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1.  Understanding the Dynamic Behavior of Genetic Regulatory Networks by Functional Decomposition 
Current genomics  2006;7(6):333-341.
A number of mechanistic and predictive genetic regulatory networks (GRNs) comprising dozens of genes have already been characterized at the level of cis-regulatory interactions. Reconstructions of networks of 100’s to 1000’s of genes and their interactions are currently underway. Understanding the organizational and functional principles underlying these networks is probably the single greatest challenge facing genomics today. We review the current approaches to deciphering large-scale GRNs and discuss some of their limitations. We then propose a bottom-up approach in which large-scale GRNs are first organized in terms of functionally distinct GRN building blocks of one or a few genes. Biological processes may then be viewed as the outcome of functional interactions among these simple, well-characterized functional building blocks. We describe several putative GRN functional building blocks and show that they can be located within GRNs on the basis of their interaction topology and additional, simple and experimentally testable constraints.
PMCID: PMC2134916  PMID: 18079985
Genetic regulatory networks; systems biology; transcriptional regulation; visualization
2.  Modeling stochasticity and robustness in gene regulatory networks 
Bioinformatics  2009;25(12):i101-i109.
Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.
Results: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs.
Availability: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from∼garg/genysis.html.
PMCID: PMC2687968  PMID: 19477975
3.  Evolution of Gene Regulatory Networks that Control Embryonic Development of the Body Plan 
Cell  2011;144(6):970-985.
Alteration of the functional organization of the gene regulatory networks (GRNs) that control development of the body plan causes evolutionary change in animal morphology. A major mechanism of evolutionary change in GRN structure is alteration of cis-regulatory modules that determine regulatory gene expression. Both evolutionary conservation and evolutionary innovation must be considered in terms of GRN structure. Here we consider the causes and consequences of GRN evolution, both from an a priori point of view, and in light of extensive recent research on developmental regulatory alterations occurring at different levels of GRN hierarchy. Some GRN subcircuits are of great antiquity while other aspects are highly flexible and thus in any given genome more recent. Both evolutionary conservation and evolutionary innovation occur at the level of whole GRN subcircuits. This mosaic view of the evolution of GRN structure explains major aspects of evolutionary process, such as hierarchical phylogeny and discontinuities of paleontological change and stasis.
PMCID: PMC3076009  PMID: 21414487
4.  Emerging properties of animal gene regulatory networks 
Nature  2010;468(7326):911-920.
Gene regulatory networks (GRNs) provide system level explanations of developmental and physiological functions in the terms of the genomic regulatory code. Depending on their developmental functions, GRNs differ in their degree of hierarchy, and also in the types of modular sub-circuit of which they are composed, although there is a commonly employed sub-circuit repertoire. Mathematical modelling of some types of GRN sub-circuit has deepened biological understanding of the functions they mediate. The structural organization of various kinds of GRN reflects their roles in the life process, and causally illuminates both developmental and evolutionary process.
PMCID: PMC3967874  PMID: 21164479
5.  Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network 
We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks.
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. 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. Much work has focused on the GRNs that control metazoan development; however, the design principles and organization of the GRNs that control systems physiology remain largely unexplored.
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. The resulting metabolic GRN is enriched for NHRs, compared with other gene-centered regulatory networks. NHRs are well-known regulators of lipid meta-qj;bolism in mammals. The transcriptional activity of NHRs can be modified by diffusible ligands, which allows these TFs to function as molecular sensors and rapidly alter the expression of their target genes. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. Furthermore, their organization in GRNs has not yet been investigated. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Interestingly, network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues.
NHRs can interact with transcriptional cofactors such as chromatin remodeling complexes and Mediator components. For instance, the C. elegans Mediator subunit, MDT-15, can interact with NHR-49 to regulate the expression of its target genes. To find all the TFs that MDT-15 can interact with, we performed systematic yeast two-hybrid assays with MDT-15 versus 755 full-length TFs. We found that MDT-15 preferentially associates with NHRs, and specifically with those NHRs that confer a metabolic phenotype and that occur in the metabolic GRN. This illustrates the central role of MDT-15 in the regulation of metabolic gene expression.
Using a variety of genetic and biochemical approaches, we characterized NHR-86 in more detail. NHR-86 participates in one of the two NHR modules, and has a high-flux capacity; that is it has both a high incoming and a high outgoing degree. We obtained an nhr-86 mutant and generated an NHR-86 antibody, and showed that NHR-86 functions as an auto-repressor in vivo and that nhr-86 mutant animals store abnormally high levels of body fat.
