Gene Regulatory Networks: 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.Models, Genetic: 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.Gene Expression Profiling: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.Computational Biology: 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.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Systems Biology: 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.Transcription Factors: Endogenous substances, usually proteins, which are effective in the initiation, stimulation, or termination of the genetic transcription process.Gene Expression Regulation, Developmental: 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 Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Models, Biological: 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.Oligonucleotide Array Sequence Analysis: 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.Sea Urchins: Somewhat flattened, globular echinoderms, having thin, brittle shells of calcareous plates. They are useful models for studying FERTILIZATION and EMBRYO DEVELOPMENT.Gene Expression Regulation: 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.Strongylocentrotus purpuratus: 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.Signal Transduction: 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.Neural Networks (Computer): 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.Bayes Theorem: 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, Genetic: Databases devoted to knowledge about specific genes and gene products.Echinodermata: 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)Embryo, Nonmammalian: The developmental entity of a fertilized egg (ZYGOTE) in animal species other than MAMMALS. For chickens, use CHICK EMBRYO.Metabolic Networks and Pathways: Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites.Stochastic Processes: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.Software: Sequential operating programs and data which instruct the functioning of a digital computer.Evolution, Molecular: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.Genomics: The systematic study of the complete DNA sequences (GENOME) of organisms.MicroRNAs: 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.Transcriptome: The pattern of GENE EXPRESSION at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.Cell Physiological Phenomena: Cellular processes, properties, and characteristics.Nucleotide Motifs: Commonly observed BASE SEQUENCE or nucleotide structural components which can be represented by a CONSENSUS SEQUENCE or a SEQUENCE LOGO.Endoderm: The inner of the three germ layers of an embryo.Transcription, Genetic: The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION.Cluster Analysis: 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.Feedback, Physiological: 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.Gene Expression Regulation, Bacterial: Any of the processes by which cytoplasmic or intercellular factors influence the differential control of gene action in bacteria.Models, Statistical: 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.Homeodomain Proteins: 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).Gene Expression Regulation, Plant: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in plants.Regulatory Elements, Transcriptional: Nucleotide sequences of a gene that are involved in the regulation of GENETIC TRANSCRIPTION.Saccharomyces cerevisiae: 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.Protein Interaction Mapping: Methods for determining interaction between PROTEINS.Proteome: The protein complement of an organism coded for by its genome.Arabidopsis: 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.Body Patterning: 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.Mesoderm: The middle germ layer of an embryo derived from three paired mesenchymal aggregates along the neural tube.Cell Differentiation: Progressive restriction of the developmental potential and increasing specialization of function that leads to the formation of specialized cells, tissues, and organs.Genes, Regulator: Genes which regulate or circumscribe the activity of other genes; specifically, genes which code for PROTEINS or RNAs which have GENE EXPRESSION REGULATION functions.Genome: The genetic complement of an organism, including all of its GENES, as represented in its DNA, or in some cases, its RNA.Immune System Phenomena: The characteristic properties and processes involved in IMMUNITY and an organism's immune response.Markov Chains: 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.Binding Sites: The parts of a macromolecule that directly participate in its specific combination with another molecule.Chromatin Immunoprecipitation: 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.Ectoderm: The outer of the three germ layers of an embryo.Biological Evolution: The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics.Gastrulation: 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.Cell Lineage: The developmental history of specific differentiated cell types as traced back to the original STEM CELLS in the embryo.Promoter Regions, Genetic: 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.Gene Expression Regulation, Fungal: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in fungi.Nonlinear Dynamics: 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.Escherichia coli: 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.Reproducibility of Results: 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.Ciona intestinalis: The only species of a cosmopolitan ascidian.Position-Specific Scoring Matrices: 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.Vertebrates: Animals having a vertebral column, members of the phylum Chordata, subphylum Craniata comprising mammals, birds, reptiles, amphibians, and fishes.Skeleton: 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.Blastula: 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.Phenotype: The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.Molecular Sequence Data: 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.Starfish: Echinoderms having bodies of usually five radially disposed arms coalescing at the center.Embryonic Stem Cells: 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.Regulon: 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.Base Sequence: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.Mutation: 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.Models, Theoretical: 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.Work Simplification: The construction or arrangement of a task so that it may be done with the greatest possible efficiency.Lytechinus: A genus of SEA URCHINS in the family Toxopneustidae possessing trigeminate ambulacral plating.Genes, Plant: The functional hereditary units of PLANTS.Epistasis, Genetic: 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.Protein Interaction Maps: Graphs representing sets of measurable, non-covalent physical contacts with specific PROTEINS in living organisms or in cells.Caenorhabditis elegans: A species of nematode that is widely used in biological, biochemical, and genetic studies.Drosophila: 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.Neural Crest: 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.Arabidopsis Proteins: 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.Phylogeny: The relationships of groups of organisms as reflected by their genetic makeup.User-Computer Interface: The portion of an interactive computer program that issues messages to and receives commands from a user.Embryonic Development: Morphological and physiological development of EMBRYOS.Artificial Intelligence: 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.Conserved Sequence: 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, Messenger: 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.Data Mining: Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.Internet: 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.Drosophila melanogaster: A species of fruit fly much used in genetics because of the large size of its chromosomes.Drosophila Proteins: 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.In Situ Hybridization: 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.SOXB1 Transcription Factors: 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.Uncertainty: The condition in which reasonable knowledge regarding risks, benefits, or the future is not available.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Bacterial Proteins: Proteins found in any species of bacterium.Basic Helix-Loop-Helix Transcription Factors: A family of DNA-binding transcription factors that contain a basic HELIX-LOOP-HELIX MOTIF.Saccharomyces cerevisiae Proteins: 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.Corynebacterium glutamicum: A species of gram-positive, asporogenous, non-pathogenic, soil bacteria that produces GLUTAMIC ACID.Wnt Signaling Pathway: 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.Flowers: The reproductive organs of plants.Repressor Proteins: 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.Nerve Net: 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.DNA-Binding Proteins: 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.Protein Binding: 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.Organogenesis: Formation of differentiated cells and complicated tissue organization to provide specialized functions.Genetic Engineering: 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.Morphogenesis: 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.Species Specificity: 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.Microarray Analysis: The simultaneous analysis, on a microchip, of multiple samples or targets arranged in an array format.Feedback: 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.Multigene Family: 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)Zebrafish: 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.Cell Cycle: 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.Nodal Protein: 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.Sequence Analysis, RNA: A multistage process that includes cloning, physical mapping, subcloning, sequencing, and information analysis of an RNA SEQUENCE.Genes, Insect: The functional hereditary units of INSECTS.Regulatory Sequences, Nucleic Acid: Nucleic acid sequences involved in regulating the expression of genes.Octamer Transcription Factor-3: An octamer transcription factor that is expressed primarily in totipotent embryonic STEM CELLS and GERM CELLS and is down-regulated during CELL DIFFERENTIATION.Gene Expression: The phenotypic manifestation of a gene or genes by the processes of GENETIC TRANSCRIPTION and GENETIC TRANSLATION.Stress, Physiological: 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.Sequence Analysis, DNA: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Biological Clocks: The physiological mechanisms that govern the rhythmic occurrence of certain biochemical, physiological, and behavioral phenomena.Enhancer Elements, Genetic: Cis-acting DNA sequences which can increase transcription of genes. Enhancers can usually function in either orientation and at various distances from a promoter.Plant Roots: 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)T-Box Domain Proteins: 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.Probability: The study of chance processes or the relative frequency characterizing a chance process.Mammals: Warm-blooded vertebrate animals belonging to the class Mammalia, including all that possess hair and suckle their young.Pluripotent Stem Cells: Cells that can give rise to cells of the three different GERM LAYERS.Two-Hybrid System Techniques: 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.Caenorhabditis elegans Proteins: 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.RNA Interference: 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.Reverse Transcriptase Polymerase Chain Reaction: 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.Genome, Bacterial: The genetic complement of a BACTERIA as represented in its DNA.Paired Box Transcription Factors: 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.Trans-Activators: Diffusible gene products that act on homologous or heterologous molecules of viral or cellular DNA to regulate the expression of proteins.Gene Knockdown Techniques: 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: 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.Proteins: 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.Adaptation, Biological: Changes in biological features that help an organism cope with its ENVIRONMENT. These changes include physiological (ADAPTATION, PHYSIOLOGICAL), phenotypic and genetic changes.Pattern Recognition, Automated: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)Transcriptional Activation: Processes that stimulate the GENETIC TRANSCRIPTION of a gene or set of genes.Computer Communication Networks: 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)Zebrafish Proteins: Proteins obtained from the ZEBRAFISH. Many of the proteins in this species have been the subject of studies involving basic embryological development (EMBRYOLOGY).Sequence Alignment: 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, Genetically Modified: ANIMALS whose GENOME has been altered by GENETIC ENGINEERING, or their offspring.Embryo, Mammalian: 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.Monte Carlo Method: 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)Quantitative Trait Loci: Genetic loci associated with a QUANTITATIVE TRAIT.Gene Expression Regulation, Neoplastic: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.Embryonic Induction: 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).Genome, Fungal: The complete gene complement contained in a set of chromosomes in a fungus.Plant Proteins: Proteins found in plants (flowers, herbs, shrubs, trees, etc.). The concept does not include proteins found in vegetables for which VEGETABLE PROTEINS is available.DNA: 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).Computer Graphics: 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.Plants: 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.Community Networks: Organizations and individuals cooperating together toward a common goal at the local or grassroots level.Escherichia coli K12: 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.Amino Acid Sequence: 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.Stem Cells: Relatively undifferentiated cells that retain the ability to divide and proliferate throughout postnatal life to provide progenitor cells that can differentiate into specialized cells.Gene Ontology: 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.Chromosome Mapping: Any method used for determining the location of and relative distances between genes on a chromosome.Yeasts: 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.Synthetic Biology: A field of biological research combining engineering in the formulation, design, and building (synthesis) of novel biological structures, functions, and systems.

