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Sample records for albicans regulatory network

  1. A Phenotypic Profile of the Candida albicans Regulatory Network

    PubMed Central

    Homann, Oliver R.; Dea, Jeanselle; Noble, Suzanne M.; Johnson, Alexander D.

    2009-01-01

    Candida albicans is a normal resident of the gastrointestinal tract and also the most prevalent fungal pathogen of humans. It last shared a common ancestor with the model yeast Saccharomyces cerevisiae over 300 million years ago. We describe a collection of 143 genetically matched strains of C. albicans, each of which has been deleted for a specific transcriptional regulator. This collection represents a large fraction of the non-essential transcription circuitry. A phenotypic profile for each mutant was developed using a screen of 55 growth conditions. The results identify the biological roles of many individual transcriptional regulators; for many, this work represents the first description of their functions. For example, a quarter of the strains showed altered colony formation, a phenotype reflecting transitions among yeast, pseudohyphal, and hyphal cell forms. These transitions, which have been closely linked to pathogenesis, have been extensively studied, yet our work nearly doubles the number of transcriptional regulators known to influence them. As a second example, nearly a quarter of the knockout strains affected sensitivity to commonly used antifungal drugs; although a few transcriptional regulators have previously been implicated in susceptibility to these drugs, our work indicates many additional mechanisms of sensitivity and resistance. Finally, our results inform how transcriptional networks evolve. Comparison with the existing S. cerevisiae data (supplemented by additional S. cerevisiae experiments reported here) allows the first systematic analysis of phenotypic conservation by orthologous transcriptional regulators over a large evolutionary distance. We find that, despite the many specific wiring changes documented between these species, the general phenotypes of orthologous transcriptional regulator knockouts are largely conserved. These observations support the idea that many wiring changes affect the detailed architecture of the circuit, but

  2. An Expanded Regulatory Network Temporally Controls Candida albicans Biofilm Formation

    PubMed Central

    Fox, Emily P.; Bui, Catherine K.; Nett, Jeniel E.; Hartooni, Nairi; Mui, Michael M.; Andes, David R.; Nobile, Clarissa J.; Johnson, Alexander D.

    2015-01-01

    Summary Candida albicans biofilms are composed of highly adherent and densely arranged cells with properties distinct from those of free-floating (planktonic) cells. These biofilms are a significant medical problem because they commonly form on implanted medical devices, are drug resistant, and are difficult to remove. C. albicans biofilms are not static structures; rather they are dynamic and develop over time. Here we characterize gene expression in biofilms during their development, and by comparing them to multiple planktonic reference states, we identify patterns of gene expression relevant to biofilm formation. In particular, we document time-dependent changes in genes involved in adhesion and metabolism, both of which are at the core of biofilm development. Additionally, we identify three new regulators of biofilm formation, Flo8, Gal4, and Rfx2, which play distinct roles during biofilm development over time. Flo8 is required for biofilm formation at all timepoints, and Gal4 and Rfx2 are needed for proper biofilm formation at intermediate time points. PMID:25784162

  3. An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells

    PubMed Central

    Tierney, Lanay; Linde, Jörg; Müller, Sebastian; Brunke, Sascha; Molina, Juan Camilo; Hube, Bernhard; Schöck, Ulrike; Guthke, Reinhard; Kuchler, Karl

    2012-01-01

    The ability to adapt to diverse micro-environmental challenges encountered within a host is of pivotal importance to the opportunistic fungal pathogen Candida albicans. We have quantified C. albicans and M. musculus gene expression dynamics during phagocytosis by dendritic cells in a genome-wide, time-resolved analysis using simultaneous RNA-seq. A robust network inference map was generated from this dataset using NetGenerator, predicting novel interactions between the host and the pathogen. We experimentally verified predicted interdependent sub-networks comprising Hap3 in C. albicans, and Ptx3 and Mta2 in M. musculus. Remarkably, binding of recombinant Ptx3 to the C. albicans cell wall was found to regulate the expression of fungal Hap3 target genes as predicted by the network inference model. Pre-incubation of C. albicans with recombinant Ptx3 significantly altered the expression of Mta2 target cytokines such as IL-2 and IL-4 in a Hap3-dependent manner, further suggesting a role for Mta2 in host–pathogen interplay as predicted in the network inference model. We propose an integrated model for the functionality of these sub-networks during fungal invasion of immune cells, according to which binding of Ptx3 to the C. albicans cell wall induces remodeling via fungal Hap3 target genes, thereby altering the immune response to the pathogen. We show the applicability of network inference to predict interactions between host–pathogen pairs, demonstrating the usefulness of this systems biology approach to decipher mechanisms of microbial pathogenesis. PMID:22416242

  4. Binding Sites in the EFG1 Promoter for Transcription Factors in a Proposed Regulatory Network: A Functional Analysis in the White and Opaque Phases of Candida albicans.

    PubMed

    Pujol, Claude; Srikantha, Thyagarajan; Park, Yang-Nim; Daniels, Karla J; Soll, David R

    2016-06-01

    In Candida albicans the transcription factor Efg1, which is differentially expressed in the white phase of the white-opaque transition, is essential for expression of the white phenotype. It is one of six transcription factors included in a proposed interactive transcription network regulating white-opaque switching and maintenance of the alternative phenotypes. Ten sites were identified in the EFG1 promoter that differentially bind one or more of the network transcription factors in the white and/or opaque phase. To explore the functionality of these binding sites in the differential expression of EFG1, we generated targeted deletions of each of the 10 binding sites, combinatorial deletions, and regional deletions using a Renilla reniformis luciferase reporter system. Individually targeted deletion of only four of the 10 sites had minor effects consistent with differential expression of EFG1, and only in the opaque phase. Alternative explanations are considered. Copyright © 2016 Pujol et al.

  5. Binding Sites in the EFG1 Promoter for Transcription Factors in a Proposed Regulatory Network: A Functional Analysis in the White and Opaque Phases of Candida albicans

    PubMed Central

    Pujol, Claude; Srikantha, Thyagarajan; Park, Yang-Nim; Daniels, Karla J.; Soll, David R.

    2016-01-01

    In Candida albicans the transcription factor Efg1, which is differentially expressed in the white phase of the white-opaque transition, is essential for expression of the white phenotype. It is one of six transcription factors included in a proposed interactive transcription network regulating white-opaque switching and maintenance of the alternative phenotypes. Ten sites were identified in the EFG1 promoter that differentially bind one or more of the network transcription factors in the white and/or opaque phase. To explore the functionality of these binding sites in the differential expression of EFG1, we generated targeted deletions of each of the 10 binding sites, combinatorial deletions, and regional deletions using a Renilla reniformis luciferase reporter system. Individually targeted deletion of only four of the 10 sites had minor effects consistent with differential expression of EFG1, and only in the opaque phase. Alternative explanations are considered. PMID:27172219

  6. A morphogenetic regulatory role for ethyl alcohol in Candida albicans.

    PubMed

    Chauhan, Nitin M; Raut, Jayant S; Karuppayil, S Mohan

    2011-11-01

    Regulation of morphogenesis through the production of chemical signalling molecules such as isoamyl alcohol, 2-phenylethyl alcohol, 1-dodecanol, E-nerolidol and farnesol is reported in Candida albicans. The present study focuses on the effect of ethyl alcohol on C. albicans dimorphism and biofilm development. Ethyl alcohol inhibited germ tube formation induced by the four standard inducers in a concentration-dependent manner. The germ tube inhibitory concentration (4%) did not have any effect on the growth and viability of C. albicans cells. Ethyl alcohol also inhibited the elongation of germ tubes. Four percentage of ethyl alcohol significantly inhibited biofilm development on polystyrene and silicone surfaces. We suggest a potential morphogenetic regulatory role for ethyl alcohol, which may influence dissemination, virulence and establishment of infection. © 2011 Blackwell Verlag GmbH.

  7. Portrait of Candida Species Biofilm Regulatory Network Genes.

    PubMed

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis.

  8. Global screening of potential Candida albicans biofilm-related transcription factors via network comparison

    PubMed Central

    2010-01-01

    Background Candida albicans is a commonly encountered fungal pathogen in humans. The formation of biofilm is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. Although many factors affecting biofilm have been analyzed, molecular mechanisms that regulate biofilm formation still await to be elucidated. Results In this study, from the gene regulatory network perspective, we developed an efficient computational framework, which integrates different kinds of data from genome-scale analysis, for global screening of potential transcription factors (TFs) controlling C. albicans biofilm formation. S. cerevisiae information and ortholog data were used to infer the possible TF-gene regulatory associations in C. albicans. Based on TF-gene regulatory associations and gene expression profiles, a stochastic dynamic model was employed to reconstruct the gene regulatory networks of C. albicans biofilm and planktonic cells. The two networks were then compared and a score of relevance value (RV) was proposed to determine and assign the quantity of correlation of each potential TF with biofilm formation. A total of twenty-three TFs are identified to be related to the biofilm formation; ten of them are previously reported by literature evidences. Conclusions The results indicate that the proposed screening method can successfully identify most known biofilm-related TFs and also identify many others that have not been previously reported. Together, this method can be employed as a pre-experiment screening approach that reveals new target genes for further characterization to understand the regulatory mechanisms in biofilm formation, which can serve as the starting point for therapeutic intervention of C. albicans infections. PMID:20102611

  9. Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans

    PubMed Central

    Harrison, Paul F.; Lo, Tricia L.; Quenault, Tara; Dagley, Michael J.; Bellousoff, Matthew; Powell, David R.; Beilharz, Traude H.; Traven, Ana

    2015-01-01

    The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability) we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease. PMID:26474309