Finally, we discovered a novel NHR circuit that responds to nutrient availability. In this circuit NHR-45 regulates the activity of nhr-178 promoter in two distinct physiologically important tissues: the intestine and the hypodermis. Both of these NHRs are required to maintain lipid homeostasis in C. elegans. The expression of nhr-178 is responsive to the nutritional status of the animal, which switches between ON and OFF states in the hypodermis. We found that NHR-45 activity is necessary to control this switch in the hypodermis. Interestingly, NHR-45 has opposite effects on the activity of the nhr-178 promoter in these tissues: NHR-45 activates this promoter in the intestine, but represses it in the hypodermis.
Altogether our study leads to a model in which the expansion of the NHR family, TFs that have the capacity to act as fast molecular sensors, is combined with a modular network organization to enable rapid and robust responses to various environmental cues.
Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. This network is enriched for nuclear hormone receptors (NHRs). The NHR family has greatly expanded in nematodes: humans have 48 NHRs, but C. elegans has 284, most of which are uncharacterized. We find that the C. elegans metabolic GRN is highly modular and that two GRN modules predominantly consist of NHRs. Network modularity has been proposed to facilitate a rapid response to different cues. As NHRs are metabolic sensors that are poised to respond to ligands, this suggests that C. elegans GRNs evolved to enable rapid and adaptive responses to different cues by a concurrence of NHR family expansion and modular GRN wiring.
PMCID: PMC2890327  PMID: 20461074
C. elegans; gene regulatory network; metabolism; nuclear hormone receptor; transcription factor
6.  Protein–DNA binding dynamics predict transcriptional response to nutrients in archaea 
Nucleic Acids Research  2013;41(18):8546-8558.
Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.
PMCID: PMC3794607  PMID: 23892291
7.  Mechanistic Explanations for Restricted Evolutionary Paths That Emerge from Gene Regulatory Networks 
PLoS ONE  2013;8(4):e61178.
The extent and the nature of the constraints to evolutionary trajectories are central issues in biology. Constraints can be the result of systems dynamics causing a non-linear mapping between genotype and phenotype. How prevalent are these developmental constraints and what is their mechanistic basis? Although this has been extensively explored at the level of epistatic interactions between nucleotides within a gene, or amino acids within a protein, selection acts at the level of the whole organism, and therefore epistasis between disparate genes in the genome is expected due to their functional interactions within gene regulatory networks (GRNs) which are responsible for many aspects of organismal phenotype. Here we explore epistasis within GRNs capable of performing a common developmental function – converting a continuous morphogen input into discrete spatial domains. By exploring the full complement of GRN wiring designs that are able to perform this function, we analyzed all possible mutational routes between functional GRNs. Through this study we demonstrate that mechanistic constraints are common for GRNs that perform even a simple function. We demonstrate a common mechanistic cause for such a constraint involving complementation between counter-balanced gene-gene interactions. Furthermore we show how such constraints can be bypassed by means of “permissive” mutations that buffer changes in a direct route between two GRN topologies that would normally be unviable. We show that such bypasses are common and thus we suggest that unlike what was observed in protein sequence-function relationships, the “tape of life” is less reproducible when one considers higher levels of biological organization.
PMCID: PMC3629181  PMID: 23613807
8.  Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks 
BMC Bioinformatics  2007;8(Suppl 7):S13.
The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency.
In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches.
The comparison indicates that both approaches had good performance in modeling the gene regulatory networks. The accuracy in terms of recall and precision can be improved if a smaller subset of genes is selected for inferring GRNs. The accuracy of both approaches is dependent upon the number of selected genes and time points of gene samples. In all tested cases, DBN identified more gene interactions and gave better recall than PBN.
PMCID: PMC2099481  PMID: 18047712
9.  RMaNI: Regulatory Module Network Inference framework 
BMC Bioinformatics  2013;14(Suppl 16):S14.
Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.
We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at
RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.
PMCID: PMC3853211  PMID: 24564496
Cancer; Systems biology; Transcriptional Module Networks; Microarray; Gene Regulatory Network; Modules
10.  Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network 
BMC Systems Biology  2013;7:126.
Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.
The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15.
For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.
PMCID: PMC3843564  PMID: 24228871
11.  Functional Genomic Analyses Identify Pathways Dysregulated by Progranulin Deficiency Implicating Wnt Signaling 
Neuron  2011;71(6):1030-1042.
Progranulin (GRN) mutations cause frontotemporal dementia (FTD), but GRN’s function in the CNS remains largely unknown. To identify the pathways downstream of GRN, we used weighted gene co-expression network analysis (WGCNA) to develop a systems-level view of transcriptional alterations in a human neural progenitor model of GRN-deficiency. This highlighted key pathways such as apoptosis and ubiquitination in GRN deficient human neurons, while revealing an unexpected major role for the Wnt signaling pathway, which was confirmed by analysis of gene expression data from postmortem FTD brain. Furthermore, we observed that the Wnt receptor Fzd2 was one of only a few genes up-regulated at 6 weeks in a GRN knockout mouse, and that FZD2 reduction caused increased apoptosis, while its upregulation promoted neuronal survival in vitro. Together, these in vitro and in vivo data point to an adaptive role for altered Wnt signaling in GRN deficiency-mediated FTD, representing a potential therapeutic target.