*  Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network | Molecular...

Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs ... We present the first gene regulatory network (GRN) that pertains to post‐developmental gene expression. Specifically, we mapped ... interactions between genes and their transcriptional regulators can be graphically represented in gene regulatory network (GRN ... Maduro MF, Rothman JH (2002) Making worm guts: the gene regulatory network of the Caenorhabditis elegans endoderm. Dev Biol 246 ...

*  From gene expression to gene regulatory networks in Arabidopsis thaliana | BMC Systems Biology | Full Text

Bayesian networks for learning GRNs. The gene regulatory network is modelled with a discrete static Bayesian network (for an ... A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied ... Learned regulatory network for other networks and poorly-characterized genes. The learned network structure starting from a set ... Learned regulatory network from an unselected list of 15,000+ genes. The learned network structure starting from a set of nine ...

*  Sandwalk: June 2009

Specific wiring of gene-regulatory networks is likely to underlie much of the phenotypic difference between species, but the ... Johnson, R., Samuel, J., Ng, C.K., Jauch, R., Stanton, L.W., and Wood, I.C. (2009) Evolution of the Vertebrate Gene Regulatory ... He thinks that real evolution takes place when alterations of regulatory genes result in major new phenotypes. Thus, the best ... refinement of their sequence and location consolidates this remodeling of networks governing neural gene regulation.. Correct ...

*  Statistical modeling of sequence and gene expression data to infer gene regulatory networks :: University of Southern...

I try to infer the gene transcription regulatory networks by first detecting the downstream or regulated genes in the network, ... Statistical modeling of sequence and gene expression data to infer gene regulatory networks ... Thus, we propose another analysis method which utilizes an EM algorithm to predict the gene regulatory network from the gene ... STATISTICAL MODELING OF SEQUENCE AND GENE EXPRESSION DATA TO INFER GENE REGULATORY NETWORKS by Xiting Yan A Dissertation ...

*  GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks [PeerJ]

A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which ... GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 ... Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is ... For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on ...

*  British Library EThOS: Stochastic modeling and inference of large-scale gene regulatory networks

Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. ... This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. ... Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities ... Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. ...

*  Experimental Design for Parameter Estimation of Gene Regulatory Networks

In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated ... Gene regulatory networks Is the Subject Area "Gene regulatory networks" applicable to this article? Yes. No. ...

*  Gene Regulatory Networks for Development

Gene Regulatory Networks for Development. 2014 MBL course. October 12-24, 2014. Gene Regulatory Networks for Development is an ... The course covers structure and function of genomically encoded gene networks controlling many developmental processes, in ... The network course provides an intense experience, which includes in addition to a series of lectures, discussions, and ... evolution and functional regulatory genomics, to be given at MBL, October 12-24, 2014. This is the 7th edition of this unique ...