  10. Understanding genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Kauffman, Stuart

    2003-04-01

    Random Boolean networks (RBM) were introduced about 35 years ago as first crude models of genetic regulatory networks. RBNs are comprised of N on-off genes, connected by a randomly assigned regulatory wiring diagram where each gene has K inputs, and each gene is controlled by a randomly assigned Boolean function. This procedure samples at random from the ensemble of all possible NK Boolean networks. The central ideas are to study the typical, or generic properties of this ensemble, and see 1) whether characteristic differences appear as K and biases in Boolean functions are introducted, and 2) whether a subclass of this ensemble has properties matching real cells. Such networks behave in an ordered or a chaotic regime, with a phase transition, "the edge of chaos" between the two regimes. Networks with continuous variables exhibit the same two regimes. Substantial evidence suggests that real cells are in the ordered regime. A key concept is that of an attractor. This is a reentrant trajectory of states of the network, called a state cycle. The central biological interpretation is that cell types are attractors. A number of properties differentiate the ordered and chaotic regimes. These include the size and number of attractors, the existence in the ordered regime of a percolating "sea" of genes frozen in the on or off state, with a remainder of isolated twinkling islands of genes, a power law distribution of avalanches of gene activity changes following perturbation to a single gene in the ordered regime versus a similar power law distribution plus a spike of enormous avalanches of gene changes in the chaotic regime, and the existence of branching pathway of "differentiation" between attractors induced by perturbations in the ordered regime. Noise is serious issue, since noise disrupts attractors. But numerical evidence suggests that attractors can be made very stable to noise, and meanwhile, metaplasias may be a biological manifestation of noise. As we learn more

  11. Microbial regulatory and metabolic networks.

    PubMed

    Cho, Byung-Kwan; Charusanti, Pep; Herrgård, Markus J; Palsson, Bernhard O

    2007-08-01

    Reconstruction of transcriptional regulatory and metabolic networks is the foundation of large-scale microbial systems and synthetic biology. An enormous amount of information including the annotated genomic sequences and the genomic locations of DNA-binding regulatory proteins can be used to define metabolic and regulatory networks in cells. In particular, advances in experimental methods to map regulatory networks in microbial cells have allowed reliable data-driven reconstruction of these networks. Recent work on metabolic engineering and experimental evolution of microbes highlights the key role of global regulatory networks in controlling specific metabolic processes and the need to consider the integrated function of multiple types of networks for both scientific and engineering purposes.

  12. Structure of the Transcriptional Network Controlling White-Opaque Switching in Candida albicans

    PubMed Central

    Hernday, Aaron D.; Lohse, Matthew B.; Fordyce, Polly M.; Nobile, Clarissa J.; DeRisi, Joseph L.; Johnson, Alexander D.

    2013-01-01

    Summary The human fungal pathogen Candida albicans can switch between two phenotypic cell types, termed “white” and “opaque.” Both cell types are heritable for many generations, and the switch between the two types occurs epigenetically, that is, without a change in the primary DNA sequence of the genome. Previous work identified six key transcriptional regulators important for white-opaque switching: Wor1, Wor2, Wor3, Czf1, Efg1, and Ahr1. In this work, we describe the structure of the transcriptional network that specifies the white and opaque cell types and governs the ability to switch between them. In particular, we use a combination of genome-wide chromatin immunoprecipitation, gene expression profiling, and microfluidics-based DNA binding experiments to determine the direct and indirect regulatory interactions that form the switch network. The six regulators are arranged together in a complex, interlocking network with many seemingly redundant and overlapping connections. We propose that the structure (or topology) of this network is responsible for the epigenetic maintenance of the white and opaque states, the switching between them, and the specialized properties of each state. PMID:23855748

  13. Structure of the transcriptional network controlling white-opaque switching in Candida albicans.

    PubMed

    Hernday, Aaron D; Lohse, Matthew B; Fordyce, Polly M; Nobile, Clarissa J; DeRisi, Joseph L; Johnson, Alexander D

    2013-10-01

    The human fungal pathogen Candida albicans can switch between two phenotypic cell types, termed 'white' and 'opaque'. Both cell types are heritable for many generations, and the switch between the two types occurs epigenetically, that is, without a change in the primary DNA sequence of the genome. Previous work identified six key transcriptional regulators important for white-opaque switching: Wor1, Wor2, Wor3, Czf1, Efg1, and Ahr1. In this work, we describe the structure of the transcriptional network that specifies the white and opaque cell types and governs the ability to switch between them. In particular, we use a combination of genome-wide chromatin immunoprecipitation, gene expression profiling, and microfluidics-based DNA binding experiments to determine the direct and indirect regulatory interactions that form the switch network. The six regulators are arranged together in a complex, interlocking network with many seemingly redundant and overlapping connections. We propose that the structure (or topology) of this network is responsible for the epigenetic maintenance of the white and opaque states, the switching between them, and the specialized properties of each state.

  14. Plant Evolution: Evolving Antagonistic Gene Regulatory Networks.

    PubMed

    Cooper, Endymion D

    2016-06-20

    Developing a structurally complex phenotype requires a complex regulatory network. A new study shows how gene duplication provides a potential source of antagonistic interactions, an important component of gene regulatory networks.

  15. Reconstructing the Prostate Cancer Transcriptional Regulatory Network

    DTIC Science & Technology

    2010-07-01

    TITLE: Reconstructing the prostate cancer transcriptional regulatory network PRINCIPAL INVESTIGATOR: Keyan Salari...CONTRACT NUMBER 4. TITLE AND SUBTITLE Reconstructing the prostate cancer transcriptional regulatory network 5b. GRANT NUMBER W81XWH-09-1...of this study is to reconstruct the prostate cancer transcriptional regulatory network and to experimentally validate novel, clinically-relevant

  16. Cross regulation between Candida albicans catalytic and regulatory subunits of protein kinase A.

    PubMed

    Giacometti, Romina; Kronberg, Florencia; Biondi, Ricardo M; Hernández, Alejandra I; Passeron, Susana

    2012-01-01

    In the pathogen Candida albicans protein kinase A (PKA) catalytic subunit is encoded by two genes TPK1 and TPK2 and the regulatory subunit by one gene, BCY1. PKA mediates several cellular processes such as cell cycle regulation and the yeast to hyphae transition, a key factor for C. albicans virulence. The catalytic isoforms Tpk1p and Tpk2p share redundant functions in vegetative growth and hyphal development, though they differentially regulate glycogen metabolism, the stress response pathway and pseudohyphal formation. In Saccharomyces cerevisiae it was earlier reported that BCY1 overexpression not only increased the amount of TPK3 mRNA but also its catalytic activity. In C. albicans a significant decrease in Bcy1p expression levels was already observed in tpk2Δ null strains. In this work we showed that the upregulation in Bcy1p expression was observed in a set of strains having a TPK1 or TPK2 allele reintegrated in its own locus, as well as in strains expressing the TPKs under the control of the constitutive ACT1 promoter. To confirm the cross regulation event between Bcy1p and Tpkp expression we generated a mutant strain with the lowest PKA activity carrying one TPK1 and a unique BCY1 allele with the aim to obtain two derived strains in which BCY1 or TPK1 were placed under their own promoters inserted in the RPS10 neutral locus. We found that placing one copy of BCY1 upregulated the levels of Tpk1p and its catalytic activity; while TPK1 insertion led to an increase in BCY1 mRNA, Bcy1p and in a high cAMP binding activity. Our results suggest that C. albicans cells were able to compensate for the increased levels of either Tpk1p or Tpk2p subunits with a corresponding elevation of Bcy1 protein levels and vice versa, implying a tightly regulated mechanism to balance holoenzyme formation. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Expression of the CDR1 efflux pump in clinical Candida albicans isolates is controlled by a negative regulatory element

    SciTech Connect

    Gaur, Naseem Akhtar; Manoharlal, Raman; Saini, Preeti; Prasad, Tulika; Mukhopadhyay, Gauranga; Hoefer, Milan; Morschhaeuser, Joachim; Prasad, Rajendra . E-mail: rp47@hotmail.com

    2005-06-24

    Resistance to azole antifungal drugs in clinical isolates of the human fungal pathogen Candida albicans is often caused by constitutive overexpression of the CDR1 gene, which encodes a multidrug efflux pump of the ABC transporter superfamily. To understand the relevance of a recently identified negative regulatory element (NRE) in the CDR1 promoter for the control of CDR1 expression in the clinical scenario, we investigated the effect of mutation or deletion of the NRE on CDR1 expression in two matched pairs of azole-sensitive and resistant clinical isolates of C. albicans. Expression of GFP or lacZ reporter genes from the wild type CDR1 promoter was much higher in the azole-resistant C. albicans isolates than in the azole-susceptible isolates, reflecting the known differences in CDR1 expression in these strains. Deletion or mutation of the NRE resulted in enhanced reporter gene expression in azole-sensitive strains, but did not further increase the already high CDR1 promoter activity in the azole-resistant strains. In agreement with these findings, electrophoretic mobility shift assays showed a reduced binding to the NRE of nuclear extracts from the resistant C. albicans isolates as compared with extracts from the sensitive isolates. These results demonstrate that the NRE is involved in maintaining CDR1 expression at basal levels and that this repression is overcome in azole-resistant clinical C. albicans isolates, resulting in constitutive CDR1 overexpression and concomitant drug resistance.

  18. Boolean Modelingof Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka

    Biological systems form complex networks of interaction on several scales, ranging from the molecular to the ecosystem level. On the subcellular scale, interaction between genes and gene products (mRNAs, proteins) forms the basis of essential processes like signal transduction, cell metabolism or embryonic development. Recent experimental advances helped uncover the qualitative structure of many gene control networks, creating a surge of interest in the quantitative description of gene regulation. We give a brief description of the main frameworks and methods used in modeling gene regulatory networks, then focus on a recent model of the segment polarity genes of the fruit fly Drosophila melanogaster. The basis of this model is the known interactions between the products of the segment polarity genes, and the network topology these interactions form. The interactions between mRNAs and proteins are described as logical (Boolean) functions. The success in reproducing both wild type and mutant gene expression patterns suggests that the kinetic details of the interactions are not essential as long as the network of interactions is unperturbed. The model predicts the gene patterns for cases that were not yet studied experimentally, and implies a remarkable robustness toward changes in internal parameters, initial conditions and even some mutations.

  19. The core regulatory network in human cells.

    PubMed

    Kim, Man-Sun; Kim, Dongsan; Kang, Nam Sook; Kim, Jeong-Rae

    2017-03-04

    In order to discover the common characteristics of various cell types in the human body, many researches have been conducted to find the set of genes commonly expressed in various cell types and tissues. However, the functional characteristics of a cell is determined by the complex regulatory relationships among the genes rather than by expressed genes themselves. Therefore, it is more important to identify and analyze a core regulatory network where all regulatory relationship between genes are active across all cell types to uncover the common features of various cell types. Here, based on hundreds of tissue-specific gene regulatory networks constructed by recent genome-wide experimental data, we constructed the core regulatory network. Interestingly, we found that the core regulatory network is organized by simple cascade and has few complex regulations such as feedback or feed-forward loops. Moreover, we discovered that the regulatory links from genes in the core regulatory network to genes in the peripheral regulatory network are much more abundant than the reverse direction links. These results suggest that the core regulatory network locates at the top of regulatory network and plays a role as a 'hub' in terms of information flow, and the information that is common to all cells can be modified to achieve the tissue-specific characteristics through various types of feedback and feed-forward loops in the peripheral regulatory networks. We also found that the genes in the core regulatory network are evolutionary conserved, essential and non-disease, non-druggable genes compared to the peripheral genes. Overall, our study provides an insight into how all human cells share a common function and generate tissue-specific functional traits by transmitting and processing information through regulatory network.