PMCID: PMC3633414  PMID: 21943601
Progranulin; Frontotemporal Dementia; Wnt; Fzd2; WGCNA
12.  Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation 
PLoS ONE  2014;9(2):e87446.
Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.
PMCID: PMC3925093  PMID: 24551058
13.  An Integrative Approach to Inferring Gene Regulatory Module Networks 
PLoS ONE  2012;7(12):e52836.
Gene regulatory networks (GRNs) provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of gene regulatory modules and GRNs require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming.
In this work, the BiologicalNetworks application–the data integration and network based research environment–was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the gene regulation analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided.
The generated modular networks, regulatory modules and binding sites can be visualized and further analyzed within this same application. The developed tools were applied to the mouse model of asthma and the OCT4 regulatory network in embryonic stem cells. Developed methods and data are available through the Java application from BiologicalNetworks program at
PMCID: PMC3527610  PMID: 23285197
14.  B-cell lymphoma gene regulatory networks: biological consistency among inference methods 
Frontiers in Genetics  2013;4:281.
Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level—that are more important for our biological understanding—the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.
PMCID: PMC3864360  PMID: 24379827
gene regulatory network; C3Net; BC3Net; Aracne; GPEA; statistical inference
15.  Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks 
Bioinformatics  2013;29(8):1060-1067.
Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein–protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.
Results: We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.
Availability and implementation: Code, datasets and networks presented in this article are available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3624811  PMID: 23525069
16.  Bagging Statistical Network Inference from Large-Scale Gene Expression Data 
PLoS ONE  2012;7(3):e33624.
Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.
PMCID: PMC3316596  PMID: 22479422
17.  Anomaly detection in gene expression via stochastic models of gene regulatory networks 
BMC Genomics  2009;10(Suppl 3):S26.
The steady-state behaviour of gene regulatory networks (GRNs) can provide crucial evidence for detecting disease-causing genes. However, monitoring the dynamics of GRNs is particularly difficult because biological data only reflects a snapshot of the dynamical behaviour of the living organism. Also most GRN data and methods are used to provide limited structural inferences.
In this study, the theory of stochastic GRNs, derived from G-Networks, is applied to GRNs in order to monitor their steady-state behaviours. This approach is applied to a simulation dataset which is generated by using the stochastic gene expression model, and observe that the G-Network properly detects the abnormally expressed genes in the simulation study. In the analysis of real data concerning the cell cycle microarray of budding yeast, our approach finds that the steady-state probability of CLB2 is lower than that of other agents, while most of the genes have similar steady-state probabilities. These results lead to the conclusion that the key regulatory genes of the cell cycle can be expressed in the absence of CLB type cyclines, which was also the conclusion of the original microarray experiment study.
G-networks provide an efficient way to monitor steady-state of GRNs. Our method produces more reliable results then the conventional t-test in detecting differentially expressed genes. Also G-networks are successfully applied to the yeast GRNs. This study will be the base of further GRN dynamics studies cooperated with conventional GRN inference algorithms.
PMCID: PMC2788379  PMID: 19958490
18.  Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks 
Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. In addition, a priori biological knowledge from genome-wide location analysis is incorporated into the structure of the gene regulatory network. tdGRN is evaluated on both an artificial dataset and a published gene expression data set. It not only determines regulatory relationships that are known to exist but also uncovers potential new ones. The results indicate that the proposed tool is effective in inferring gene regulatory relationships with time delay. tdGRN is complementary to existing methods for inferring gene regulatory networks. The novel part of the proposed tool is that it is able to infer time-delayed regulatory relationships.
PMCID: PMC3171427  PMID: 19841683
19.  Genotype–phenotype mapping in a post-GWAS world 
Trends in genetics : TIG  2012;28(9):421-426.
Understanding how metabolic reactions, cell signaling, and developmental pathways translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular biology approach directly ties gene function to phenotype through gene regulatory networks (GRNs). Using natural variation in allele-specific expression, GWAS and GRN approaches can be merged into a single framework via structural equation modeling (SEM). This approach leverages the myriad of polymorphisms in natural populations to elucidate and quantitate the molecular pathways that underlie phenotypic variation. The SEM framework can be used to quantitate a GRN, evaluate its consistency across environments or sexes, identify the differences in GRNs between species, and annotate GRNs de novo in non-model organisms.