*  Gene Regulatory Networks: Methods and Protocols, Book by Bart Deplancke (Hardcover) |

Buy the Hardcover Book Gene Regulatory Networks by Bart Deplancke at, Canada's 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: ...

*  Charting gene regulatory networks: strategies, challenges and perspectives | Biochemical Journal

Charting gene regulatory networks: strategies, challenges and perspectives. Gong-Hong WEI, De-Pei LIU, Chih-Chuan LIANG ... Charting gene regulatory networks: strategies, challenges and perspectives Message Subject (Your Name) has forwarded a page to ... One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including ... Abbreviations: ChIP-chip, chromatin immunoprecipitation-DNA microarray; FFL, feed-forward loop; GRN, gene regulatory network; ...

*  Gene regulatory network - Wikipedia

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 ...

*  KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks

... Imbeault, Michaël; Helleboid, Pierre-Yves; ... the products of a rapidly evolving gene family that has been traced back to early tetrapods(1,2). The function of most KZFPs is ... we combined phylogenetic and genomic studies to investigate the evolutionary emergence of KZFP genes in vertebrates and to ... we obtained evidence that KZFPs exploit evolutionarily conserved fragments of transposable elements as regulatory platforms ...

*  Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression - ePrints Soton

Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of ... Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of ... Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression ... Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression ...

*  NUS Postdocatoral Research Fellow in computational analysis of gene regulatory networks

We are looking for three highly motivated postdoctoral researchers in computational analysis of gene regulatory networks ( ... Job: NUS Postdocatoral Research Fellow in computational analysis of gene regulatory networks ... Machine Learning + Network Analysis: Compbio Postdoc Opportunity At Oregon State University Postdoctoral Position in ... Postdoctoral position to work on Gene Expression Regulation, Centre for Molecular Medicine Norway Background A 3-year ...

*  Gene regulatory networks

The regulatory network responsible for spatial patterning along the long embryo axis will ultimately convey a unique cell fate ... Transcription factors also regulate each other and respond to external signals, forming a regulatory network that orchestrates ... find and bind regulatory sequences on the DNA, thereby activating or repressing target genes. ... We analyze these contributions to noise in gene expression and ask how cells manage to function reliably in the presence of ...

*  Logical Modelling of Gene Regulatory Networks with GINsim. | GINsim

... framework for the modelling of regulatory networks. Relying on GINsim, a software implementing this logical formalism, we guide ... Discrete mathematical formalisms are well adapted to model large biological networks, for which detailed kinetic data are ... Home » Biblio » Logical Modelling of Gene Regulatory Networks with GINsim.. Logical Modelling of Gene Regulatory Networks with ...

*  Why and how genetic canalization evolves in gene regulatory networks - pdf descargar

Why and how genetic canalization evolves in gene regulatory networks. . Biblioteca virtual para leer y descargar libros, ... In this paper, we use a quantitative model of gene regulatory network to describe the conditions in which substantial ... Why and how genetic canalization evolves in gene regulatory networks - Descarga este documento en PDF. Documentación en PDF ... Overall, constrained networks evolve less canalization than networks in which some genes could evolve freely i.e. without ...

*  Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum - Semantic Scholar

... graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a ... This GRN reveals connectivity between key regulators and their target genes. Focusing on key regulators, this network contains ... revealing a compartmentalized network structure that may reflect network rewiring related to specific adaptation of this plant ... Interestingly, the inferred top regulators regulate genes that are significantly enriched from the same genomic regions (P , ...

*  PTO8: Modeling Stochasticity and Robustness in Gene Regulatory Networks (ISMB 2009) - the mind wobbles

For T-helper differentiation, the GR network is reasonably complex (he's using this as the example for the talk). How does the ... Abhishek Garg Looking at stochasticity in nodes and in functions within GR networks. ... Categories Meetings & ConferencesTags abhishek garg, gene regulatory networks, ismb 2009, robustness, stochasticity Leave a ... PTO8: Modeling Stochasticity and Robustness in Gene Regulatory Networks (ISMB 2009). Abhishek Garg ...

*  "Intricate gene regulatory networks of helix-loop-helix (HLH) proteins " by Ying Zhang, Mohammad Q. Hassan et al.

Here the relative contributions of multiple classes of HLH factors to the expression of bone related genes during osteoblast ... Thus, our data suggest that the integrated activities of negative and positive E-box related regulatory factors control ... None of these factors affect Runx2 gene expression. Interestingly, Snail enhances expression of osteoblast markers, while ... during osteoblast differentiation and their functional contributions to bone phenotypic gene regulation. While expression of ...

*  HHMI Scientists Search Results | Howard Hughes Medical Institute (HHMI)

Gene Expression (90) Apply Gene Expression filter *Gene Networks (36) Apply Gene Networks filter ... The Genetics of Human Regulatory Systems. Brigham and Women's Hospital Boston, MA ...

*  Table of Contents | Genetics

Population Genetics of Duplicated Disease-Defense Genes, hm1 and hm2, in Maize (Zea mays ssp. mays L.) and Its Wild Ancestor ( ... The Regulatory Regions Required for B′ Paramutation and Expression Are Located Far Upstream of the Maize b1 Transcribed ... Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping. *. Southeast Asian mitochondrial DNA analysis ... Coevolution of the S-Locus Genes SRK, SLG and SP11/SCR in Brassica oleracea and B. rapa ...

*  JoVE | Peer Reviewed Scientific Video Journal - Methods and Protocols

Neural immune gene expression variables are measured with qPCR screening, qPCR arrays, and, importantly, use of cDNA ... Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs ... exert remarkable tissue trophic and immune regulatory effects onto different host target cells in vivo. Here we describe the ... The 4T1 cell line has been engineered to constitutively express the firefly luciferase gene (luc2). When mice carrying 4T1-luc2 ...

*  The Multi-Level Action of Fatty Acids on Adiponectin Production by Fat Cells

Genes Dev. 2007;21:1443-1455. [PubMed]. 8. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes ... Clearly, such a rapid effect of fatty acids may only be related to a very late post-translational regulatory step in ... Adiponectin gene expression is controlled primarily by PPARγ and C/EBPα. Using mouse embryonic fibroblasts from C/EBPα-null ... Fatty acid-gene interactions, adipokines and obesity. Eur J Clin Nutr. 2011;65:285-297. [PubMed] ...

