarray gene expression data because microarray data can be noisy. (
  • The process of mapping experimental gene expression data of Meis, Hoxa11 and Hoxa13 to the 2D limb bud model. (
  • This new technique is applied to functionally describe triplets of activators, repressors and targets, and their regulations in gene expression data. (
  • The predictions based on the identified triplets in two yeast gene expression data sets agree with some experimental data in the literature. (
  • The network shown was generated from microarray data without the use of any prior information, and yet the method managed to identify the strong causal relationships between clock components (TOC1, LHY, ELF3, ELF4, CCA1) and to link these to further key regulators of important processes (e.g. (
  • During Phase I the method generates K modules of similarly expressed genes and then associates each module to a few regulators. (
  • Thus, at the end of this step the method has generated K * B modules of similarly regulated genes, each of them with their associated regulators. (
  • During Phase II the proposed method generates, for each module, a bipartite graph that links the individual target genes to their associated regulators. (
  • Note that if no combination of regulators represents accurately the expression profile of a given target gene, that gene is removed from the graph. (
  • The core gene regulatory cascade that drives endoderm development, extending from early maternal regulators to terminal differentiation genes, is characterized by activation of successive tiers of transcription factors, including a sequential cascade of redundant GATA transcription factors. (
  • The existence of each tier in the regulatory hierarchy is justified by the assignment of a unique task and each invariably performs at least two functions: to activate the regulators in the next tier and to perform one other activity distinct from that of the next tier. (
  • Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. (
  • In this article, researchers at the Plant Cell Physiology laboratory organize the 4 major regulators controlling the seed maturation in Arabidopsis, in a hierarchical network, and show that they are connected by transcriptional controls. (
  • Genes responsive to SMV infection are identified as are their potential miRNA regulators. (
  • The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. (
  • The main problem, however, is the neglect of the fact that orthologous regulators and target genes not necessarily are involved in conserved regulations. (
  • GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. (
  • Genes regulate each other as part of a complex system, of which it is vitally important to gain an understanding. (
  • Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. (
  • We propose to use measurements of constitutive gene expression in several panels of inbred mice combined with statistical analyses of transcription factor activity to construct robust networks that regulate gene expression in the liver. (
  • The genes necessary for curli production are clustered in the csgBA and csgDEFG operons, which encode the curli subunits and regulate their transcription and transport, respectively. (
  • Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. (
  • These results show how the spatially restricted and balanced effects of specific components of a signaling network can regulate stem cell proliferation in the niche and account for asymmetric organogenesis. (
  • Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. (
  • Importantly, the yeast cell can rapidly respond to changes in its nutritional environment by altering the expression of specific genes that allow it to make use of those different energy sources. (
  • 800 transcripts that exhibit the same regulatory patterns as a number of endoderm-specific genes, are contributing to elucidation of the complete endoderm gene regulatory network in C. elegans. (
  • We further developed the tools to investigate the function of specific genes in supporting the development of behavioral correlates of drug addiction in mice. (
  • Each PD skeletal element expresses specific genes. (
  • Gene regulatory networks play a vital role in organismal development and function by controlling gene expression. (
  • Large sets of microarray experiments are used in this analysis, specifically 2466 NASC Arabidopsis thaliana microarrays containing gene expression levels of over twenty thousand genes in a number of experimental conditions. (
  • Two time points are found to play a significant role in the Arabidopsis gene regulatory networks. (
  • Here, we use a recent comprehensive data set of DNase I sequencing-identified cis-regulatory binding sites (footprints) at single-base-pair resolution to compare binding sites and network connectivity in duplicated gene pairs in Arabidopsis ( Arabidopsis thaliana ). (
  • The experimental results on real as well as in silico datasets including time-series RTX therapy, Arabidopsis thaliana , DREAM-3, and DREAM-8 datasets, in comparison with existing state-of-the-art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self-regulatory interactions. (
  • Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. (
  • and activation of some downstream differentiation genes. (