PMCID: PMC3476940  PMID: 22818580
genotype-to-phenotype map; quantitative variation; allele-specific expression; gene regulatory network; cis-regulatory polymorphism; trans effect
20.  GeNGe: systematic generation of gene regulatory networks 
Bioinformatics  2009;25(9):1205-1207.
Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.
Availability: Available online at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2672627  PMID: 19251773
21.  Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection 
The immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.
We applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.
Our results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.
PMCID: PMC3219564  PMID: 22017961
Gene Regulatory Network; Time Varying Dynamic Bayesian Network; Immune System; Influenza A
Learning or inferring networks of genomic regulation specific to a cellular state, such as a subtype of tumor, can yield insight above and beyond that resulting from network learning-techniques which do not acknowledge the adaptive nature of the cellular system. In this study we show that Cellular Context Mining, which is based on a mathematical model of contextual genomic regulation, produces gene regulatory networks (GRNs) from steady-state expression microarray data which are specific to the varying cellular contexts hidden in the data; we show that these GRNs not only model gene interactions, but that they are also readily annotated with context-specific genomic information. We propose that these context-specific GRNs provide advantages over other techniques, such as clustering and Bayesian networks, when applied to gene expression data of cancer patients.
PMCID: PMC2734457  PMID: 19213132
23.  Cell–cell signaling drives the evolution of complex traits: introduction—lung evo-devo 
Physiology integrates biology with the environment through cell–cell interactions at multiple levels. The evolution of the respiratory system has been “deconvoluted” (Torday and Rehan in Am J Respir Cell Mol Biol 31:8–12, 2004) through Gene Regulatory Networks (GRNs) applied to cell–cell communication for all aspects of lung biology development, homeostasis, regeneration, and aging. Using this approach, we have predicted the phenotypic consequences of failed signaling for lung development, homeostasis, and regeneration based on evolutionary principles. This cell–cell communication model predicts other aspects of vertebrate physiology as adaptational responses. For example, the oxygen-induced differentiation of alveolar myocytes into alveolar adipocytes was critical for the evolution of the lung in land dwelling animals adapting to fluctuating Phanarezoic oxygen levels over the past 500 million years. Adipocytes prevent lung injury due to oxygen radicals and facilitate the rise of endothermy. In addition, they produce the class I cytokine leptin, which augments pulmonary surfactant activity and alveolar surface area, increasing selection pressure for both respiratory oxygenation and metabolic demand initially constrained by high-systemic vascular pressure, but subsequently compensated by the evolution of the adrenomedullary beta-adrenergic receptor mechanism. Conserted positive selection for the lung and adrenals created further selection pressure for the heart, which becomes progressively more complex phylogenetically in tandem with the lung. Developmentally, increasing heart complexity and size impinges precociously on the gut mesoderm to induce the liver. That evolutionary-developmental interaction is significant because the liver provides regulated sources of glucose and glycogen to the evolving physiologic system, which is necessary for the evolution of the neocortex. Evolution of neocortical control furthers integration of physiologic systems. Such an evolutionary vertical integration of cell-to-tissue-to-organ-to-physiology of intrinsic cell–cell signaling and extrinsic factors is the reverse of the “top-down” conventional way in which physiologic systems are usually regarded. This novel mechanistic approach, incorporating a “middle-out” cell–cell signaling component, will lead to a readily available algorithm for integrating genes and phenotypes. This symposium surveyed the phylogenetic origins of such vertically integrated mechanisms for the evolution of cell–cell communication as the basis for complex physiologic traits, from sponges to man.
PMCID: PMC2895351  PMID: 20607136
24.  Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition 
PLoS ONE  2011;6(6):e20560.
A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals.
PMCID: PMC3107214  PMID: 21655103
25.  Oscillatory Protein Expression Dynamics Endows Stem Cells with Robust Differentiation Potential 
PLoS ONE  2011;6(11):e27232.
The lack of understanding of stem cell differentiation and proliferation is a fundamental problem in developmental biology. Although gene regulatory networks (GRNs) for stem cell differentiation have been partially identified, the nature of differentiation dynamics and their regulation leading to robust development remain unclear. Herein, using a dynamical system modeling cell approach, we performed simulations of the developmental process using all possible GRNs with a few genes, and screened GRNs that could generate cell type diversity through cell-cell interactions. We found that model stem cells that both proliferated and differentiated always exhibited oscillatory expression dynamics, and the differentiation frequency of such stem cells was regulated, resulting in a robust number distribution. Moreover, we uncovered the common regulatory motifs for stem cell differentiation, in which a combination of regulatory motifs that generated oscillatory expression dynamics and stabilized distinct cellular states played an essential role. These findings may explain the recently observed heterogeneity and dynamic equilibrium in cellular states of stem cells, and can be used to predict regulatory networks responsible for differentiation in stem cell systems.
PMCID: PMC3207845  PMID: 22073296

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