  20. A genomic regulatory network for development

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; Otim, Ochan; Brown, C. Titus; Livi, Carolina B.; Lee, Pei Yun; Revilla, Roger; Rust, Alistair G.; Pan, Zheng jun; Schilstra, Maria J.; Clarke, Peter J C.; Arnone, Maria I.; Rowen, Lee; Cameron, R. Andrew; McClay, David R.; Hood, Leroy; Bolouri, Hamid

    2002-01-01

    Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-regulatory analysis, and molecular embryology. The network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time.

  1. A genomic regulatory network for development

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; hide

    2002-01-01

    Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-regulatory analysis, and molecular embryology. The network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time.

  2. Hierarchical decomposition of dynamically evolving regulatory networks.

    PubMed

    Ay, Ahmet; Gong, Dihong; Kahveci, Tamer

    2015-05-15

    Gene regulatory networks describe the interplay between genes and their products. These networks control almost every biological activity in the cell through interactions. The hierarchy of genes in these networks as defined by their interactions gives important insights into how these functions are governed. Accurately determining the hierarchy of genes is however a computationally difficult problem. This problem is further complicated by the fact that an intrinsic characteristic of regulatory networks is that the wiring of interactions can change over time. Determining how the hierarchy in the gene regulatory networks changes with dynamically evolving network topology remains to be an unsolved challenge. In this study, we develop a new method, named D-HIDEN (Dynamic-HIerarchical DEcomposition of Networks) to find the hierarchy of the genes in dynamically evolving gene regulatory network topologies. Unlike earlier methods, which recompute the hierarchy from scratch when the network topology changes, our method adapts the hierarchy based on the wiring of the interactions only for the nodes which have the potential to move in the hierarchy. We compare D-HIDEN to five currently available hierarchical decomposition methods on synthetic and real gene regulatory networks. Our experiments demonstrate that D-HIDEN significantly outperforms existing methods in running time, accuracy, or both. Furthermore, our method is robust against dynamic changes in hierarchy. Our experiments on human gene regulatory networks suggest that our method may be used to reconstruct hierarchy in gene regulatory networks.

  3. Evolving Robust Gene Regulatory Networks

    PubMed Central

    Noman, Nasimul; Monjo, Taku; Moscato, Pablo; Iba, Hitoshi

    2015-01-01

    Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems. PMID:25616055

  4. Evolutionary dynamics of prokaryotic transcriptional regulatory networks.

    PubMed

    Madan Babu, M; Teichmann, Sarah A; Aravind, L

    2006-04-28

    The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.

  5. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Foster, D. V.; Kauffman, S. A.; Socolar, J. E. S.

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  6. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as parameters are varied, including the broadening of indegree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  7. A sticky situation: untangling the transcriptional network controlling biofilm development in Candida albicans.

    PubMed

    Fox, Emily P; Nobile, Clarissa J

    2012-01-01

    Candida albicans is a commensal microorganism of the human microbiome; it is also the most prevalent fungal pathogen of humans. Many infections caused by C. albicans are a direct consequence of its proclivity to form biofilms--resilient, surface-associated communities of cells where individual cells acquire specialized properties that are distinct from those observed in suspension cultures. We recently identified the transcriptional network that orchestrates the formation of biofilms in C. albicans. These results set the stage for understanding how biofilms are formed and, once formed, how the specialized properties of biofilms are elaborated. This information will provide new insight for understanding biofilms in more detail and may lead to improvements in preventing and treating biofilm-based infections in the future.

  8. A Recently Evolved Transcriptional Network Controls Biofilm Development in Candida albicans

    PubMed Central

    Nobile, Clarissa J.; Fox, Emily P.; Nett, Jeniel E.; Sorrells, Trevor R.; Mitrovich, Quinn M.; Hernday, Aaron D.; Tuch, Brian B.; Andes, David R.; Johnson, Alexander D.

    2012-01-01

    A biofilm is an organized, resilient group of microbes where individual cells acquire properties, such as drug resistance, that are distinct from those observed in suspension cultures. Here we describe and analyze the transcriptional network controlling biofilm formation in the pathogenic yeast Candida albicans, whose biofilms are a major source of medical device-associated infections. We have combined genetic screens, genome-wide approaches, and two in vivo animal models to describe a master circuit controlling biofilm formation, composed of six transcription regulators that form a tightly woven network with ~1000 target genes. Evolutionary analysis indicates that the biofilm network has rapidly evolved: genes in the biofilm circuit are significantly weighted towards genes that arose relatively recently with ancient genes being underrepresented. This circuit provides a framework for understanding many aspects of biofilm formation by C. albicans in a mammalian host. It also provides insights into how complex cell behaviors can arise from the evolution of transcription circuits. PMID:22265407

  9. Modeling of hysteresis in gene regulatory networks.

    PubMed

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  10. Reconstructing the Prostate Cancer Transcriptional Regulatory Network

    DTIC Science & Technology

    2010-09-01

    TITLE: Reconstructing the prostate cancer transcriptional regulatory network PRINCIPAL INVESTIGATOR: Keyan Salari...2009 – 30 Sep 2010 5a. CONTRACT NUMBER W81XWH-09-1-0414 4. TITLE AND SUBTITLE Reconstructing the prostate cancer transcriptional regulatory...to novel diagnostic, prognostic, and therapeutic strategies in the future. The overall objective of this study was to reconstruct the prostate

  11. ASSEMBLING NEURAL CREST REGULATORY CIRCUITS INTO A GENE REGULATORY NETWORK

    PubMed Central

    Betancur, Paola; Bronner-Fraser, Marianne; Sauka-Spengler, Tatjana

    2014-01-01

    The neural crest is a multipotent stem cell--like population that gives rise to a wide range of derivatives in vertebrate embryo including elements of the craniofacial skeleton and peripheral nervous system as well as melanocytes. The neural crest forms in a series of regulatory steps that include induction and specification of the prospective neural crest territory--neural plate border, specification of bona fide neural crest progenitors, and differentiation into diverse derivatives. These individual processes during neural crest ontogeny are controlled by regulatory circuits that can be assembled into a hierarchical gene regulatory network (GRN). Here we present an overview of the GRN that orchestrates the formation of cranial neural crest cells. Formulation of this network relies on information largely inferred from gene perturbation studies performed in several vertebrate model organisms. Our representation of the cranial neural crest GRN also includes information about direct regulatory interactions obtained from the cis-regulatory analyses performed to date, which increases the resolution of the architectural circuitry within the network. PMID:19575671

  12. Evolutionary rewiring of bacterial regulatory networks

    PubMed Central

    Taylor, Tiffany B.; Mulley, Geraldine; McGuffin, Liam J.; Johnson, Louise J.; Brockhurst, Michael A.; Arseneault, Tanya; Silby, Mark W.; Jackson, Robert W.

    2015-01-01

    Bacteria have evolved complex regulatory networks that enable integration of multiple intracellular and extracellular signals to coordinate responses to environmental changes. However, our knowledge of how regulatory systems function and evolve is still relatively limited. There is often extensive homology between components of different networks, due to past cycles of gene duplication, divergence, and horizontal gene transfer, raising the possibility of cross-talk or redundancy. Consequently, evolutionary resilience is built into gene networks - homology between regulators can potentially allow rapid rescue of lost regulatory function across distant regions of the genome. In our recent study [Taylor, et al. Science (2015), 347(6225)] we find that mutations that facilitate cross-talk between pathways can contribute to gene network evolution, but that such mutations come with severe pleiotropic costs. Arising from this work are a number of questions surrounding how this phenomenon occurs. PMID:28357301

  13. Autonomous Boolean modeling of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Sun, Mengyang; Cheng, Xianrui

    2014-03-01

    In cases where the dynamical properties of gene regulatory networks are important, a faithful model must include three key features: a network topology; a functional response of each element to its inputs; and timing information about the transmission of signals across network links. Autonomous Boolean network (ABN) models are efficient representations of these elements and are amenable to analysis. We present an ABN model of the gene regulatory network governing cell fate specification in the early sea urchin embryo, which must generate three bands of distinct tissue types after several cell divisions, beginning from an initial condition with only two distinct cell types. Analysis of the spatial patterning problem and the dynamics of a network constructed from available experimental results reveals that a simple mechanism is at work in this case. Supported by NSF Grant DMS-10-68602

  14. Analysis of Two Putative Candida albicans Phosphopantothenoylcysteine Decarboxylase / Protein Phosphatase Z Regulatory Subunits Reveals an Unexpected Distribution of Functional Roles

    PubMed Central

    Petrényi, Katalin; Molero, Cristina; Kónya, Zoltán; Erdődi, Ferenc; Ariño, Joaquin; Dombrádi, Viktor

    2016-01-01

    Protein phosphatase Z (Ppz) is a fungus specific enzyme that regulates cell wall integrity, cation homeostasis and oxidative stress response. Work on Saccharomyces cerevisiae has shown that the enzyme is inhibited by Hal3/Vhs3 moonlighting proteins that together with Cab3 constitute the essential phosphopantothenoylcysteine decarboxylase (PPCDC) enzyme. In Candida albicans CaPpz1 is also involved in the morphological changes and infectiveness of this opportunistic human pathogen. To reveal the CaPpz1 regulatory context we searched the C. albicans database and identified two genes that, based on the structure of their S. cerevisiae counterparts, were termed CaHal3 and CaCab3. By pull down analysis and phosphatase assays we demonstrated that both of the bacterially expressed recombinant proteins were able to bind and inhibit CaPpz1 as well as its C-terminal catalytic domain (CaPpz1-Cter) with comparable efficiency. The binding and inhibition were always more pronounced with CaPpz1-Cter, indicating a protective effect against inhibition by the N-terminal domain in the full length protein. The functions of the C. albicans proteins were tested by their overexpression in S. cerevisiae. Contrary to expectations we found that only CaCab3 and not CaHal3 rescued the phenotypic traits that are related to phosphatase inhibition by ScHal3, such as tolerance to LiCl or hygromycin B, requirement for external K+ concentrations, or growth in a MAP kinase deficient slt2 background. On the other hand, both of the Candida proteins turned out to be essential PPCDC components and behaved as their S. cerevisiae counterparts: expression of CaCab3 and CaHal3 rescued the cab3 and hal3 vhs3 S. cerevisiae mutations, respectively. Thus, both CaHal3 and CaCab3 retained the PPCDC related functions and have the potential for CaPpz1 inhibition in vitro. The fact that only CaCab3 exhibits its phosphatase regulatory potential in vivo suggests that in C. albicans CaCab3, but not CaHal3, acts as a