  • knowledge- based approach for network prediction, which is to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes. (
  • Understanding how they work together to form a cellular network in a living organism is extremely important in the field of molecular biology. (
  • Importantly, changes in cellular levels of NAD, a close relative of NADP, had previously been linked to a gene circuit that controls aging and longevity in a large number of different organisms, including yeast but also including animals," said Professor Rolf Sternglanz of Stony Brook University in New York, a co-author of the study. (
  • Cancer cells are immature cells resulting from cellular reprogramming by gene misregulation, and redifferentiation is expected to reduce malignancy. (
  • Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. (
  • Gene duplication events during evolutionary history have provided large numbers of new genes that can diverge in function and gain new functions, resulting in new morphological, physiological, and biochemical characteristics of organisms and cellular systems. (
  • Moreover, time series data only gives the expression levels of genes without any knowledge of other cellular elements like protein/metabolite concentrations. (
  • Recent experimental evidence suggests that these networks are not fixed but rather change their topology as cells develop. (
  • Identification of Boolean Networks Using Premined Network Topology Information. (
  • These authors observed that the performance of the evaluated methods depends on many factors such as features of the data, network size and topology, as well as parameter settings. (
  • It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. (
  • Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. (
  • Activation of a metabolic gene regulatory network downstream of mTOR complex 1. (
  • We show here that the GRN structure upstream and downstream of the conserved network kernel has, by contrast, diverged extensively. (
  • These genes form components of an implied cardiac gene regulatory network, in which TFs drive downstream effectors to maintain normal electrical conduction. (
  • In multicellular eukaryotes CRMs may be located not only in the upstream vicinity of transcription start sites of the dependent genes but also at tens of thousands nucleotides upstream or downstream from the transcription start sites. (
  • Our approach identifies the most parsimonious gene regulatory network that can correctly pattern the PD markers downstream of FGF and RA. (
  • An evolutionary change in a morphological feature or features must depend on a reorganization or co-option of one or more developmental gene regulatory network just as retention of an ancestral morphological trait must rely on retention of a common gene regulatory network. (
  • Lastly, we will combine this data with transcription factor activity from a variety of sources and use statistical models to construct liver gene expression networks. (
  • Lastly, we will combine these gene expression networks with transcription factor activity into a statistical model which will produce the most likely candidate networks for inclusion in future predictive models of liver toxicity and serve as primary candidate networks for in vitro follow-up experiments. (
  • To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. (
  • Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. (
  • Here we examine the transcription-factor-(TF)-dependence of ncRNA expression to define enhancers and enhancer-associated ncRNAs that are involved in a TF-dependent regulatory network. (
  • Integrative analyses of gene expression with chromatin and transcription factor binding data demonstrated that RUNX1-EVI1 binding caused the re-distribution of endogenous RUNX1 within the genome and interfered with both RUNX1 and EVI1 regulated gene expression programs. (
  • A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. (
  • Detail investigation of CRM sequences exhibit that transcription factor binding sites (TFBS) form complex arrangements, probably corresponding to yet unknown regulatory code of gene expression. (
  • We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. (
  • This platform will realize a faster workflow for strain construction in which genes predicted to encode transcription factors are deleted or overexpressed resulting in a rigorous analysis of transcription factor-mediated gene regulatory networks underlying the physiology of extremophilic archaea living at the limits of life. (
  • They are essentially hardwired genomic regulatory codes, the role of which is to specify the sets of genes that must be expressed in specific spatial and temporal patterns. (
  • This suggested to us that defined experiences may be encoded by unique patterns of gene expression in relevant brain regions. (
  • Such patterns can provide an understanding of the regulatory mechanisms in the cells. (
  • Genes repressing or being downregulated during cancer development/progression show varied expression patterns in Xenopus embryos. (
  • Following duplication, genes can exhibit divergence in their coding sequence and their expression patterns. (
  • Changes in the cis-regulatory element landscape can result in changes in gene expression patterns. (