*  IL17RD Gene - GeneCards | I17RD Protein | I17RD Antibody

Complete information for IL17RD gene (Protein Coding), Interleukin 17 Receptor D, including: function, proteins, disorders, ... Regulatory Elements for IL17RD Gene. Enhancers for IL17RD Gene GeneHancer Identifier. Enhancer Score. Enhancer Sources. Gene- ... Interacting Proteins for IL17RD Gene. STRING Interaction Network Preview (showing 5 interactants - click image to see 13) ... No data available for DME Specific Peptides for IL17RD Gene Domains & Families for IL17RD Gene Gene Families for IL17RD Gene. ...

Biological network: A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web.Gene signature: A gene signature is a group of genes in a cell whose combined expression patternItadani H, Mizuarai S, Kotani H. Can systems biology understand pathway activation?PSI Protein Classifier: PSI Protein Classifier is a program generalizing the results of both successive and independent iterations of the PSI-BLAST program. PSI Protein Classifier determines belonging of the found by PSI-BLAST proteins to the known families.Clonal Selection Algorithm: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.List of systems biology conferences: Systems biology is a biological study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (integration instead of reduction) to study them. Particularly from year 2000 onwards, the term is used widely in the biosciences.Pituitary-specific positive transcription factor 1: POU domain, class 1, transcription factor 1 (Pit1, growth hormone factor 1), also known as POU1F1, is a transcription factor for growth hormone.Interval boundary element method: Interval boundary element method is classical boundary element method with the interval parameters.
Matrix model: == Mathematics and physics ==Cellular microarray: A cellular microarray is a laboratory tool that allows for the multiplex interrogation of living cells on the surface of a solid support. The support, sometimes called a "chip", is spotted with varying materials, such as antibodies, proteins, or lipids, which can interact with the cells, leading to their capture on specific spots.Sea urchin injury: Sea urchin injuries are caused by contact with sea urchins, and are characterized by puncture wounds inflicted by the animal's brittle, fragile spines.Physical neural network: A physical neural network is a type of artificial neural network in which an electrically adjustable resistance material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches which simulate neural networks.Hyperparameter: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.Extracellular: In cell biology, molecular biology and related fields, the word extracellular (or sometimes extracellular space) means "outside the cell". This space is usually taken to be outside the plasma membranes, and occupied by fluid.Aaglacrinus: Carboniferoushttp://strata.geology.Flux (metabolism): Flux, or metabolic flux is the rate of turnover of molecules through a metabolic pathway. Flux is regulated by the enzymes involved in a pathway.Doob decomposition theorem: In the theory of stochastic processes in discrete time, a part of the mathematical theory of probability, the Doob decomposition theorem gives a unique decomposition of every adapted and integrable stochastic process as the sum of a martingale and a predictable process (or "drift") starting at zero. The theorem was proved by and is named for Joseph L.Mac OS X Server 1.0Molecular evolution: Molecular evolution is a change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes.Ontario Genomics Institute: The Ontario Genomics Institute (OGI) is a not-for-profit organization that manages cutting-edge genomics research projects and platforms.The Ontario Genomics Institute OGI also helps scientists find paths to the marketplace for their discoveries and the products to which they lead, and it works through diverse outreach and educational activities to raise awareness and facilitate informed public dialogue about genomics and its social impacts.MicroRNA and microRNA target database: This microRNA database and microRNA targets databases is a compilation of databases and web portals and servers used for microRNAs and their targets. MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (ncRNAs) that regulate gene expression by targeting messenger RNAs.De novo transcriptome assembly: De novo transcriptome assembly is the method of creating a transcriptome without the aid of a reference genome.Eukaryotic transcription: Eukaryotic transcription is the elaborate process that eukaryotic cells use to copy genetic information stored in DNA into units of RNA replica. Gene transcription occurs in both eukaryotic and prokaryotic cells.Inverse probability weighting: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.Iroquois homeobox factor: Iroquois homeobox factors are a family of homeodomain transcription factors that play a role in many developmental processes.Squamosa promoter binding protein: The SQUAMOSA promoter binding protein-like (SBP or SPL) family of transcription factors are defined by a plant-specific DNA-binding domain. The founding member of the family was identified based on its specific in vitro binding to the promoter of the snapdragon SQUAMOSA gene.Zuotin: Z-DNA binding protein 1, also known as Zuotin, is a Saccharomyces cerevisiae yeast gene.Protein–protein interactionPlant Proteome Database: The Plant Proteome Database is a National Science Foundation-funded project to determine the biological function of each protein in plants.Sun Q, Zybailov B, Majeran W, Friso G, Olinares PD, van Wijk KJ.GAI (Arabidopsis thaliana gene)Multiple patterning: Multiple patterning (or multi-patterning) is a class of technologies for manufacturing integrated circuits (ICs), developed for photolithography to enhance the feature density. The simplest case of multiple patterning is double patterning, where a conventional lithography process is enhanced to produce double the expected number of features.Mesenchyme: Mesenchyme is a type of tissue characterized by loosely associated cells that lack polarity and are surrounded by a large extracellular matrix. Mesenchymal cells are able to develop into the tissues of the lymphatic and circulatory systems, as well as connective tissues throughout the body, such as bone and cartilage.List of sequenced eukaryotic genomesVladimir Andreevich Markov: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.DNA binding site: DNA binding sites are a type of binding site found in DNA where other molecules may bind. DNA binding sites are distinct from other binding sites in that (1) they are part of a DNA sequence (e.ChIP-exo: ChIP-exo is a chromatin immunoprecipitation based method for mapping the locations at which a protein of interest (transcription factor) binds to the genome. It is a modification of the ChIP-seq protocol, improving the resolution of binding sites from hundreds of base pairs to almost one base pair.Lineage markers: The lineage markers are characteristic molecules for cell lineages, e.g.GC box: In molecular biology, a GC box is a distinct pattern of nucleotides found in the promoter region of some eukaryotic genes upstream of the TATA box and approximately 110 bases upstream from the transcription initiation site. It has a consensus sequence GGGCGG which is position dependent and orientation independent.Nonlinear system: In physics and other sciences, a nonlinear system, in contrast to a linear system, is a system which does not satisfy the superposition principle – meaning that the output of a nonlinear system is not directly proportional to the input.List of strains of Escherichia coli: Escherichia coli is a well studied bacterium that was first identified by Theodor Escherich, after whom it was later named.Generalizability theory: Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.Retortamonas intestinalis: Retortamonas intestinalis is a species of retortamonad which is found in the gastrointestinal tract.Type XXVII collagen: Type XXVII collagen is the protein predicted to be encoded by COL27A1. It was first described by Dr.Midblastula: In developmental biology, midblastula or midblastula transition (MBT) is a stage during the blastula stage of embryonic development in which zygotic gene transcription is activated. There are three major characteristics of pre-MBT embryos.Phenotype microarray: The phenotype microarray approach is a technology for high-throughput phenotyping of cells.Coles PhillipsPisaster ochraceusHuman embryonic stem cells clinical trials: ==Human Embryonic Stem Cell Clinical Trials==Nif regulonSymmetry element: A symmetry element is a point of reference about which symmetry operations can take place. In particular, symmetry elements can be centers of inversion, axes of rotation and mirror planes.Silent mutation: Silent mutations are mutations in DNA that do not significantly alter the phenotype of the organism in which they occur. Silent mutations can occur in non-coding regions (outside of genes or within introns), or they may occur within exons.Von Neumann regular ring: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.History of research on Caenorhabditis elegans: The nematode worm Caenorhabditis elegans was first studied in the laboratory by Victor Nigon and Ellsworth Dougherty in the 1940s, but came to prominence after being adopted by Sydney Brenner in 1963 as a model organism for the study of developmental biology using genetics. In 1974, Brenner published the results of his first genetic screen, which isolated hundreds of mutants with morphological phenotypes.Drosophila embryogenesis: Drosophila embryogenesis, the process by which Drosophila (fruit fly) embryos form, is a favorite model system for geneticists and developmental biologists studying embryogenesis. The small size, short generation time, and large brood size make it ideal for genetic studies.Truncal neural crest: The truncal neural crest or trunk neural crest is a form of neural crest.Branching order of bacterial phyla (Gupta, 2001): There are several models of the Branching order of bacterial phyla, one of these was proposed in 2001 by Gupta based on conserved indels or protein, termed "protein signatures", an alternative approach to molecular phylogeny. Some problematic exceptions and conflicts are present to these conserved indels, however, they are in agreement with several groupings of classes and phyla.Immersive technologyMexican International Conference on Artificial Intelligence: MICAI (short for Mexican International Conference on Artificial Intelligence) is the name of an annual conference covering all areas of Artificial Intelligence (AI), held in Mexico. The first MICAI conference was held in 2000.Mature messenger RNA: Mature messenger RNA, often abbreviated as mature mRNA is a eukaryotic RNA transcript that has been spliced and processed and is ready for translation in the course of protein synthesis. Unlike the eukaryotic RNA immediately after transcription known as precursor messenger RNA, it consists exclusively of exons, with all introns removed.Process mining: Process mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system.Internet organizations: This is a list of Internet organizations, or organizations that play or played a key role in the evolution of the Internet by developing recommendations, standards, and technology; deploying infrastructure and services; and addressing other major issues.Indy (gene): Indy, short for I'm not dead yet, is a gene of the model organism, the fruit fly Drosophila melanogaster. Mutant versions of this gene have doubled the average life span of fruit flies in at least one set of experiments, but this result has been subject to controversy.BESS domain: In molecular biology, the BESS domain is a protein domain which has been named after the three proteins that originally defined the domain: BEAF (Boundary element associated factor 32), Suvar(3)7 and Stonewall ). The BESS domain is 40 amino acid residues long and is predicted to be composed of three alpha helices, as such it might be related to the myb/SANT HTH domain.Model risk: In finance, model risk is the risk of loss resulting from using models to make decisions, initially and frequently referring to valuing financial securities. However model risk is more and more prevalent in industries other than financial securities valuation, such as consumer credit score, real-time probability prediction of a fraudulent credit card transaction to the probability of air flight passenger being a terrorist.Temporal analysis of products: Temporal Analysis of Products (TAP), (TAP-2), (TAP-3) is an experimental technique for studyingFerric uptake regulator family: In molecular biology, the ferric uptake regulator (FUR) family of proteins includes metal ion uptake regulator proteins. These are responsible for controlling the intracellular concentration of iron in many bacteria.