  15. Modeling gene regulatory network motifs using statecharts

    PubMed Central

    2012-01-01

    Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

  16. An iron homeostasis regulatory circuit with reciprocal roles in Candida albicans commensalism and pathogenesis.

    PubMed

    Chen, Changbin; Pande, Kalyan; French, Sarah D; Tuch, Brian B; Noble, Suzanne M

    2011-08-18

    The mammalian gastrointestinal tract and bloodstream are highly disparate biological niches that differ in concentrations of nutrients such as iron. However, some commensal-pathogenic microorganisms, such as the yeast Candida albicans, thrive in both environments. We report the evolution of a transcription circuit in C. albicans that controls iron uptake and determines its fitness in both niches. Our analysis of DNA-binding proteins that regulate iron uptake by this organism suggests the evolutionary intercalation of a transcriptional activator called Sef1 between two broadly conserved iron-responsive transcriptional repressors, Sfu1 and Hap43. Sef1 activates iron-uptake genes and promotes virulence in a mouse model of bloodstream infection, whereas Sfu1 represses iron-uptake genes and is dispensable for virulence but promotes gastrointestinal commensalism. Thus, C. albicans can alternate between genetic programs conferring resistance to iron depletion in the bloodstream versus iron toxicity in the gut, and this may represent a fundamental attribute of gastrointestinal commensal-pathogens.

  17. Splitting Strategy for Simulating Genetic Regulatory Networks

    PubMed Central

    You, Xiong; Liu, Xueping; Musa, Ibrahim Hussein

    2014-01-01

    The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. PMID:24624223

  18. Detecting controlling nodes of boolean regulatory networks.

    PubMed

    Schober, Steffen; Kracht, David; Heckel, Reinhard; Bossert, Martin

    2011-10-11

    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied.

  19. A recently evolved transcriptional network controls biofilm development in Candida albicans.

    PubMed

    Nobile, Clarissa J; Fox, Emily P; Nett, Jeniel E; Sorrells, Trevor R; Mitrovich, Quinn M; Hernday, Aaron D; Tuch, Brian B; Andes, David R; Johnson, Alexander D

    2012-01-20

    A biofilm is an organized, resilient group of microbes in which individual cells acquire properties, such as drug resistance, that are distinct from those observed in suspension cultures. Here, we describe and analyze the transcriptional network controlling biofilm formation in the pathogenic yeast Candida albicans, whose biofilms are a major source of medical device-associated infections. We have combined genetic screens, genome-wide approaches, and two in vivo animal models to describe a master circuit controlling biofilm formation, composed of six transcription regulators that form a tightly woven network with ∼1,000 target genes. Evolutionary analysis indicates that the biofilm network has rapidly evolved: genes in the biofilm circuit are significantly weighted toward genes that arose relatively recently with ancient genes being underrepresented. This circuit provides a framework for understanding many aspects of biofilm formation by C. albicans in a mammalian host. It also provides insights into how complex cell behaviors can arise from the evolution of transcription circuits.

  20. Phenotypic switching in gene regulatory networks.

    PubMed

    Thomas, Philipp; Popović, Nikola; Grima, Ramon

    2014-05-13

    Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

  1. The Mycobacterium tuberculosis regulatory network and hypoxia

    PubMed Central

    Galagan, James E.; Minch, Kyle; Peterson, Matthew; Lyubetskaya, Anna; Azizi, Elham; Sweet, Linsday; Gomes, Antonio; Rustad, Tige; Dolganov, Gregory; Glotova, Irina; Abeel, Thomas; Mahwinney, Chris; Kennedy, Adam D.; Allard, René; Brabant, William; Krueger, Andrew; Jaini, Suma; Honda, Brent; Yu, Wen-Han; Hickey, Mark J.; Zucker, Jeremy; Garay, Christopher; Weiner, Brian; Sisk, Peter; Stolte, Christian; Winkler, Jessica K.; Van de Peer, Yves; Iazzetti, Paul; Camacho, Diogo; Dreyfuss, Jonathan; Liu, Yang; Dorhoi, Anca; Mollenkopf, Hans-Joachim; Drogaris, Paul; Lamontagne, Julie; Zhou, Yiyong; Piquenot, Julie; Park, Sang Tae; Raman, Sahadevan; Kaufmann, Stefan H. E.; Mohney, Robert P.; Chelsky, Daniel; Moody, D. Branch; Sherman, David R.; Schoolnik, Gary K.

    2014-01-01

    We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub. PMID:23823726

  2. The Regulatory Network of Pseudomonas aeruginosa

    PubMed Central

    2011-01-01

    Background Pseudomonas aeruginosa is an important bacterial model due to its metabolic and pathogenic abilities, which allow it to interact and colonize a wide range of hosts, including plants and animals. In this work we compile and analyze the structure and organization of an experimentally supported regulatory network in this bacterium. Results The regulatory network consists of 690 genes and 1020 regulatory interactions between their products (12% of total genes: 54% sigma and 16% of transcription factors). This complex interplay makes the third largest regulatory network of those reported in bacteria. The entire network is enriched for activating interactions and, peculiarly, self-activation seems to occur more prominent for transcription factors (TFs), which contrasts with other biological networks where self-repression is dominant. The network contains a giant component of 650 genes organized into 11 hierarchies, encompassing important biological processes, such as, biofilms formation, production of exopolysaccharide alginate and several virulence factors, and of the so-called quorum sensing regulons. Conclusions The study of gene regulation in P. aeruginosa is biased towards pathogenesis and virulence processes, all of which are interconnected. The network shows power-law distribution -input degree -, and we identified the top ten global regulators, six two-element cycles, the longest paths have ten steps, six biological modules and the main motifs containing three and four elements. We think this work can provide insights for the design of further studies to cover the many gaps in knowledge of this important bacterial model, and for the design of systems strategies to combat this bacterium. PMID:22587778

  3. Modeling Emergence in Neuroprotective Regulatory Networks

    SciTech Connect

    Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.; Stevens, S.L.; Stenzel-Poore, Mary

    2013-01-05

    The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.

  4. A Core Filamentation Response Network in Candida albicans Is Restricted to Eight Genes

    PubMed Central

    Martin, Ronny; Albrecht-Eckardt, Daniela; Brunke, Sascha; Hube, Bernhard; Hünniger, Kerstin; Kurzai, Oliver

    2013-01-01

    Although morphological plasticity is a central virulence trait of Candida albicans, the number of filament-associated genes and the interplay of mechanisms regulating their expression remain unknown. By correlation-based network modeling of the transcriptional response to different defined external stimuli for morphogenesis we identified a set of eight genes with highly correlated expression patterns, forming a core filamentation response. This group of genes included ALS3, ECE1, HGT2, HWP1, IHD1 and RBT1 which are known or supposed to encode for cell- wall associated proteins as well as the Rac1 guanine nucleotide exchange factor encoding gene DCK1 and the unknown function open reading frame orf19.2457. The validity of network modeling was confirmed using a dataset of advanced complexity that describes the transcriptional response of C. albicans during epithelial invasion as well as comparing our results with other previously published transcriptome studies. Although the set of core filamentation response genes was quite small, several transcriptional regulators are involved in the control of their expression, depending on the environmental condition. PMID:23516516

  5. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  6. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  7. Matrix Factorization for Transcriptional Regulatory Network Inference

    PubMed Central

    Ochs, Michael F.; Fertig, Elana J.

    2013-01-01

    Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis. PMID:25364782

  8. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    PubMed

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  9. A Relay Network of Extracellular Heme-Binding Proteins Drives C. albicans Iron Acquisition from Hemoglobin

    PubMed Central

    Kuznets, Galit; Vigonsky, Elena; Weissman, Ziva; Lalli, Daniela; Gildor, Tsvia; Kauffman, Sarah J.; Turano, Paola; Becker, Jeffrey; Lewinson, Oded; Kornitzer, Daniel

    2014-01-01

    Iron scavenging constitutes a crucial challenge for survival of pathogenic microorganisms in the iron-poor host environment. Candida albicans, like many microbial pathogens, is able to utilize iron from hemoglobin, the largest iron pool in the host's body. Rbt5 is an extracellular glycosylphosphatidylinositol (GPI)-anchored heme-binding protein of the CFEM family that facilitates heme-iron uptake by an unknown mechanism. Here, we characterize an additional C. albicans CFEM protein gene, PGA7, deletion of which elicits a more severe heme-iron utilization phenotype than deletion of RBT5. The virulence of the pga7−/− mutant is reduced in a mouse model of systemic infection, consistent with a requirement for heme-iron utilization for C. albicans pathogenicity. The Pga7 and Rbt5 proteins exhibit distinct cell wall attachment, and discrete localization within the cell envelope, with Rbt5 being more exposed than Pga7. Both proteins are shown here to efficiently extract heme from hemoglobin. Surprisingly, while Pga7 has a higher affinity for heme in vitro, we find that heme transfer can occur bi-directionally between Pga7 and Rbt5, supporting a model in which they cooperate in a heme-acquisition relay. Together, our data delineate the roles of Pga7 and Rbt5 in a cell surface protein network that transfers heme from extracellular hemoglobin to the endocytic pathway, and provide a paradigm for how receptors embedded in the cell wall matrix can mediate nutrient uptake across the fungal cell envelope. PMID:25275454

  10. Dynamic simulation of regulatory networks using SQUAD

    PubMed Central

    Di Cara, Alessandro; Garg, Abhishek; De Micheli, Giovanni; Xenarios, Ioannis; Mendoza, Luis

    2007-01-01

    Background The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusion The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or

  11. Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.