  • This different biochemical and phenotypical behavior reflects different patterns of gene expression compared with planktonic cells ( 39 , 41 ). (
  • 9. Perry MW, Boettiger AN, Levine M. Multiple enhancers ensure precision of gap gene-expression patterns in the Drosophila embryo. (
  • The chamber GRN subsequently decayed with individual genes exhibiting decay patterns unrelated to known patterning boundaries. (
  • We also found that the regulatory core yields an epigenetic landscape that restricts temporal patterns of progression between the steady states, such that recovered patterns resemble the time-ordered transitions observed during the spontaneous immortalization of epithelial cells, both in vivo and in vitro. (
  • Our study strongly suggests that the in vitro tumorigenic transformation of epithelial cells, which strongly correlates with the patterns observed during the pathological progression of epithelial carcinogenesis in vivo, emerges from underlying regulatory networks involved in epithelial trans-differentiation during development. (
  • The general regulatory model uses the RA and FGF morphogen gradients as inputs and should explain the expression patterns of the PD markers as outputs: Meis, Hoxa11 and Hoxa13 over time and space. (
  • Promoter sequence analysis of differentially expressed genes allowed us to reverse-engineer a regulatory network. (
  • Further, we report a number of differentially expressed genes in regions containing highly associated SNP including ATRN, DOCK7, FGFR1OP, GLI3, SILV and TBX15. (
  • Divided into five convenient sections, Gene Regulatory Networks: Methods and Protocols details how each of these approaches contributes to a more thorough understanding of the composition and function of gene regulatory networks, while providing a comprehensive protocol on how to implement them in the laboratory. (
  • For example, numerical and analytic methods have been used to investigate stochastic gene induction and repressor action ( 5 - 7 ), and analytic results have been obtained for the stochastic expression of a single gene in eukaryotes ( 8 ) and in a growing cell population ( 9 ). (
  • Authoritative and accessible, Gene Regulatory Networks: Methods and Protocols aims to provide novices and experienced researchers alike with a comprehensive and timely toolkit to study gene regulatory networks from the point of data generation to processing, visualization, and modeling. (
  • Optimal Intervention Methods for Markovian Gene Regulatory Networks. (
  • Gene info dataframe: As information about transcription factors is required for these methods, we will use a dataframe containing a boolean variable with this information. (
  • In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. (
  • their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. (
  • For this reason, methods that simplify and aid exploration of complex networks are necessary. (
  • It also considers approximate validation methods based on data for which the generating network is not known, the kind of situation one faces when using real data. (
  • High-throughput methods developed recently can identify potential cis-regulatory elements on a genome-wide scale. (
  • Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. (
  • By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. (
  • Hello everyone I was wondering, Which are the current methods to identify trans-regulatory modul. (
  • The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies. (
  • The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. (
  • These approaches have been implemented in-vivo for the investigation of the role of defined gene products within specific neuronal types in regions of the reward circuitry in supporting the development of addiction correlates in mice. (
  • Here, we review primarily bioinformatic approaches to capture this regulatory complexity and look at different algorithmic strategies and associated available software solutions to identify composite cis-elements. (
  • Different Systems Biology approaches have been proposed to reconstruct the transcriptional, post-transcriptional and the post-translational regulatory networks of a cell starting from genomics data. (
  • Chapter 2 illustrates a comparative study of the different approaches to reverse-engineering gene networks from gene expression profiles (GEPs) and their limitations. (
  • Current state-of-the-art reverse-engineering approaches model gene networks as static processes, i.e. regulatory interactions among genes in the network (such as direct physical interactions or indirect functional interactions) do not change across different conditions or tissue types. (
  • Genome-Wide Investigation Using sRNA-Seq, Degradome-Seq and Transcriptome-Seq Reveals Regulatory Networks of microRNAs and Their Target Genes in Soybean during Soybean mosaic virus Infection. (
  • In this study, we performed small RNA (sRNA)-seq, degradome-seq and as well as a genome-wide transcriptome analysis to profile the global gene and miRNA expression in soybean following infections by three different Soybean mosaic virus (SMV) isolates, L (G2 strain), LRB (G2 strain) and G7 (G7 strain). (
  • Genome-wide transcriptome analysis showed that total 2679 genes are differentially expressed in response to SMV infection including 71 genes predicted as involved in defense response. (
  • Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing. (
  • A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module. (
  • We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. (
  • By binding to the promoter region at the start of other genes they turn them on, initiating the production of another protein, and so on. (
  • It "docks" to a protein called Gal80p, which acts along with a gene regulating-protein called Gal4p, to adapt the metabolism of the yeast cell so that it can make use of galactose. (
  • It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. (
  • At the intersection of both networks, we identified thirteen genes with insulin-like growth factor binding protein 7 (IGFBP7), platelet-derived growth factor alpha (PDGFRA) and the tetraspanin platelet activator CD9 at the kernel of the intersection. (
  • This is achieved through the activation of a transcriptional program affecting metabolic gene targets of hypoxia-inducible factor (HIF1alpha) and sterol regulatory element-binding protein (SREBP1 and SREBP2). (
  • One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including aspects such as genome annotation, identification of cis -regulatory elements and transcription factors, information on protein-DNA and protein-protein interactions, and data mining and integration. (
  • Information about the structural targets of the protein evolution in the GRN may predict switching points in gene networks functioning in course of evolution. (
  • One aspect of systems biology is understanding the dynamics of protein-DNA interactions affecting gene expression that are caused by transcription factors (TFs) and chromatin remodeling factors. (
  • Gene expression is controlled at multiple layers, and cells may integrate different regulatory steps for coherent production of proper protein levels. (
  • We show that epithelial stem cell proliferation in the cervical loops is controlled by an integrated gene regulatory network consisting of Activin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Follistatin within the incisor stem cell niche. (
  • Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. (
  • Specifically, we aimed to understand how these gene expression networks are reorganized following chronic drug exposure, assuming that this knowledge will provide insight into mechanisms underlying the development of addiction. (
  • Biofilm formation and the presence of intrinsic resistance-associated genes are examples of the mechanisms that P. aeruginosa employs to resist chemotherapy. (
  • In addition, this bacterium can become resistant to a broad range of antibiotics through the acquisition of new resistance mechanisms via horizontal gene transfer. (
  • Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. (
  • We use this method to predict the regulatory mechanisms that underlie proximodistal (PD) patterning of the developing limb bud. (
  • This scenario arises when the target gene in question was an outlier in the corresponding module. (
  • Tbx5 -dependent ncRNA transcription provided a quantitative metric of Tbx5 -dependent enhancer activity, correlating with target gene expression. (
  • Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. (
  • Semi-supervised algorithms often exploit positive-unlabeled (PU) learning techniques by taking a small sample of positive examples and a large number of unlabeled examples to train a classification model and use the trained model to predict a network. (
  • These algorithms were implemented on different sizes of data generated by synthetic networks. (
  • They streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. (
  • Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. (
  • A, whole mount in situ hybridization (WMISH) detection of expression of genes coding for chromatin modification enzymes. (
  • Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. (
  • The book also presents an overview of exploring gene regulatory networks, chromatin, and miRNAs. (
  • The second part of my thesis is about the development of GNRevealer, a method for reverse engineering of gene regulatory networks from temporal data. (
  • Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. (
  • We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. (
  • We introduce the problem of learning new gene-gene interactions from positive and unlabeled data and propose a roadmap of possible approches. (
  • Then the gene regulatory networks constructed based on complete and incomplete data series are compared in terms of statistics at different levels. (
  • The recent high-throughput genomic technologies are able to measure the gene expression values and have provided large-scale data sets, which can be used to obtain insights into how the gene networks are organized and operated. (
  • Using existing data from two separate panels of inbred mice, we will perform quantitative trait locus (QTL) mapping on the gene expression measurements. (
  • By extracting the reproducible QTLs across the two data sets, we will find networks of correlated genes which are likely to share common regulatory control. (
  • Depletion of TRIM28 in human or mouse ES cells triggers the upregulation of a broad range of transposable elements(4,10,11), and recent data based on a few specific examples have pointed to an arms race between hosts and transposable elements as an important driver of KZFP gene selection(5). (