(1/4873) Where are we in genomics?

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)

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

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)

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

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)

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

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)

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

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)

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

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)

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

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)

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

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)


  • 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. (
  • 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. (
  • Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. (
  • 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). (
  • The genome contains around 28,000 genes including around 2,000 transcription factor genes and many other genes encoding proteins with regulatory roles. (
  • Cells control the expression levels of their proteins primarily by transcriptional regulation, a process where special signaling proteins called transcription factors (TFs), find and bind regulatory sequences on the DNA, thereby activating or repressing target genes. (
  • However, in this pathway-focused view of biology we sometimes miss the larger picture: First, genes and proteins do not form independent pathway modules to which a distinct biological function can be assigned - but instead they collectively establish an almost genome-wide network of regulatory interactions. (
  • Intricate gene regulatory networks of helix-loop-helix (HLH) proteins " by Ying Zhang, Mohammad Q. Hassan et al. (
  • Interestingly, Snail enhances expression of osteoblast markers, while Twist1 and Twist2 factors are cross-regulated and inhibit bone specific gene expression and other HLH proteins (e.g. (
  • Zebrafish Znfl1 proteins control the expression of hoxb1b gene in the posterior neuroectoderm by acting upstream of pou5f3 and sall4 genes. (
  • MyoD, which was discovered in the laboratory of Harold M. Weintraub, belongs to a family of proteins known as myogenic regulatory factors (MRFs). (
  • Thus, common evolutionary rates could be forcing the genes for certain proteins to evolve together while preventing other genes from being co-opted unless there is a shift in evolutionary rate. (
  • Cooperative activation of muscle gene expression by MEF2 and myogenic bHLH proteins. (
  • They suggested that proteins made by specific tissues acted on these enhancers to activate sets of genes during cell differentiation. (
  • As the E-box is connected to several circadian genes, it is possible that the genes and proteins associated with it are "crucial and vulnerable points in the (circadian) system. (
  • In addition, SALL4 can also activate gene expression via the recruitment of the mixed lineage leukemia (MLL) protein, which is a homolog of Drosophila Trithorax and yeast Set1 proteins and has histone 3 lysine 4 (H3K4) trimethylation activity. (
  • Together these proteins can affect each other's expression patterns as well as their own, thus forming a mESC-specific transcriptional regulatory circuit. (
  • This is connected to research on Chromatin and Gene Expression, as well as research on RNA proteins interactions. (