    PubMed

    Gonçalves, Joana P; Aires, Ricardo S; Francisco, Alexandre P; Madeira, Sara C

    2012-01-01

    Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched

  12. Adaptation by Plasticity of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Brenner, Naama

    2007-03-01

    Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.

  13. Modeling gene regulatory networks: A network simplification algorithm

    NASA Astrophysics Data System (ADS)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  14. Generation of oscillating gene regulatory network motifs

    NASA Astrophysics Data System (ADS)

    van Dorp, M.; Lannoo, B.; Carlon, E.

    2013-07-01

    Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

  15. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  16. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  17. Invariance kernel of biological regulatory networks.

    PubMed

    Ahmad, Jamil; Roux, Olivier

    2010-01-01

    The analysis of Biological Regulatory Network (BRN) leads to the computing of the set of the possible behaviours of the biological components. These behaviours are seen as trajectories and we are specifically interested in cyclic trajectories since they stand for stability. The set of cycles is given by the so-called invariance kernel of a BRN. This paper presents a method for deriving symbolic formulae for the length, volume and diameter of a cylindrical invariance kernel. These formulae are expressed in terms of delay parameters expressions and give the existence of an invariance kernel and a hint of the number of cyclic trajectories.

  18. Genetic Regulatory Networks in Embryogenesis and Evolution

    NASA Technical Reports Server (NTRS)

    1998-01-01

    The article introduces a series of papers that were originally presented at a workshop titled Genetic Regulatory Network in Embryogenesis and Evaluation. Contents include the following: evolution of cleavage programs in relationship to axial specification and body plan evolution, changes in cell lineage specification elucidate evolutionary relations in spiralia, axial patterning in the leech: developmental mechanisms and evolutionary implications, hox genes in arthropod development and evolution, heterochronic genes in development and evolution, a common theme for LIM homeobox gene function across phylogeny, and mechanisms of specification in ascidian embryos.

  19. Intersecting transcription networks constrain gene regulatory evolution

    PubMed Central

    Sorrells, Trevor R; Booth, Lauren N; Tuch, Brian B; Johnson, Alexander D

    2015-01-01

    Epistasis—the non-additive interactions between different genetic loci—constrains evolutionary pathways, blocking some and permitting others1–8. For biological networks such as transcription circuits, the nature of these constraints and their consequences are largely unknown. Here we describe the evolutionary pathways of a transcription network that controls the response to mating pheromone in yeasts9. A component of this network, the transcription regulator Ste12, has evolved two different modes of binding to a set of its target genes. In one group of species, Ste12 binds to specific DNA binding sites, while in another lineage it occupies DNA indirectly, relying on a second transcription regulator to recognize DNA. We show, through the construction of various possible evolutionary intermediates, that evolution of the direct mode of DNA binding was not directly accessible to the ancestor. Instead, it was contingent on a lineage-specific change to an overlapping transcription network with a different function, the specification of cell type. These results show that analyzing and predicting the evolution of cis-regulatory regions requires an understanding of their positions in overlapping networks, as this placement constrains the available evolutionary pathways. PMID:26153861

  20. Discovering Study-Specific Gene Regulatory Networks

    PubMed Central

    Bo, Valeria; Curtis, Tanya; Lysenko, Artem; Saqi, Mansoor; Swift, Stephen; Tucker, Allan

    2014-01-01

    Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets. PMID:25191999

  1. Chaotic Motifs in Gene Regulatory Networks

    PubMed Central

    Zhang, Zhaoyang; Ye, Weiming; Qian, Yu; Zheng, Zhigang; Huang, Xuhui; Hu, Gang

    2012-01-01

    Chaos should occur often in gene regulatory networks (GRNs) which have been widely described by nonlinear coupled ordinary differential equations, if their dimensions are no less than 3. It is therefore puzzling that chaos has never been reported in GRNs in nature and is also extremely rare in models of GRNs. On the other hand, the topic of motifs has attracted great attention in studying biological networks, and network motifs are suggested to be elementary building blocks that carry out some key functions in the network. In this paper, chaotic motifs (subnetworks with chaos) in GRNs are systematically investigated. The conclusion is that: (i) chaos can only appear through competitions between different oscillatory modes with rivaling intensities. Conditions required for chaotic GRNs are found to be very strict, which make chaotic GRNs extremely rare. (ii) Chaotic motifs are explored as the simplest few-node structures capable of producing chaos, and serve as the intrinsic source of chaos of random few-node GRNs. Several optimal motifs causing chaos with atypically high probability are figured out. (iii) Moreover, we discovered that a number of special oscillators can never produce chaos. These structures bring some advantages on rhythmic functions and may help us understand the robustness of diverse biological rhythms. (iv) The methods of dominant phase-advanced driving (DPAD) and DPAD time fraction are proposed to quantitatively identify chaotic motifs and to explain the origin of chaotic behaviors in GRNs. PMID:22792171

  2. Chaotic motifs in gene regulatory networks.

    PubMed

    Zhang, Zhaoyang; Ye, Weiming; Qian, Yu; Zheng, Zhigang; Huang, Xuhui; Hu, Gang

    2012-01-01

    Chaos should occur often in gene regulatory networks (GRNs) which have been widely described by nonlinear coupled ordinary differential equations, if their dimensions are no less than 3. It is therefore puzzling that chaos has never been reported in GRNs in nature and is also extremely rare in models of GRNs. On the other hand, the topic of motifs has attracted great attention in studying biological networks, and network motifs are suggested to be elementary building blocks that carry out some key functions in the network. In this paper, chaotic motifs (subnetworks with chaos) in GRNs are systematically investigated. The conclusion is that: (i) chaos can only appear through competitions between different oscillatory modes with rivaling intensities. Conditions required for chaotic GRNs are found to be very strict, which make chaotic GRNs extremely rare. (ii) Chaotic motifs are explored as the simplest few-node structures capable of producing chaos, and serve as the intrinsic source of chaos of random few-node GRNs. Several optimal motifs causing chaos with atypically high probability are figured out. (iii) Moreover, we discovered that a number of special oscillators can never produce chaos. These structures bring some advantages on rhythmic functions and may help us understand the robustness of diverse biological rhythms. (iv) The methods of dominant phase-advanced driving (DPAD) and DPAD time fraction are proposed to quantitatively identify chaotic motifs and to explain the origin of chaotic behaviors in GRNs.

  3. Exploring the bZIP transcription factor regulatory network in Neurospora crassa

    PubMed Central

    Tian, Chaoguang; Li, Jingyi; Glass, N. Louise

    2011-01-01

    Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution. PMID:21081763

  4. Topological origin of global attractors in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhang, YunJun; Ouyang, Qi; Geng, Zhi

    2015-02-01

    Fixed-point attractors with global stability manifest themselves in a number of gene regulatory networks. This property indicates the stability of regulatory networks against small state perturbations and is closely related to other complex dynamics. In this paper, we aim to reveal the core modules in regulatory networks that determine their global attractors and the relationship between these core modules and other motifs. This work has been done via three steps. Firstly, inspired by the signal transmission in the regulation process, we extract the model of chain-like network from regulation networks. We propose a module of "ideal transmission chain (ITC)", which is proved sufficient and necessary (under certain condition) to form a global fixed-point in the context of chain-like network. Secondly, by examining two well-studied regulatory networks (i.e., the cell-cycle regulatory networks of Budding yeast and Fission yeast), we identify the ideal modules in true regulation networks and demonstrate that the modules have a superior contribution to network stability (quantified by the relative size of the biggest attraction basin). Thirdly, in these two regulation networks, we find that the double negative feedback loops, which are the key motifs of forming bistability in regulation, are connected to these core modules with high network stability. These results have shed new light on the connection between the topological feature and the dynamic property of regulatory networks.

  5. Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

    PubMed

    Cao, Mu-Shui; Liu, Bing-Ya; Dai, Wen-Tao; Zhou, Wei-Xin; Li, Yi-Xue; Li, Yuan-Yuan

    2015-01-01

    Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing

  6. Information transmission in genetic regulatory networks: a review

    NASA Astrophysics Data System (ADS)

    Tkačik, Gašper; Walczak, Aleksandra M.

    2011-04-01

    Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.

  7. Non-transcriptional regulatory processes shape transcriptional network dynamics.

    PubMed

    Ray, J Christian J; Tabor, Jeffrey J; Igoshin, Oleg A

    2011-10-11

    Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.

  8. Modeling Evolution of Regulatory Networks in Artificial Organisms

    NASA Astrophysics Data System (ADS)

    Sánchez-Dehesa, Yolanda; Beslon, Guillaume; Peña, José-María

    2007-09-01

    Regulatory networks are not randomly connected. They are modular, scale-free networks and some motifs distribution is clearly different from random distribution. However, the evolutionary causes and consequences of this specific connectivity are mainly unknown. In this paper we propose Raevol, an integrative model to study the evolution of regulatory networks. While most existing models consider direct evolution of the regulatory network, Raevol integrates a realistic genotype-phenotype mapping where the genome undergo mutations that indirectly modify the genetic network. Moreover, the organisms are selected at the phenotype level (which is produced by the genome via the regulation network). Thus, in Raevol, the network only indirectly evolve and it can only be selected if its activity influences the phenotype. We plan to use this model to better understand the network evolution and to study the influence of networks topology on evolution.

  9. Browsing Metabolic and Regulatory Networks with BioCyc

    PubMed Central

    Latendresse, Mario; Paley, Suzanne; Karp, Peter D.

    2012-01-01

    Summary The BioCyc database collection at BioCyc.org integrates genome and cellular network information for more than 500 organisms. This method article describes Web-based tools for browsing metabolic and regulatory networks within BioCyc. These tools allow visualization of complete metabolic and regulatory networks, and allow the user to zoom-in on regions of the network of interest. The user can find objects of interest such as genes and metabolites within the networks, and can selectively examine the connectivity of the network. The EcoCyc database within the BioCyc collection has been extensively curated. The descriptions within EcoCyc of the Escherichia coli metabolic network and regulatory network were derived from thousands of publications. Other BioCyc databases received moderate levels of curation, or no curation at all. Those databases receiving no curation contain metabolic networks that were computationally inferred from the annotated genome sequences of each organism. PMID:22144155

  10. Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-01-01

    Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

  11. Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

    PubMed Central

    Chandrasekaran, Sriram; Price, Nathan D.

