Gene co-expression network analysis of transcriptome data has enabled the identification of key genes and important networks underlying complex production and disease traits. This study used weighted gene co-expression network analysis (WGCNA) approach to (1) detect modules or clusters of differentially expressed genes (DEG) with similar expression patterns in calf rumen transcriptome during pre- and post-weaning periods and (2) identify regulatory mechanisms linking gene modules to relevant phenotypes during the pre-weaning period (day 33 [d33]): weight gain (BWT_d33), average daily gain (ADG_d33), blood glucose (Glucose_d33) and β-hydroxybutyrate (BHB_d33) concentrations and post-weaning period (d96): weight gain (BWT_d96), average daily gain (ADG_d96), blood glucose (Glucose_d96) and β-hydroxybutyrate (BHB_d96) concentrations, dry matter intake (DMI_d96) and feed efficiency (FE_d96). Rumen tissues were collected from 16 calves on d33 and another 16 on d96 for whole transcriptome sequencing ...
Table_6_Weighted Gene Co-expression Network Analysis Identifies Critical Genes for the Production of Cellulase and Xylanase in Penicillium oxalicum.XLSX
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our
To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in ...
Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or periods during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where
This video was recorded at 6th International Workshop on Machine Learning in Systems Biology (MLSB), Basel 2012. Motivation: Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re- implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment, and equally well performing as existing methods. Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard E. coli and yeast reference datasets showed that this ...
Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic
Gene regulation is accomplished mainly by the interplay of multiple transcription factors. This gives rise to highly complex and cell-type specific, interwoven structures of regulatory interactions summarized in gene regulatory networks. In this thesis, I address two approaches of computational analysis of such networks, forward modeling and reverse engineering. The first part of this thesis is about the Web application GEne Network GEnerator (GeNGe) which I have developed as a framework for automatic generation of gene regulatory network models. I have developed a novel algorithm for the generation of network structures featuring important biological properties. In order to model the transcriptional kinetics, I have modified an existing non-linear kinetic. This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. GeNGe supports also the generation of various in silico experiments for predicting effects of perturbations as theoretical counterparts of ...
Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using
GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying
Title: Validation of Inference Procedures for Gene Regulatory Networks. VOLUME: 8 ISSUE: 6. Author(s):Edward R. Dougherty. Affiliation:Department of Electrical and Computer Engineering, Texas A University, College Station, TX 77843-3128, USA.. Keywords:Epistemology, gene network, inference, validation. Abstract: The availability of high-throughput genomic data has motivated the development of numerous algorithms to infer gene regulatory networks. The validity of an inference procedure must be evaluated relative to its ability to infer a model network close to the ground-truth network from which the data have been generated. The input to an inference algorithm is a sample set of data and its output is a network. Since input, output, and algorithm are mathematical structures, the validity of an inference algorithm is a mathematical issue. This paper formulates validation in terms of a semi-metric distance between two networks, or the distance between two structures of the same kind deduced from ...
Gene expression network analysis and applications to immunology - We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.
This function fits iRafNet, a flexible unified integrative algorithm that allows information from prior data, such as protein-protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference. This function takes as input only one set of sampling scores, computed considering one prior data such as protein-protein interactions or gene expression from knock-out experiments. Note that some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).
There are various factors that alter physiological characteristics in skin. Elucidating the underlying mechanism of transcriptional alterations by intrinsic and extrinsic factors may lead us to understand the aging process of skin. To identify the transcriptomic changes of the aging skin, we analyzed publicly available RNA sequencing data from Genotype-Tissue Expression (GTEx) project. GTEx provided RNA sequencing data of suprapubic (n=228) and lower leg (n=349) skins, which are photo-protected and photo-damaged. Using differentially expressed gene analysis and weighted gene co-expression network analysis, we characterized transcriptomic changes due to UV exposure and aging. Genes involved in skin development such as epidermal differentiation complex component (SPRR and LCE families), vasculature development (TGFBR1, TGFBR2, TGFBR3, KDR, FGF2, and VEGFC), and matrix metalloproteinase (MMP2, MMP3, MMP8, MMP10, and MMP13) were up
Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF. Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database. We performed weighted correlation network and differential
Enriched gene interaction network detected with opposite expression patterns in PZQ-treated paired or unpaired mature females.This gene interaction network is s
In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the
Animals consist of a wide variety of cells that serve different functions depending on their location in the body. Cells with similar functions, or cell types, in different animal species are related both by an evolutionary line of descentÐsimilar to the relatedness of species themselvesÐand by a developmental line of descent in the embryo. Networks of interacting genes, or gene regulatory networks, control gene expression in the cell, thereby specifying cell type identity. Understanding how new cell types arise by changing gene regulatory networks is critical both to comprehending fundamental aspects of human biology and to manipulating cell types in the laboratory. We approached this question by studying endometrial stromal fibroblast (ESF) cells from the uterus of humans and opossums, two distantly related mammals. We showed that the distantly related cell type in opossum expresses a similar set of regulatory genes as the human cell, but in response to pregnancy-related signals, the opossum ...
Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae (Balcan et al., 2007). Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in
A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included
Author Summary A regulatory protein can activate the expression of a target gene either directly, i.e., by binding to the genes promoter, or indirectly, i.e., by altering the expression of regulators, which, in turn, bind to the target genes promoter and induce or inhibit its transcription. Indirect regulatory circuits can contain multiple components and functional elements, such as feedforward and feedback loops. The complex structure of indirect regulation raises the question of its evolutionary origins. Here, we study the dynamic and evolutionary properties of regulatory architectures that involve members of the recently emerged class of bacterial proteins termed connectors. Such proteins post-translationally modulate the activity of two-component systems and phosphorelays, which constitute the prevalent form of bacterial signal transduction. We describe a novel connector-mediated regulatory circuit that combines the structural and functional properties of direct and indirect regulation. Our
This chapter presents a survey of recent methods for reconstruction of time-varying biological networks such as gene interaction networks based on time series node observations (e.g. gene expressions) from a modeling perspective. Time series gene expression data has been extensively used for analysis of gene interaction networks, and studying the influence of regulatory relationships on different phenotypes. Traditional correlation and regression based methods have focussed on identifying a single interaction network based on time series data. However, interaction networks vary over time and in response to environmental and genetic stress during the course of the experiment. Identifying such time-varying networks promises new insight into transient interactions and their role in the biological process. A key challenge in inferring such networks is the problem of high-dimensional data i.e. the number of unknowns p is much larger than the number of observations n. We discuss the computational aspects of
We performed an experimental evaluation of a supervised learning algorithm, namely PosOnly, which is able to learn from only positive and unlabeled examples. Such a method is particulary suitable in the context of gene regulatory networks where a partial set of known regulatory connections is available in public databases. In such a contexts it is crucial to take into account that the only available information are a partial set of gene-gene interactions, i.e. positive examples, and unlabeled data which could include both positive and negative examples.. The data mining community developed a number of approaches to deal with such a problem. In this paper we adopted the approach introduced in [15] that we compared, through a benchmark experiment performed with simulated and experimental data, with a negative selection method introduced in [20] (PSEUDO-RANDOM) and with the current state of the art of supervised methods, namely SVMOnly [14]. We showed that PosOnly, outperforms significantly both ...
Gene co-epxression network analyses are common in the study of large scale biological data sets. In this study, we have developed a methodology for the comparison of pairs of co-expression networks using the s-core network peeling approach. We apply the methodology to gene-expression data for human and mouse. ...
Endosperm is an absorptive structure that supports embryo development or seedling germination in angiosperms. The endosperm of cereals is a main source of food, feed, and industrial raw materials worldwide. However, the genetic networks that regulate endosperm cell differentiation remain largely unclear. As a first step toward characterizing these networks, we profiled the mRNAs in five major cell types of the differentiating endosperm and in the embryo and four maternal compartments of the maize (Zea mays) kernel. Comparisons of these mRNA populations revealed the diverged gene expression programs between filial and maternal compartments and an unexpected close correlation between embryo and the aleurone layer of endosperm. Gene coexpression network analysis identified coexpression modules associated with single or multiple kernel compartments including modules for the endosperm cell types, some of which showed enrichment of previously identified temporally activated and/or imprinted genes. ...
Mason MJ, Fan G, Plath K, Zhou Q, Horvath S (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells BMC Genomics 2009, 10:327. ...
Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. ...
It is now well established that the study of biological complexity has shifted from gene level to interaction networks and this shift from components to associated interactions has gained increasing interest in network biology. Gene Regulatory Networks (GRNs) depict the functioning circuitry in organisms at the gene level and represent an abstract mapping of the more complicated biochemical network which includes other components such as proteins, metabolites, etc. Understanding GRNs can provide new ideas for treating complex diseases and offer novel candidate drug targets. A commonly accepted top-down approach is to reverse engineer GRNs from experimental data generated by microarray technology [1-5].. Early computational approaches for inferring GRNs from gene expression data employed classical methods. Boolean network modeling considers the gene expression to be in a binary state (either switched on or off), and display via a Boolean function the impact of other genes on a specific target ...
Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best
In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Although the individual networks represent different levels of cellular interactions with global structural and
Eric H. Davidson, PhD, has been awarded the International Prize for Biology from the Japan Society for the Promotion of Science for his research related to understanding gene regulatory networks, particularly as it pertains to embryonic development. Dr. Davidson is Norman Chandler Professor of Cell Biology in the Division of Biology at the California Institute of Technology in Pasadena.. Instead of focusing on individual genes, Dr. Davidson has focused his studies on a broad approach to understanding how groups of genes are regulated and interact to establish and maintain the developmental program that underlies developmental processes. This approach has proven critical to understanding how a single cell can ultimately give rise to multiple specialized cells and tissues with an array of diverse functions.. As their model system, Dr. Davidson and his colleagues have used the sea urchin to define and study these developmental gene regulatory networks. The production of hundreds of thousands of ...
With the advent of sequencing technologies, a variety of methods have been available day by day. Each of these methods have helped scientists to for a deeper understanding of the biological function and evolutionary constraints on the relevant genes, which can be gained through the use of modern computational approaches. Numerous approaches have being developed to advance these goals, and interaction network mapping is one of them. This method has been employed to study a variety of organisms to illustrate shared (conserved) or individual (unique) properties, and is mainly based on identifying and visualizing modules of co-expressed genes. As being a very strong candidate for such tools, co-expression gene network was used in this study to indentify the genes in wood formation of Populus trichocarpa with the help of the other novel bioinformatics tools such as Gene Ontology and Cytoscape. In order to booster the accuracy of the findings, we have combined it with an evolutionary approach, ...
Most of Gene Regulatory Network (GRN) studies are based on crisp and parametric algorithms, despite inherent fuzzy nature of gene co-regulation. This paper presents Adaptive Fuzzy Evolutionary GRN Reconstruction (AFEGRN) framework for modeling GRNs. The AFEGRN automatically determines model parameters, such as, number of clusters for fuzzy c-means using fuzzy-PBM index and Estimation of Gaussian Distribution Algorithm. The proposed strategy was tested for breast cancer and normal GRNs. The results conformed to biological knowledge and showed that most of cancer related GRN changes were caused by differentially expressed genes. This demonstrates effectiveness of AFEGRN to model any GRN.. ...
Morphogenesis and the spatial structure of an organism have repercussions on gene expression. These effects can influence the results of regulatory network reconstruction. An integrated, flexible and extensible computational framework for modelling gene expression dynamics within spatially growing structures is developed and used as a test system for evaluating a reconstruction algorithm. With complex morphological structures, significant effects of spatial organisation on the reconstruction process are observed. The results also reveal that stronger regulatory interactions result in more frequent cases of indirect regulation, posing a challenge for accurate network reconstruction.. ...
Feed-forward loops (FFLs), consisting of miRNAs, transcription factors (TFs) and their common target genes, have been validated to be important for the initialization and development of complex diseases, including cancer. Esophageal Carcinoma (ESCA) and Stomach Adenocarcinoma (STAD) are two types of malignant tumors in the digestive tract. Understanding common and distinct molecular mechanisms of ESCA and STAD is extremely crucial. In this paper, we presented a computational framework to explore common and distinct FFLs, and molecular biomarkers for ESCA and STAD. We identified FFLs by combining regulation pairs and RNA-seq data. Then we constructed disease-specific co-expression networks based on the FFLs identified. We also used random walk with restart (RWR) on disease-specific co-expression networks to prioritize candidate molecules. We identified 148 and 242 FFLs for these two types of cancer, respectively. And we found that one TF, E2F3 was related to ESCA, two genes, DTNA and KCNMA1 were related
Cells acquire their unique fates by the differential pathways of combinatorial gene activity during the developmental period. Gene regulatory networks (GRN) con...
Relationships between Models of Genetic Regulatory Networks with Emphasis on Discrete State Stochastic Models: 10.4018/978-1-5225-0353-8.ch002: Genetic Regulatory Networks (GRNs) represent the interconnections between genomic entities that govern the regulation of gene expression. GRNs have been
netmes - Netmes is an R software package that allows assessing the Gene Regulatory Network Inference (GRNI) algorithms in detail by using several local and global network-based error measures in combination with an ensemble approach ...