Effector Genes

  • Underlying the development of neural crest is a gene regulatory network, described as a set of interacting signals, transcription factors, and downstream effector genes that confer cell characteristics such as multipotency and migratory capabilities. (


  • 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. (
  • 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. (
  • 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. (
  • The gene set for iterative growth can be as large as the entire genome. (
  • This has been greatly facilitated by genome sequencing and subsequent design of microarrays allowing determination of gene expression patterns with near full-genome coverage. (
  • Evolutionarily, the F. graminearum genome can be divided into core regions shared with closely related species and variable regions harboring genes that are unique to F. graminearum and perform species-specific functions. (
  • 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. (
  • The Regulatory Genome: Gene Regulatory Networks In Development And Evolution (2006) ISBN 0-12-088563-8 Hinman, Veronica (2016). (
  • The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. (
  • Gene regulation studies include the organization and evolution of the regulatory genome, chromatin composition and transcriptional regulation mediated by steroid hormones, epigenetic mechanisms in leukemia and stem cells, regulation of periodic splicing and mRNA translation, and gene function and epigenetic reprogramming in embryogenesis and the germline. (


  • These molecules and their interactions comprise a gene regulatory network. (
  • In this way, however, the mRNAs are considered independently which contradicts the fact that interactions between genes are commonly observed. (
  • Due to the limited knowledge of the interactions between mRNAs, currently this method can only be applied to the gene expression data. (
  • Physical and/or regulatory interactions between transcription factors (TFs) and their target genes are essential to establish body plans of multicellular organisms during development, and these interactions have been studied extensively in the context of GRNs. (
  • In this study, we present the first gene‐centered GRN that includes ∼70 genes involved in C. elegans metabolism and physiology, 100 TFs and more than 500 protein-DNA interactions between them. (
  • This is achieved by a distributed, internal network of cooperative interactions (hydrophobic, polar and covalent). (
  • MyoD is known to have binding interactions with hundreds of muscular gene promoters and to permit myoblast proliferation. (
  • Networks can be built using lists of interactions entered via dialog boxes. (
  • Stabilizing selection, if ubiquitously spread across the network, could then be a "wall" that makes the formation of novel interactions more difficult and maintains previously established interactions. (


  • Some of these broad sets of data have already been assembled for building networks of gene regulation. (
  • Although it is not often possible to determine using co-expression analysis alone which transcription factors mediate this regulation, it is clear that large bodies of microarray data contain information which may allow reconstruction of regulatory networks. (
  • ConclusionsTaken together, these results lead us to propose a two-fold mechanism involved in the evolution of genetic canalization in gene regulatory networks: the shrinkage of mutational target useless genes are virtually removed from the network and redundancy in gene regulation so that some regulatory factors can be lost without affecting gene expression. (
  • The actual macroscopic dynamics of cell fate regulation itself must also be considered when studying the details of regulatory pathways for one cannot understand the regulating mechanisms without knowing the regulated behavior. (
  • Twist1/2, USF1/2, c-Myc, Id1 approximately 4, E12/47, Stra13) and one Zn finger protein (Snail which recognizes a subset of E-boxes), during osteoblast differentiation and their functional contributions to bone phenotypic gene regulation. (
  • 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. (
  • Through his collaborative work with Sankar Adhya in 1998, he elucidated the role of differential contact in the transcription regulation mechanism and demonstrated the theory in many genetic regulatory circuits. (
  • Although these genes are not spatially clustered, co-regulation seems to be achieved via this common cis and trans control mechanism. (
  • He and his colleagues discovered many of the key transcription factors and mechanisms responsible for cardiac gene regulation and formation of the heart and, in so doing, unveiled the molecular underpinnings of congenital and acquired diseases of the heart. (
  • This interaction is best characterized in the co-regulation of HOXA9 gene by SALL4 and MLL in leukemic cells. (
  • This is possibly due to down-regulation of Pou5f1 (encoding Oct4) expression and up-regulation of caudal-type homeobox 2 (Cdx2) gene expression. (
  • Gene Prediction and Modeling of Splicing is related to the research on Regulation of Alternative Splicing, Regulation of Protein Synthesis within the Gene Regulation program, and Genes and Diseases program. (
  • Another research line involves identification and characterization of genomic regions associated with gene regulation. (
  • The Gene Regulation, Stem Cells, and Cancer program focus on mechanisms of gene expression, mechanisms of epigenetic regulation, and the molecular underpinnings of cellular operations pertaining to tissue homeostasis and cancer. (

controlling gene expression

  • Gene regulatory networks play a vital role in organismal development and function by controlling gene expression. (

circadian clock

  • these show (i) an agreement with the current literature on the circadian clock network, (ii) the ability to model other networks, and (iii) that the learned network hypotheses can suggest new roles for poorly characterized genes, through addition of relevant genes from an unconstrained list of over 15,000 possible genes. (
  • The link between E-box-regulated genes and the circadian clock was discovered in 1997, when Hao, Allen, and Hardin (Department of Biology at Texas A&M University) analyzed rhythmicity in the period (per) gene in Drosophila melanogaster. (


  • 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. (
  • Activation of cardiac gene expression by myocardin, a transcriptional cofactor for serum response factor. (
  • During his doctoral training, his research interests focused on modelling the evolution of gene regulatory networks-he specifically examined the evolution of feed-forward loops, and studied the evolutionary influences of binding site organisation during transcriptional processes. (
  • They found a circadian transcriptional enhancer upstream of the per gene within a 69 bp DNA fragment. (
  • Sall4 is part of the transcriptional regulatory network that includes other pluripotent factors such as Oct4, Nanog, and Sox2 Because of its important role in early development, genetically mutated mice without functioning SALL4 die early on at the peri-implantation stage, while heterozygous mice have neural, kidney, heart defects and limb abnormalities. (