    2013-01-01

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10−172), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10−14) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework

  12. Estimating Gene Regulatory Networks with pandaR.

    PubMed

    Schlauch, Daniel; Paulson, Joseph N; Young, Albert; Glass, Kimberly; Quackenbush, John

    2017-03-11

    PANDA (Passing Attributes betweenNetworks forData Assimilation) is a gene regulatory network inference method that begins with amodel of transcription factor-target gene interactions and usesmessage passing to update the network model given available transcriptomic and protein-protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks.

  13. Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis

    PubMed Central

    Cao, Mu-Shui; Liu, Bing-Ya; Dai, Wen-Tao; Zhou, Wei-Xin; Li, Yi-Xue; Li, Yuan-Yuan

    2015-01-01

    Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing

  14. Regulatory networks contributing to psoriasis susceptibility.

    PubMed

    Szabó, Kornélia; Bata-Csörgő, Zsuzsanna; Dallos, Attila; Bebes, Attila; Francziszti, László; Dobozy, Attila; Kemény, Lajos; Széll, Márta

    2014-07-01

    The non-involved, healthy-looking skin of psoriatic patients displays inherent characteristics that make it prone to develop typical psoriatic symptoms. Our primary aim was to identify genes and proteins that are differentially regulated in the non-involved psoriatic and the normal epidermis, and to discover regulatory networks responsible for these differences. A cDNA microarray experiment was performed to compare the gene expression profiles of 4 healthy and 4 psoriatic non-involved epidermis samples in response to T-cell lymphokine induction in organotypic cultures. We identified 61 annotated genes and another 11 expressed transcripts that were differentially regulated in the psoriatic tissues. Bioinformatics analysis suggested that the regulation of cell morphology, development and cell death is abnormal, and that the metabolism of small molecules and lipids is differentially regulated in psoriatic epidermis. Our results indicate that one of the early steps of psoriasis pathogenesis may be the abnormal regulation of IL-23A and IL-1B genes in psoriatic keratinocytes.

  15. Improving gene regulatory network inference using network topology information.

    PubMed

    Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P

    2015-09-01

    Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

  16. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  17. Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens

    PubMed Central

    Guthke, Reinhard; Gerber, Silvia; Conrad, Theresia; Vlaic, Sebastian; Durmuş, Saliha; Çakır, Tunahan; Sevilgen, F. E.; Shelest, Ekaterina; Linde, Jörg

    2016-01-01

    In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models. PMID:27148247

  18. A Regulatory Network for Coordinated Flower Maturation

    PubMed Central

    Ploense, Sara E.; Wu, Miin-Feng; Yadav, Vandana; Tholl, Dorothea; Chételat, Aurore; Haupt, Ina; Kennerley, Brian J.; Hodgens, Charles; Farmer, Edward E.; Nagpal, Punita; Reed, Jason W.

    2012-01-01

    For self-pollinating plants to reproduce, male and female organ development must be coordinated as flowers mature. The Arabidopsis transcription factors AUXIN RESPONSE FACTOR 6 (ARF6) and ARF8 regulate this complex process by promoting petal expansion, stamen filament elongation, anther dehiscence, and gynoecium maturation, thereby ensuring that pollen released from the anthers is deposited on the stigma of a receptive gynoecium. ARF6 and ARF8 induce jasmonate production, which in turn triggers expression of MYB21 and MYB24, encoding R2R3 MYB transcription factors that promote petal and stamen growth. To understand the dynamics of this flower maturation regulatory network, we have characterized morphological, chemical, and global gene expression phenotypes of arf, myb, and jasmonate pathway mutant flowers. We found that MYB21 and MYB24 promoted not only petal and stamen development but also gynoecium growth. As well as regulating reproductive competence, both the ARF and MYB factors promoted nectary development or function and volatile sesquiterpene production, which may attract insect pollinators and/or repel pathogens. Mutants lacking jasmonate synthesis or response had decreased MYB21 expression and stamen and petal growth at the stage when flowers normally open, but had increased MYB21 expression in petals of older flowers, resulting in renewed and persistent petal expansion at later stages. Both auxin response and jasmonate synthesis promoted positive feedbacks that may ensure rapid petal and stamen growth as flowers open. MYB21 also fed back negatively on expression of jasmonate biosynthesis pathway genes to decrease flower jasmonate level, which correlated with termination of growth after flowers have opened. These dynamic feedbacks may promote timely, coordinated, and transient growth of flower organs. PMID:22346763

  19. A regulatory network for coordinated flower maturation.

    PubMed

    Reeves, Paul H; Ellis, Christine M; Ploense, Sara E; Wu, Miin-Feng; Yadav, Vandana; Tholl, Dorothea; Chételat, Aurore; Haupt, Ina; Kennerley, Brian J; Hodgens, Charles; Farmer, Edward E; Nagpal, Punita; Reed, Jason W

    2012-02-01

    For self-pollinating plants to reproduce, male and female organ development must be coordinated as flowers mature. The Arabidopsis transcription factors AUXIN RESPONSE FACTOR 6 (ARF6) and ARF8 regulate this complex process by promoting petal expansion, stamen filament elongation, anther dehiscence, and gynoecium maturation, thereby ensuring that pollen released from the anthers is deposited on the stigma of a receptive gynoecium. ARF6 and ARF8 induce jasmonate production, which in turn triggers expression of MYB21 and MYB24, encoding R2R3 MYB transcription factors that promote petal and stamen growth. To understand the dynamics of this flower maturation regulatory network, we have characterized morphological, chemical, and global gene expression phenotypes of arf, myb, and jasmonate pathway mutant flowers. We found that MYB21 and MYB24 promoted not only petal and stamen development but also gynoecium growth. As well as regulating reproductive competence, both the ARF and MYB factors promoted nectary development or function and volatile sesquiterpene production, which may attract insect pollinators and/or repel pathogens. Mutants lacking jasmonate synthesis or response had decreased MYB21 expression and stamen and petal growth at the stage when flowers normally open, but had increased MYB21 expression in petals of older flowers, resulting in renewed and persistent petal expansion at later stages. Both auxin response and jasmonate synthesis promoted positive feedbacks that may ensure rapid petal and stamen growth as flowers open. MYB21 also fed back negatively on expression of jasmonate biosynthesis pathway genes to decrease flower jasmonate level, which correlated with termination of growth after flowers have opened. These dynamic feedbacks may promote timely, coordinated, and transient growth of flower organs.

  20. Variable neighborhood search for reverse engineering of gene regulatory networks.

    PubMed

    Nicholson, Charles; Goodwin, Leslie; Clark, Corey

    2017-01-01

    A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

  1. Construction of gene regulatory networks using biclustering and bayesian networks

    PubMed Central

    2011-01-01

    Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. Results In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. Conclusions Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods. PMID:22018164

  2. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    PubMed

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  3. Network component analysis: reconstruction of regulatory signals in biological systems.

    PubMed

    Liao, James C; Boscolo, Riccardo; Yang, Young-Lyeol; Tran, Linh My; Sabatti, Chiara; Roychowdhury, Vwani P

    2003-12-23

    High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

  4. General trends in the evolution of prokaryotic transcriptional regulatory networks.

    PubMed

    Madan Babu, M; Balaji, S; Aravind, L

    2007-01-01

    Gene expression in organisms is controlled by regulatory proteins termed transcription factors, which recognize and bind to specific nucleotide sequences. Over the years, considerable information has accumulated on the regulatory interactions between transcription factors and their target genes in various model prokaryotes, such as Escherichia coli and Bacillus subtilis. This has allowed the representation of this information in the form of a directed graph, which is commonly referred to as the transcriptional regulatory network. The network representation provides us with an excellent conceptual framework to understand the structure of the transcriptional regulation, both at local and global levels of organization. Several studies suggest that the transcriptional network inferred from model organisms may be approximated by a scale-free topology, which in turn implies the presence of a relatively small group of highly connected regulators (hubs or global regulators). While the graph theoretical principles have been applied to infer various properties of such networks, there have been few studies that have actually investigated the evolution of the transcriptional regulatory networks across diverse organisms. Using recently developed computational methods that exploit various evolutionary principles, we have attempted to reconstruct and compare these networks across a wide-range of prokaryotes. This has provided several insights on the modification and diversification of network structures of various organisms in course of evolution. Firstly, we observed that target genes show a much higher level of conservation than their transcriptional regulators. This in turn suggested that the same set of functions could be differently controlled across diverse organisms, contributing significantly to their adaptive radiations. In particular, at the local level of network structure, organism-specific optimization of the transcription network has evolved primarily via tinkering

  5. Electricity distribution networks: Changing regulatory approaches

    NASA Astrophysics Data System (ADS)

    Cambini, Carlo

    2016-09-01

    Increasing the penetration of distributed generation and smart grid technologies requires substantial investments. A study proposes an innovative approach that combines four regulatory tools to provide economic incentives for distribution system operators to facilitate these innovative practices.

  6. Mapping the Regulatory Network for Salmonella enterica Serovar Typhimurium Invasion

    PubMed Central

    Smith, Carol; Stringer, Anne M.; Mao, Chunhong; Palumbo, Michael J.