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. Some of these broad sets of data have already been assembled for building networks of gene regulation. Even though these datasets are still far from comprehensive, and the approach faces many important and difficult challenges, some strategies have begun to make connections between disparate regulatory events and to foster new hypotheses. In this article we review several different genomics and proteomics technologies, and present bioinformatics methods for exploring these data in order to make novel discoveries.. ...
To appear in Computational Systems Biology, 2005, Eds. A. Kriete, E. Eils, Revised 3/2/05 Multistability and Multicellularity: Cell Fates as High-dimensional Attractors of Gene Regulatory Networks Sui
The extracellular matrix (ECM) is a determining factor in the tumor microenvironment that restrains or promotes malignant growth. In this report, we show how the molecular chaperone protein Hsp47 functions as a nodal hub in regulating an ECM gene transcription network. A transcription network analysis showed that Hsp47 expression was activated during breast cancer development and progression. Hsp47 silencing reprogrammed human breast cancer cells to form growth-arrested and/or non-invasive structures in 3D cultures, and to limit tumor growth in xenograft assays by reducing deposition of collagen and fibronectin. Co-expression network analysis also showed that levels of microRNA-29b and 29c were inversely correlated with expression of Hsp47 and ECM network genes in human breast cancer tissues. We found that miR-29 repressed expression of Hsp47 along with multiple ECM network genes. Ectopic expression of miR-29b suppressed malignant phenotypes of breast cancer cells in 3D culture. Clinically, ...
Another study in the special feature by Marianne Bronner-Fraser, the second Albert Billings Ruddock Professor of Biology, focuses on the gene regulatory network underlying neural crest formation in the lamprey, the most primitive living vertebrate. The neural crest is a group of embryonic cells that are pinched off during the formation of the neural tube--the precursor to the spinal cord--and then migrate throughout the developing body to form other nervous system structures. The study "reveals order and linkages within the network at early stages," Bronner-Fraser says. "Because the neural crest cell type represents a vertebrate innovation, our work in lampreys shows that this network is ancient and tightly conserved to the base of vertebrates," she says.. The fourth of the Caltech papers, by Paul W. Sternberg, the Thomas Hunt Morgan Professor of Biology at Caltech and an investigator with the Howard Hughes Medical Institute (HHMI), and his colleagues, looks at a postembryonic gene regulatory ...
Viswesh Periyasamy presented a poster in May at the Great Lakes Bioinformatics Consortium 2019 Conference (GLBIO) on the topic of gene regulatory network inference. This is a method to recover the interactions and relationships between …
Salmonella Typhimurium is an important pathogen of human and animals. It shows a broad growth range and survives in harsh conditions. The aim of this study was to analyze transcriptional responses to a number of growth and stress conditions as well as the relationship of metabolic pathways and/or cell functions at the genome-scale-level by network analysis, and further to explore whether highly connected genes (hubs) in these networks were essential for growth, stress adaptation and virulence. De novo generated as well as published transcriptional data for 425 selected genes under a number of growth and stress conditions were used to construct a bipartite network connecting culture conditions and significantly regulated genes (transcriptional network). Also, a genome scale network was constructed for strain LT2. The latter connected genes with metabolic pathways and cellular functions. Both networks were shown to belong to the family of scale-free networks characterized by the presence of highly
Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of time samples and requirements to estimate a large number of parameters. 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. This approach is tested on several synthetic and one real dataset. The results indicate the efficacy of the approach with promising future directions ...
The unprecedented volumes of large-scale genomic and genetic data being generated today, combined with the poor understanding of the genetics underlying complex biological systems, demand a systems biology approach to identify a global landscape of interactomes that contribute to a variety of clinical endpoints such as tumor progression. Along this direction, two kinds of gene network analyses, association (gene coexpression network) and causality inference (Bayesian network), have been almost independently developed in the past decade. In spite of the significant success of the methods, it is still far away from generating a clear picture for even a single pathway involved in complex human disease like cancer due to insufficient information and various limitations of the methods. Here we present a framework to combine the two types of network analyses with an aim of integrating many high-throughput gene expression datasets on multiple platforms. By applying the integrative network approach to ...
... 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. This is the 7th edition of this unique course, which is open to graduate students, postdocs, PIs or anyone else who might profit from a fast moving treatment of this new field ...