  • 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. (
  • In this dissertation, I try to infer the gene transcription regulatory networks by first detecting the downstream or regulated genes in the network, which tend to be differentially expressed under different conditions such as different tissues or treatment versus control experiment. (
  • After the downstream genes are detected, the genes that regulates the expression of these downstream genes are critical to infer the regulatory networks. (
  • A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied to learn parts of the transcriptomic network in Arabidopsis thaliana from a large number (thousands) of separate microarray experiments. (

groups of genes

  • Only a small proportion of these genes have been analysed by phenotypic characterization of mutants and fewer still have been subjected to microarray analysis to determine the groups of genes which are mis-regulated in these mutants. (
  • Since groups of genes involved in the same biological process often share one or more common control elements, it has been suggested that the differential expression of these synexpression groups in different tissues of organisms can contribute to co-evolution tissues, organs, and appendages. (
  • Today it is commonly believed that it is not primarily the gene products themselves that evolve, but that it is the control networks for groups of genes that contribute most to the evolution of higher eukaryotes. (

sets of genes

  • Therefore, the second method was developed which detects sets of genes that are enriched in differentially expressed or more generally speaking, phenotype associated genes. (
  • The sets of genes are predefined so that genes in each set interact with each other in certain way. (


  • Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. (
  • Understanding the gene regulatory network has always been one of the important and challenging tasks to understanding the mechanisms behind different biological processes or behaviors of organisms. (
  • Discrete mathematical formalisms are well adapted to model large biological networks, for which detailed kinetic data are scarce. (
  • During his time at MIT his lab started with parallel lines of research in actin dynamics and noise in gene networks, and then focused on stochasticity in gene networks biological networks as control systems, and the evolution of small networks. (
  • Modularity refers to the ability of a system to organize discrete, individual units that can overall increase the efficiency of network activity and, in a biological sense, facilitates selective forces upon the network. (
  • As a scholar, Doyle applies systems engineering principles to the analysis of regulatory mechanisms in biological systems. (


  • Why and how genetic canalization evolves in gene regulatory networks - Descarga este documento en PDF. (
  • ResultsThrough an individual-based simulation framework, we confirmed that most parameters associated with the network topology complexity and size of the network have less influence than mutational parameters rate and size of mutations on the evolution of genetic canalization. (
  • For example, genetic redundancy reduces the effect of mutations in any one copy of a multi-copy gene. (
  • Tim Taylor (2004): "A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots", Intelligent Autonomous Systems 8 (Proceedings of IAS8), F. Groen, N. Amato, A. Bonarini, E. Yoshida and B. Kröse (eds. (
  • The CT-Rich Regions(CTRR) located about 23 nucleotides upstream of the E-box is important in E-box binding, transactivation (increased rate of genetic expression), and transcription of circadian genes BMAL1/NPAS2 and BMAL1/CLOCK complexes. (
  • In this project, the investigators develop methodologies that will explore the effects of genetic variation in gene expression. (

represent genes

  • Synexpression groups in particular represent genes that are simultaneously up- or down-regulated, often because their gene products are required in stoichiometric amounts or are protein-complex subunits. (


  • 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. (
  • GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). (
  • Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. (
  • BioTapestry is an open source software application for modeling and visualizing gene regulatory networks (GRNs). (

candidate genes

  • 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. (
  • The cost and time to make and analyse mutants in Arabidopsis combined with the scarcity of phenotypic effects of mutagenesis means that computational tools suggesting candidate genes and roles are particularly useful. (

Genomic Regulatory Systems

  • 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. (
  • Allfrey, V.G. & Mirsky, A.E. (1964), "On the RNA synthesized during the lampbrush phase of amphibian oogenesis", PNAS, 52: 501-508, doi:10.1073/pnas.52.2.501 Genomic Regulatory Systems: Development and Evolution. (
  • 1997. The hardwiring of development: organization and function of genomic regulatory systems. (

pathways to produce

  • Similarly metabolic networks have multiple alternate pathways to produce many key metabolites. (


  • 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. (
  • To demonstrate the latter point, the method is used to suggest that particular GATA transcription factors are regulators of photosynthetic genes. (
  • This GRN reveals connectivity between key regulators and their target genes. (
  • Focusing on key regulators, this network contains eight distinct but interwoven modules. (


  • 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. (


  • 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. (
  • 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. (
  • These genes share one common cis-regulatory element, called ECB, which serves as a binding site for the MCM1 trans-acting protein. (
  • An E-box (enhancer box) is a DNA response element found in some eukaryotes that acts as a protein-binding site and has been found to regulate gene expression in neurons, muscles, and other tissues. (
  • These include, but are not limited to: CACGTT sequence 20 bp upstream of the mouse Period2 (PER2) gene and regulates its expression CAGCTT sequence found within the MyoD core enhancer CACCTCGTGAC sequence in the proximal promoter region of human and rat APOE, which is a protein component of lipoproteins. (
  • Sal-like protein 4 (SALL4) is a transcription factor encoded by a member of the Spalt-like (SALL) gene family, SALL4. (


  • We also established that selecting for extreme phenotypic optima nil or full gene expression leads to much higher canalization levels than selecting for intermediate expression levels. (

developmental gene

  • In 2011, he was awarded the International Prize for Biology in recognition for his pioneering work on developmental gene regulatory networks. (
  • Zebrafish developmental gene regulatory network from the Yuh Lab. (


  • Transcription factors also regulate each other and respond to external signals, forming a regulatory network that orchestrates the cellular response to environmental cues. (


  • The SALL4 gene encodes at least three isoforms, termed A, B, and C, through alternative splicing, with the A and B forms being the most studied. (
  • SALL4 can alter gene expression changes through its interaction with many co-factors and epigenetic complexes. (
  • Beside the NuRD complex, SALL4 is reportedly able to bind to other epigenetic modifiers such as histone lysine-specific demethylase 1 (LSD1), which is frequently associated with the NuRD complex and subsequently gene repression. (


  • For T-helper differentiation, the GR network is reasonably complex (he's using this as the example for the talk). (
  • A. Kriete, E. Eils, Revised 3/2/05 Multistability and Multicellularity: Cell Fates as High-dimensional Attractors of Gene Regulatory Networks Sui Huang Vascular Biology Program Children's Hospital / Harvard Medical School Boston, MA I. INTRODUCTION A hall-mark of multicellular organisms is the differentiation of cells into functionally distinct cell types, such as a resting nerve cell or a proliferating skin cell. (
  • For instance, a nerve cell differentiation factor would induce the expression of neuronal genes and repress genes specific for other tissues. (
  • Thus, our data suggest that the integrated activities of negative and positive E-box related regulatory factors control osteoblast differentiation. (
  • Setdb1 appears to be necessary to maintain both MyoD expression and also genes that are specific to muscle tissues because reduction of Setdb1 expression results in a severe delay of myoblast differentiation and determination. (
  • A gene with homology to the myc similarity region of MyoD1 is expressed during myogenesis and is sufficient to activate the muscle differentiation program. (


  • Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. (
  • A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. (
  • Against such strong positive selection, other evolutionary forces acting on the network must exist, with gaps of relaxed selection, to allow focused reorganization to occur. (


  • Much of molecular biology aims to decipher the mechanisms organisms use to modulate their gene expression patterns. (


  • Stochastic modelling techniques include static network analysis, kinetic modeling techniques, and boolean modeling (randomly flipping the expression of nodes). (
  • Today, Van Oudenaardens work at the Hubrecht Institute focuses on stochastic gene expression, developing new tools for quantifying gene expression in single cells and MicroRNAs "Alexander van Oudenaarden" (in Dutch). (
  • Some particular topics of investigation are: signal transduction, gene regulatory networks, multicellular patterning, chemotaxis, systems neuroscience, the evolution of networks, and the effect of stochastic noise at the organism level. (

Systems Biology

  • GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. (
  • The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. (
  • In fact, many molecular biologists now even equate systems biology with network biology. (
  • Analysis BioTapestry can create Systems Biology Markup Language files for a subset of networks. (
  • Gene regulatory network Systems biology Longabaugh WJR, Davidson EH, Bolouri H (2005). (
  • Systems Biology research includes dynamic gene regulatory networks and systems neuroscience. (


  • 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. (


  • In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. (
  • In animals, it is often beneficial for appendages to co-evolve, and it has been observed that fore-and hind-limbs share expression of Hox genes early in metazoan development. (


  • Expression Pattern Analysis of Regulatory Transcription Factors in Caenorhabditis elegans Huiyun Feng, Hannah L. Craig, and Ian A. Hope 3. (


  • Overall, constrained networks evolve less canalization than networks in which some genes could evolve freely i.e. without direct stabilizing selection pressure on gene expression. (
  • Developmental processes provide an example of how changes in synexpression control networks could significantly affect an organism's capacity to evolve and adapt effectively. (


  • Robustness can be empirically measured for several genomes and individual genes by inducing mutations and measuring what proportion of mutants retain the same phenotype, function or fitness. (


  • Although MyoD marks myoblast commitment, muscle development is not dramatically ablated in mouse mutants lacking the MyoD gene. (

computational analysis

  • 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. (


  • The course covers structure and function of genomically encoded gene networks controlling many developmental processes, in vertebrate, Drosophila, and sea urchin model systems. (
  • The SALL genes were identified based on their sequence homology to Spalt, which is a homeotic gene originally cloned in Drosophila melanogaster that is important for terminal trunk structure formation in embryogenesis and imaginal disc development in the larval stages. (


  • In multicellular animals the same principle has been put in the service of gene cascades that control body-shape. (
  • The precise control of differential gene expression is also of critical importance to maintain physiological homeostasis, and many metabolic disorders such as obesity and diabetes coincide with substantial changes in gene expression. (
  • It is likely that these gene groups share common cis- and trans-acting control elements to achieve coordinate expression. (
  • Control of stress-dependent cardiac growth and gene expression by a microRNA. (
  • Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. (


  • Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol. (
  • Thus, we propose another analysis method which utilizes an EM algorithm to predict the gene regulatory network from the gene expression data, sequence data and allele-specific expression data in certain number of genotypes. (
  • Thus, changes in the regulatory patterns of these genes would affect the development of both the fore- and hind-limbs, facilitating co-evolution. (


  • Looking at stochasticity in nodes and in functions within GR networks. (


  • Synexpression is a type of non-random eukaryotic gene organization. (


  • Additionally, the performance in recovering a known network from different amounts of synthetically generated data is evaluated. (


  • In single-celled organisms, regulatory networks respond to the external environment, optimising the cell at a given time for survival in this environment. (
  • A gene that is turned on in one cell may make a product that leaves the cell and diffuses through adjacent cells, entering them and turning on genes only when it is present above a certain threshold level. (
  • The regulatory network responsible for spatial patterning along the long embryo axis will ultimately convey a unique cell fate to each of the ~100 rows of nuclei, by extracting positional information from maternally-deposited chemical cues. (
  • In the latter, many liver-specific genes are shut down and the cell undergoes repeated cell divisions - yet this cell is still a liver cell. (
  • MyoD has also been shown to function cooperatively with the tumor suppressor gene, Retinoblastoma (pRb) to cause cell cycle arrest in the terminally differentiated myoblasts. (
  • T-cell gene regulatory network from the Rothenberg Lab. (
  • One simplified example of a synexpression group is the genes cdc6, cdc3, cdc46, and swi4 in yeast, which are all co-expressed early in the G-1 stage of the cell cycle. (

noise in gene

  • We analyze these contributions to noise in gene expression and ask how cells manage to function reliably in the presence of such noise. (


  • BioTapestry was initially made public in late 2003 as a web-based, read-only interactive viewer for the sea urchin network, with the first fully functional editor released in August 2004 (v0.94.1). (

transcription factor

  • 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. (


  • In coherence with this observation, the promoter region of slug (a neural crest specific gene) contains a binding site for transcription factors involved in the activation of Wnt-dependent target genes, suggestive of a direct role of Wnt signaling in neural crest specification. (
  • A splice-variant of the E2-2 was discovered in 1997 and was found to inhibit the promoter of a muscle-specific gene. (


  • A key verified target gene encodes the enzyme phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase (PTEN). (


  • While individual array experiments can be examined for differential expression of genes of interest, this may be misleading owing to irreproducibility, and only uses a small fraction of the data often available. (


  • We reconstructed the global F. graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a machine-learning algorithm. (


  • KAP1 is localized at muscle-related genes in myoblasts along with both MyoD and Mef2 (a myocyte transcription enhancer factor). (


  • 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. (
  • 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. (
  • 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. (
  • It is expected that genes that function in the same process be regulated coordinately. (

expression patterns

  • The use of this technology helps researchers monitor changes in expression patterns for large numbers of genes in a given experiment. (