    2016-01-01

    ABSTRACT Salmonella enterica pathogenicity island 1 (SPI-1) encodes proteins required for invasion of gut epithelial cells. The timing of invasion is tightly controlled by a complex regulatory network. The transcription factor (TF) HilD is the master regulator of this process and senses environmental signals associated with invasion. HilD activates transcription of genes within and outside SPI-1, including six other TFs. Thus, the transcriptional program associated with host cell invasion is controlled by at least 7 TFs. However, very few of the regulatory targets are known for these TFs, and the extent of the regulatory network is unclear. In this study, we used complementary genomic approaches to map the direct regulatory targets of all 7 TFs. Our data reveal a highly complex and interconnected network that includes many previously undescribed regulatory targets. Moreover, the network extends well beyond the 7 TFs, due to the inclusion of many additional TFs and noncoding RNAs. By comparing gene expression profiles of regulatory targets for the 7 TFs, we identified many uncharacterized genes that are likely to play direct roles in invasion. We also uncovered cross talk between SPI-1 regulation and other regulatory pathways, which, in turn, identified gene clusters that likely share related functions. Our data are freely available through an intuitive online browser and represent a valuable resource for the bacterial research community. PMID:27601571

  7. Optimal finite horizon control in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  8. Env7p Associates with the Golgin Protein Imh1 at the trans-Golgi Network in Candida albicans

    PubMed Central

    Rao, Kongara Hanumantha; Ghosh, Swagata

    2016-01-01

    ABSTRACT Vesicular dynamics is one of the very important aspects of cellular physiology, an imbalance of which leads to the disorders or diseases in higher eukaryotes. We report the functional characterization of a palmitoylated protein kinase from Candida albicans whose homologue in Saccharomyces cerevisiae has been reported to be involved in negative regulation of membrane fusion and was named Env7. However, the downstream target of this protein remains to be identified. Env7 in C. albicans (CaEnv7) could be isolated from the membrane fraction and localized to vesicular structures associated with the Golgi apparatus. Our work reports Env7 in C. albicans as a new player involved in maintaining the functional dynamics at the trans-Golgi network (TGN) by interacting with two other TGN-resident proteins, namely, Imh1p and Arl1p. Direct interaction could be detected between Env7p and the golgin protein Imh1p. Env7 is itself phosphorylated (Env7p) and phosphorylates Imh1 in vivo. An interaction between Env7 and Imh1 is required for the targeted localization of Imh1. CaEnv7 has a putative palmitoylation site toward both N and C termini. An N-terminal palmitoylation-defective strain retains its ability to phosphorylate Imh1 in vitro. An ENV7 homozygous mutant showed compromised filamentation in solid media and attenuated virulence, whereas an overexpressed strain affected cell wall integrity. Thus, Env7 plays a subtle but important role at the level of multitier regulation that exists at the TGN. IMPORTANCE A multitier regulation exists at the trans-Golgi network in all higher organisms. We report a palmitoylated protein kinase, Env7, that functions at the TGN interface by interacting with two more TGN-resident proteins, namely, Imh1 and Arl1. Palmitoylation seems to be important for the specific localization. This study focuses on the involvement of a ubiquitous protein kinase, whose substrates had not yet been reported from any organism, as an upstream signaling

  9. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  10. Phenotype accessibility and noise in random threshold gene regulatory networks.

    PubMed

    Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W

    2014-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes.

  11. Time-Delayed Models of Gene Regulatory Networks

    PubMed Central

    Parmar, K.; Blyuss, K. B.; Kyrychko, Y. N.; Hogan, S. J.

    2015-01-01

    We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems. PMID:26576197

  12. Motor Protein Myo5p Is Required To Maintain the Regulatory Circuit Controlling WOR1 Expression in Candida albicans

    PubMed Central

    Kachurina, Nadezda; Turcotte, Bernard

    2012-01-01

    The Candida albicans MYO5 gene encodes myosin I, a protein required for the formation of germ tubes and true hyphae. Because the polarized growth of opaque-phase cells in response to pheromone results in mating projections that can resemble germ tubes, we examined the role of Myo5p in this process. We localized green fluorescent protein (GFP)-tagged Myo5p in opaque-phase cells of C. albicans during both bud and shmoo formation. In vegetatively growing opaque cells, Myo5p is found at sites of bud emergence and bud growth, while in pheromone-stimulated cells, Myo5p localizes at the growing tips of shmoos. Intriguingly, cells homozygous for MTLa in which the MYO5 gene was deleted failed to switch efficiently from the white phase to the opaque phase, although ectopic expression of WOR1 from the MET3 promoter can convert myo5 mutants into mating-competent opaque cells. However, when WOR1 expression was shut off, the myo5-defective cells rapidly lost both their opaque phenotype and mating competence, suggesting that Myo5p is involved in the maintenance of the opaque state. When MYO5 is expressed conditionally in opaque cells, the opaque phenotype, as well as the mating ability of the cells, becomes unstable under repressive conditions, and quantitative real-time PCR demonstrated that the shutoff of MYO5 expression correlates with a dramatic reduction in WOR1 expression. It appears that while myosin I is not directly required for mating in C. albicans, it is involved in WOR1 expression and the white-opaque transition and thus is indirectly implicated in mating. PMID:22408227

  13. Systems Approaches to Identifying Gene Regulatory Networks in Plants

    PubMed Central

    Long, Terri A.; Brady, Siobhan M.; Benfey, Philip N.

    2009-01-01

    Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed. PMID:18616425

  14. Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks.

    PubMed

    Ben-Tabou de-Leon, Smadar

    2016-01-01

    Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  15. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    PubMed Central

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  16. Efficient reverse-engineering of a developmental gene regulatory network.

    PubMed

    Crombach, Anton; Wotton, Karl R; Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  17. Functional Alignment of Regulatory Networks: A Study of Temperate Phages

    PubMed Central

    Trusina, Ala; Sneppen, Kim; Dodd, Ian B; Shearwin, Keith E; Egan, J. Barry

    2005-01-01

    The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage λ and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective. PMID:16477325

  18. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

    PubMed Central

    Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

    2006-01-01

    A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

  19. A gene regulatory network controlling the embryonic specification of endoderm.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2011-05-29

    Specification of endoderm is the prerequisite for gut formation in the embryogenesis of bilaterian organisms. Modern lineage labelling studies have shown that in the sea urchin embryo model system, descendants of the veg1 and veg2 cell lineages produce the endoderm, and that the veg2 lineage also gives rise to mesodermal cell types. It is known that Wnt/β-catenin signalling is required for endoderm specification and Delta/Notch signalling is required for mesoderm specification. Some direct cis-regulatory targets of these signals have been found and various phenomenological patterns of gene expression have been observed in the pre-gastrular endomesoderm. However, no comprehensive, causal explanation of endoderm specification has been conceived for sea urchins, nor for any other deuterostome. Here we propose a model, on the basis of the underlying genomic control system, that provides such an explanation, built at several levels of biological organization. The hardwired core of the control system consists of the cis-regulatory apparatus of endodermal regulatory genes, which determine the relationship between the inputs to which these genes are exposed and their outputs. The architecture of the network circuitry controlling the dynamic process of endoderm specification then explains, at the system level, a sequence of developmental logic operations, which generate the biological process. The control system initiates non-interacting endodermal and mesodermal gene regulatory networks in veg2-derived cells and extinguishes the endodermal gene regulatory network in mesodermal precursors. It also generates a cross-regulatory network that specifies future anterior endoderm in veg2 descendants and institutes a distinct network specifying posterior endoderm in veg1-derived cells. The network model provides an explanatory framework that relates endoderm specification to the genomic regulatory code.

  20. Gene regulatory networks modelling using a dynamic evolutionary hybrid

    PubMed Central

    2010-01-01

    Background Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. Results The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes. Conclusions The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/. PMID:20298548

  1. Inferring slowly-changing dynamic gene-regulatory networks

    PubMed Central

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with ℓ1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset. PMID:25917062

  2. Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria

    PubMed Central

    2013-01-01

    Background Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group. Results A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported. Conclusions The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov). PMID:23398941

  3. Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

    PubMed Central

    Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

    2014-01-01

    Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

  4. Modelling gene and protein regulatory networks with answer set programming.

    PubMed

    Fayruzov, Timur; Janssen, Jeroen; Vermeir, Dirk; Cornelis, Chris; De Cock, Martine

    2011-01-01

    Recently, many approaches to model regulatory networks have been proposed in the systems biology domain. However, the task is far from being solved. In this paper, we propose an Answer Set Programming (ASP)-based approach to model interaction networks. We build a general ASP framework that describes the network semantics and allows modelling specific networks with little effort. ASP provides a rich and flexible toolbox that allows expanding the framework with desired features. In this paper, we tune our framework to mimic Boolean network behaviour and apply it to model the Budding Yeast and Fission Yeast cell cycle networks. The obtained steady states of these networks correspond to those of the Boolean networks.

  5. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine. PMID:25364745

  6. Characterizing regulatory path motifs in integrated networks using perturbational data

    PubMed Central

    2010-01-01

    We introduce Pathicular http://bioinformatics.psb.ugent.be/software/details/Pathicular, a Cytoscape plugin for studying the cellular response to perturbations of transcription factors by integrating perturbational expression data with transcriptional, protein-protein and phosphorylation networks. Pathicular searches for 'regulatory path motifs', short paths in the integrated physical networks which occur significantly more often than expected between transcription factors and their targets in the perturbational data. A case study in Saccharomyces cerevisiae identifies eight regulatory path motifs and demonstrates their biological significance. PMID:20230615

  7. Statistical inference of regulatory networks for circadian regulation.

    PubMed

    Aderhold, Andrej; Husmeier, Dirk; Grzegorczyk, Marco

    2014-06-01

    We assess the accuracy of various state-of-the-art statistics and machine learning methods for reconstructing gene and protein regulatory networks in the context of circadian regulation. Our study draws on the increasing availability of gene expression and protein concentration time series for key circadian clock components in Arabidopsis thaliana. In addition, gene expression and protein concentration time series are simulated from a recently published regulatory network of the circadian clock in A. thaliana, in which protein and gene interactions are described by a Markov jump process based on Michaelis-Menten kinetics. We closely follow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and the knock-out of various key regulatory genes. Our study provides relative network reconstruction accuracy scores for a critical comparative performance evaluation, and sheds light on a series of highly relevant questions: it quantifies the influence of systematically missing values related to unknown protein concentrations and mRNA transcription rates, it investigates the dependence of the performance on the network topology and the degree of recurrency, it provides deeper insight into when and why non-linear methods fail to outperform linear ones, it offers improved guidelines on parameter settings in different inference procedures, and it suggests new hypotheses about the structure of the central circadian gene regulatory network in A. thaliana.

  8. Learning gene regulatory networks from next generation sequencing data.

    PubMed

    Jia, Bochao; Xu, Suwa; Xiao, Guanghua; Lamba, Vishal; Liang, Faming

    2017-03-10

    In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model-based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.

  9. Recurrent rewiring and emergence of RNA regulatory networks.

    PubMed

    Wilinski, Daniel; Buter, Natascha; Klocko, Andrew D; Lapointe, Christopher P; Selker, Eric U; Gasch, Audrey P; Wickens, Marvin

    2017-04-04

    Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

  10. Reverse engineering of gene regulatory networks: a comparative study.

    PubMed

    Hache, Hendrik; Lehrach, Hans; Herwig, Ralf

    2009-01-01

    Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  11. Identification of key player genes in gene regulatory networks.

    PubMed

    Nazarieh, Maryam; Wiese, Andreas; Will, Thorsten; Hamed, Mohamed; Helms, Volkhard

    2016-09-06

    Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs. Both MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS. This paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds . The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenes and as supplementary material.