  • On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae , we constructed gene regulatory networks using a two-stage penalized least squares method. (
  • Additionally, regulatory changes of the miRNAs themselves are described and the regulatory relationships were supported with degradome data. (
  • A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA. (
  • how to get genes and their interactions from RNA-Seq data? (
  • I am trying to construct a Gene Regulatory Network from RNA-sequencing data. (
  • To this end, we first integrate published functional and well-curated molecular data of the components and interactions that have been found to be involved in such cell states and transitions into a network of 41 molecular components. (
  • We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. (
  • We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. (
  • Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. (
  • For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same ON and OFF gene expression profiles across multiple samples of experimental data. (
  • These rich data sets, all acquired under a standardized condition, reveal a substantial coordination between regulatory layers and provide a basis for a systems-level understanding of multilayered gene-expression programs. (
  • In this work, researchers from the Shanghai Jiao Tong University , China compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. (
  • Logical models such as Boolean networks and Petri nets could represent the network structure but are unable to describe dynamic processes. (
  • Boolean networks use a binary variable to represent the state of a gene activity and a directed graph, where edges are represented by boolean functions, to represent the interactions between genes. (
  • The control system that determines how development of an animal occurs in each species is encoded in the genome, and the physical location of the sequences where this code is resident is being revealed in a new area of systems biology--the study of gene regulatory networks," says Davidson. (
  • The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. (
  • Reverse engineering of gene regulatory networks remains a major issue and area of interest in the field of bioinformatics and systems biology. (
  • Inferring gene regulatory networks is an important problem in systems biology. (
  • GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. (
  • Jason T. L. Wang (2015) Inferring Gene Regulatory Networks: Challenges and opportunities . (
  • Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of time samples and requirements to estimate a large number of parameters. (
  • Inferring Gene Regulatory Networks from a Population of Yeast Segregan" by Chen Chen, Dabao Zhang et al. (
  • We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. (
  • Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. (
  • We also propose a list of candidate genes responsible for the development of trichomes in a wide range of plant species. (
  • B) Sub-circuits of pancreas gene regulatory network representing canonical network motifs. (
  • Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. (
  • Here I describe the repeated use of specific network architectures in the sea urchin developmental gene regulatory networks, and illustrate how they contribute to robust cell fate decision. (
  • The course covers structure and function of genomically encoded gene networks controlling many developmental processes, in vertebrate, Drosophila, and sea urchin model systems. (
  • By applying this theoretical framework to the free swimming, feeding larval stage of the purple sea urchin, it is possible to delineate the conserved regulatory circuitry that regulates the gut-associated immune response. (
  • This larval model provides a means to experimentally characterize immune function encoded in the sea urchin genome and the regulatory interconnections that control immune response and resolution across the tissues of the organism. (
  • Regulatory circuitry in sea urchin development probed using Morpholino oligos for gene knockdown. (
  • In this investigation I have identified key genes of the gene regulatory network (GRN) found in embryonic endo-mesoderm development in the sea urchin, responsible for embryonic skeletogenesis, and compared these key genes with homologues in the brittle star. (
  • A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo. (
  • We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. (
  • There, Davidson took an interest in development of marine invertebrates, especially of the purple sea urchin Strongylocentrotus purpuratus, and in investigating the function of genomic repetitive DNA elements, both interests of which would lead to a long line of investigation that eventually led to his contemporary interest in gene regulatory networks. (
  • Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. (
  • Here, to obtain a global view of this phenomenon, we combined phylogenetic and genomic studies to investigate the evolutionary emergence of KZFP genes in vertebrates and to identify their targets in the human genome. (
  • Gene regulatory networks explicitly represent the causality of developmental processes. (
  • The regulatory genome as a logic processing system: Every regulatory module contained in the genome receives multiple disparate inputs and processes them in ways that can be mathematically represented as combinations of logic functions (e.g., "and" functions, "switch" functions, "or" functions). (
  • A GRN is a complex set of highly interconnected processes that govern the rate at which different genes in a cell are expressed in time, space, and amplitude. (
  • This finding indicates that the genes involved in these patterning processes could be components of a conserved gene regulatory network (GRN) - a set of genes that operate together in a predicted pattern of activation or repression to control a particular process within an organism - and that this GRN is deployed regardless of developmental mode. (
  • Changes in the way in which these networks are deployed might therefore provide phylogenetic signals that could aid our understanding of evolutionary processes. (
  • Identification of CRMs in silico and prediction of their regulatory function allows one to suggest new regulatory inputs controlling expression of particular genes, which makes a useful introductory step before modeling of cell signaling processes. (
  • The network specifies genomically encoded regulatory processes between early cleavage and gastrula stages. (
  • Gene Regulatory Networks for Development is an advanced short course that conveys the central conceptual focus of this field, which lies at the conceptual nexus of development, evolution and functional regulatory genomics, to be given at MBL, October 12-24, 2014. (
  • We are looking for a postdoctoral scientist with expertise in genomics, epigenetics and next-gene. (
  • Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. (
  • DINA is based on the hypothesis that genes belonging to a condition-specific pathway are actively co-regulated only when the pathway is active, independently of their absolute level of expression. (
  • E.H. Davidson, Genomic Regulatory Systems. (
  • Shortly before his death from a heart attack in 2015, Davidson co-authored a landmark review book providing a grand synthesis of the theory and experimental evidence relating to the design and function of genomic regulatory networks within the animal taxonomic clade of Bilateria. (
  • Gene Activity in Early Development (1987) ISBN 0-12-205161-0 Genomic Regulatory Systems: Development and Evolution (2001) ISBN 0-12-205351-6 Davidson, E.H. (
  • Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. (
  • Expression Pattern Analysis of Regulatory Transcription Factors in Caenorhabditis elegans Huiyun Feng, Hannah L. Craig, and Ian A. Hope 3. (
  • Emerging Research in the Analysis and Modeling of Gene Regulatory Networks, edited by Ivan V. Ivanov, et al. (
  • Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. (
  • We illustrate the practical interest of our approach through the numerical analysis of three well-known networks developed in the field of synthetic biology. (
  • The Analysis of Regulatory DNA provides readers with both the necessary background knowledge and provocative, up-to-date insights aimed at sparking new and vibrant experimental designs for understanding and predicting cis-elements in the eukaryotic genome. (
  • Molecular understanding of cardiac rhythm control requires analysis of the essential TF-driven enhancers that modulate the expression of channel genes, and these regulatory elements are not yet described. (
  • Transcriptional analysis by fluorescence expression profiling and quantitative PCR revealed a regulatory network controlled by six transcriptional repressors. (
  • It adopts multivariate co-variances using principal component analysis (PCA) to predict an asymmetric and non-diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. (
  • Therefore, its architecture is testable by cis-regulatory analysis. (
  • It is most important to determine regulatory network architecture, and this can be done by experimental perturbation followed by measurement of the effects on function of many individual genes. (
  • But gene regulatory network architecture can be authenticated only by experimental molecular biology in which the functional meaning of given regulatory sequences is directly determined. (
  • In this project, we aimed to define the organization and the logic of the gene expression networks that underlie the development of drug addiction using mice as an experimental model. (
  • Using the experimental setups in the lab, we have developed a comprehensive characterization of the gene expression programs induced by cocaine experience in the brain. (
  • The proposed approach aims at computing the differences in variances of co-expressed genes rather than computing differences in values of mean expressions across experimental conditions. (
  • A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts. (
  • The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. (
  • Regulatory neofunctionalization and subfunctionalization are likely caused by mutational changes within the cis-regulatory region of the duplicated genes, which alter the temporal/spatial expression profile as well as responses to various biotic and abiotic stimuli. (
  • Each of the temporal-spatial phases of specification is represented in a subelement of the network model, that treats regulatory events within the relevant embryonic nuclei at particular stages. (
  • Development of high-throughput, next-generation sequencing and other advanced technologies, a large number of gene expression profiles have been produced. (
  • He and Levine also coauthored a perspective in the same issue of the journal on the properties of gene regulatory networks. (
  • The csgD gene encodes the transcription regulator CsgD, which in turn activates transcription of the csgBA operon encoding curli, extracellular structures involved in bacterial adhesion. (
  • Horizontal gene transfer (HGT) is a major force driving bacterial evolution. (
  • Hello, Quick question: I have a bunch of differentially regulated genes from bacterial RNA-seq e. (
  • I first reverse-engineered 30 tissue-specific networks from a collection of about 3000 GEPs. (
  • Secondly, we use reverse‐engineering to test how different gene regulatory networks can interpret the opposing gradients of fibroblast growth factors (FGF) and retinoic acid (RA) to pattern the PD markers. (
  • A dynamical 2D computer model of limb development, combining tissue movements and spatially controlled gene regulatory interactions, allows reverse‐engineering the regulatory network controlling cell fate decisions along the main proximodistal (PD) axis. (
  • Reverse‐engineering is used to test how different gene regulatory networks can interpret the opposing gradient of FGF and RA to pattern the PD markers. (
  • The papers in the collection focus on the gene regulatory networks of a variety of organisms, including fruit flies, soil-dwelling nematodes, sea urchins, lampreys, and mice. (
  • In single-celled organisms, regulatory networks respond to the external environment, optimising the cell at a given time for survival in this environment. (
  • Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation. (
  • Even for prokaryotic model organisms, such as Escherichia coli or Corynebacterium glutamicum the monumental task of deciphering transcriptional regulatory networks for whole species is far from being complete. (
  • Dynamical information about network components and their interactions is critical to predictive modeling of gene regulatory networks, and is currently not accessible through omics and other end point techniques. (
  • The introduction of regulatory interactions creates a gene network with complex emergent properties ( 10 ). (
  • This gives rise to highly complex and cell-type specific, interwoven structures of regulatory interactions summarized in gene regulatory networks. (
  • Gene regulatory networks are the complex networks of gene interactions that direct the development of any given species. (
  • The modern science of networks has brought significant advances to our understanding of complex systems. (
  • Common sequence variants in cis-regulatory elements (CREs) are suspected etiological causes of complex disorders. (
  • These studies show how the effects of functionally independent non-coding variants in a coordinated gene regulatory network amplify their individually small effects, providing a model for complex disorders. (
  • In addition to GL2, the MBW complex induces the expression of repressor genes (TRY/CPC), which can move between the cells and assemble into a complex (GL3/EGL3-CPC/TRY-TTG1) that is unable to initiate trichome formation. (
  • Intervality and coherence in complex networks. (
  • On the origins of hierarchy in complex networks. (
  • The footprints, in turn, result in more regulatory network connections between WGD s and other genes, forming denser, more complex regulatory networks than shown by TD s. (
  • 8. Dunipace L, Ozdemir A, Stathopoulos A. Complex interactions between cis-regulatory modules in native conformation are critical for Drosophila snail expression. (
  • Although live cell imaging has not been often used in the study of gene regulatory networks, it is a method that uniquely enables the measurement of dynamic events in single cells. (
  • In order to evaluate the impact of time point measurement on network reconstruction, we deleted time points one by one to yield 11 distinct groups of incomplete time series. (
  • In addition, important regulations between genes, which were insensitive to the time point measurement, were also identified. (
  • Yan W, Zhu H, Yang Y, Chen J, Zhang Y, Shen B. Effects of Time Point Measurement on the Reconstruction of Gene Regulatory Networks. (
  • The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. (
  • The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. (
  • Thus, the direct connectivity of this network is highly reliable and can provide a systems level view of how network architecture contributes to the precise control of embryonic axes formation and germ layer specification. (
  • Similarity in gene-regulatory networks suggests that cancer cells share characteristics of embryonic neural cells. (
  • The regulatory effect of these enzymes in neuronal differentiation resided in their intrinsic activity in embryonic neural precursor/progenitor cells. (
  • This correlation indicated that cancer cells and embryonic neural cells share a regulatory network, mediating both tumorigenesis and neural development. (