  12. Empirical Bayes conditional independence graphs for regulatory network recovery.

    PubMed

    Mahdi, Rami; Madduri, Abishek S; Wang, Guoqing; Strulovici-Barel, Yael; Salit, Jacqueline; Hackett, Neil R; Crystal, Ronald G; Mezey, Jason G

    2012-08-01

    Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. ramimahdi@yahoo.com or jgm45@cornell.edu Supplementary data are available at Bioinformatics online.

  13. The Regulatory Subunit of Protein Kinase A (Bcy1) in Candida albicans Plays Critical Roles in Filamentation and White-Opaque Switching but Is Not Essential for Cell Growth

    PubMed Central

    Ding, Xuefen; Cao, Chengjun; Zheng, Qiushi; Huang, Guanghua

    2017-01-01

    The conserved cAMP-dependent protein kinase (PKA) is composed of the regulatory and catalytic subunits and acts as the central component of the cAMP signaling pathway. In the human fungal pathogen Candida albicans, the PKA regulatory subunit Bcy1 plays a critical role in the regulation of cell differentiation and death. It has long been considered that Bcy1 is essential for cell viability in C. albicans. In the current study, surprisingly, we found that Bcy1 is not required for cell growth, and we successfully generated a bcy1/bcy1 null mutant in C. albicans. Deletion of BCY1 leads to multiple cellular morphologies and promotes the development of filaments. Filamentous and smooth colonies are two typical morphological types of the bcy1/bcy1 mutant, which can undergo spontaneous switching between the two types. Cells of filamentous colonies grow better on a number of different culture media and have a higher survival rate than cells of smooth colonies. In addition, deletion of BCY1 significantly increased the frequency of white-to-opaque switching on N-acetylglucosamine (GlcNAc)-containing medium. The bcy1/bcy1 null mutant generated herein provides the field a new resource to study the biological functions of the cAMP signaling pathway in C. albicans. PMID:28105026

  14. The Regulatory Subunit of Protein Kinase A (Bcy1) in Candida albicans Plays Critical Roles in Filamentation and White-Opaque Switching but Is Not Essential for Cell Growth.

    PubMed

    Ding, Xuefen; Cao, Chengjun; Zheng, Qiushi; Huang, Guanghua

    2016-01-01

    The conserved cAMP-dependent protein kinase (PKA) is composed of the regulatory and catalytic subunits and acts as the central component of the cAMP signaling pathway. In the human fungal pathogen Candida albicans, the PKA regulatory subunit Bcy1 plays a critical role in the regulation of cell differentiation and death. It has long been considered that Bcy1 is essential for cell viability in C. albicans. In the current study, surprisingly, we found that Bcy1 is not required for cell growth, and we successfully generated a bcy1/bcy1 null mutant in C. albicans. Deletion of BCY1 leads to multiple cellular morphologies and promotes the development of filaments. Filamentous and smooth colonies are two typical morphological types of the bcy1/bcy1 mutant, which can undergo spontaneous switching between the two types. Cells of filamentous colonies grow better on a number of different culture media and have a higher survival rate than cells of smooth colonies. In addition, deletion of BCY1 significantly increased the frequency of white-to-opaque switching on N-acetylglucosamine (GlcNAc)-containing medium. The bcy1/bcy1 null mutant generated herein provides the field a new resource to study the biological functions of the cAMP signaling pathway in C. albicans.

  15. Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.

    PubMed

    Doni Jayavelu, Naresh; Bar, Nadav

    2014-01-01

    Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.

  16. Charting gene regulatory networks: strategies, challenges and perspectives

    PubMed Central

    2004-01-01

    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. PMID:15080794

  17. Reconstructing prokaryotic transcriptional regulatory networks: lessons from actinobacteria

    PubMed Central

    Venancio, Thiago M; Aravind, L

    2009-01-01

    Reconstruction of transcriptional regulatory networks of uncharacterized bacteria is a main challenge for the post-genomic era. Recent studies, including one in BMC Systems Biology, address this problem in the relatively underexplored actinobacteria clade, which includes major pathogenic and economically relevant taxa. PMID:19435474

  18. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum

    USDA-ARS?s Scientific Manuscript database

    Head blight caused by Fusarium graminearum (Fg) is a major limiting factor of wheat production with both yield loss and mycotoxin contamination. Here we report a model for global Fg gene regulatory networks (GRNs) inferred from a large collection of transcriptomic data using a machine-learning appro...

  19. Multilevel modeling for inference of genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Ng, Shu-Kay; Wang, Kui; McLachlan, Geoffrey J.

    2005-12-01

    Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

  20. Genomic analysis of regulatory network dynamics reveals large topological changes

    NASA Astrophysics Data System (ADS)

    Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

    2004-09-01

    Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

  1. Fused Regression for Multi-source Gene Regulatory Network Inference

    PubMed Central

    Lam, Kari Y.; Westrick, Zachary M.; Müller, Christian L.; Christiaen, Lionel; Bonneau, Richard

    2016-01-01

    Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms. PMID:27923054

  2. Genomic analysis of regulatory network dynamics reveals large topological changes.

    PubMed

    Luscombe, Nicholas M; Babu, M Madan; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A; Gerstein, Mark

    2004-09-16

    Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.

  3. Genomic analysis of the hierarchical structure of regulatory networks

    PubMed Central

    Yu, Haiyuan; Gerstein, Mark

    2006-01-01

    A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

  4. Mathematical modeling of complex regulatory networks.

    PubMed

    Stelling, Jörg; Gilles, Ernst Dieter

    2004-09-01

    Cellular regulation comprises overwhelmingly complex interactions between genes and proteins that ultimately will only be rendered understandable by employing formal approaches. Developing large-scale mathematical models of such systems in an efficient and reliable way, however, requires careful evaluation of structuring principles for the models, of the description of the system dynamics, and of the experimental data basis for adjusting the models to reality. We discuss these three aspects of model development using the example of cell cycle regulation in yeast and suggest that capturing complex dynamic networks is feasible despite incomplete (quantitative) biological knowledge.

  5. Global analysis of photosynthesis transcriptional regulatory networks.

    PubMed

    Imam, Saheed; Noguera, Daniel R; Donohue, Timothy J

    2014-12-01

    Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis.

  6. Single promoters as regulatory network motifs

    NASA Astrophysics Data System (ADS)

    Zopf, Christopher; Maheshri, Narendra

    2012-02-01

    At eukaryotic promoters, chromatin can influence the relationship between a gene's expression and transcription factor (TF) activity. This additional complexity might allow single promoters to exhibit dynamical behavior commonly attributed to regulatory motifs involving multiple genes. We investigate the role of promoter chromatin architecture in the kinetics of gene activation using a previously described set of promoter variants based on the phosphate-regulated PHO5 promoter in S. cerevisiae. Accurate quantitative measurement of transcription activation kinetics is facilitated by a controllable and observable TF input to a promoter of interest leading to an observable expression output in single cells. We find the particular architecture of these promoters can result in a significant delay in activation, filtering of noisy TF signals, and a memory of previous activation -- dynamical behaviors reminiscent of a feed-forward loop but only requiring a single promoter. We suggest this is a consequence of chromatin transactions at the promoter, likely passing through a long-lived ``primed'' state between its inactive and competent states. Finally, we show our experimental setup can be generalized as a ``gene oscilloscope'' to probe the kinetics of heterologous promoter architectures.

  7. Toward an orofacial gene regulatory network.

    PubMed

    Kousa, Youssef A; Schutte, Brian C

    2016-03-01

    Orofacial clefting is a common birth defect with significant morbidity. A panoply of candidate genes have been discovered through synergy of animal models and human genetics. Among these, variants in interferon regulatory factor 6 (IRF6) cause syndromic orofacial clefting and contribute risk toward isolated cleft lip and palate (1/700 live births). Rare variants in IRF6 can lead to Van der Woude syndrome (1/35,000 live births) and popliteal pterygium syndrome (1/300,000 live births). Furthermore, IRF6 regulates GRHL3 and rare variants in this downstream target can also lead to Van der Woude syndrome. In addition, a common variant (rs642961) in the IRF6 locus is found in 30% of the world's population and contributes risk for isolated orofacial clefting. Biochemical studies revealed that rs642961 abrogates one of four AP-2alpha binding sites. Like IRF6 and GRHL3, rare variants in TFAP2A can also lead to syndromic orofacial clefting with lip pits (branchio-oculo-facial syndrome). The literature suggests that AP-2alpha, IRF6 and GRHL3 are part of a pathway that is essential for lip and palate development. In addition to updating the pathways, players and pursuits, this review will highlight some of the current questions in the study of orofacial clefting.

  8. EXAMINE: a computational approach to reconstructing gene regulatory networks.

    PubMed

    Deng, Xutao; Geng, Huimin; Ali, Hesham

    2005-08-01

    Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously.

  9. Architecture of the human regulatory network derived from ENCODE data.

    PubMed

    Gerstein, Mark B; Kundaje, Anshul; Hariharan, Manoj; Landt, Stephen G; Yan, Koon-Kiu; Cheng, Chao; Mu, Xinmeng Jasmine; Khurana, Ekta; Rozowsky, Joel; Alexander, Roger; Min, Renqiang; Alves, Pedro; Abyzov, Alexej; Addleman, Nick; Bhardwaj, Nitin; Boyle, Alan P; Cayting, Philip; Charos, Alexandra; Chen, David Z; Cheng, Yong; Clarke, Declan; Eastman, Catharine; Euskirchen, Ghia; Frietze, Seth; Fu, Yao; Gertz, Jason; Grubert, Fabian; Harmanci, Arif; Jain, Preti; Kasowski, Maya; Lacroute, Phil; Leng, Jing Jane; Lian, Jin; Monahan, Hannah; O'Geen, Henriette; Ouyang, Zhengqing; Partridge, E Christopher; Patacsil, Dorrelyn; Pauli, Florencia; Raha, Debasish; Ramirez, Lucia; Reddy, Timothy E; Reed, Brian; Shi, Minyi; Slifer, Teri; Wang, Jing; Wu, Linfeng; Yang, Xinqiong; Yip, Kevin Y; Zilberman-Schapira, Gili; Batzoglou, Serafim; Sidow, Arend; Farnham, Peggy J; Myers, Richard M; Weissman, Sherman M; Snyder, Michael

    2012-09-06

    Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

  10. Gene regulatory networks governing haematopoietic stem cell development and identity.

    PubMed

    Pimanda, John E; Göttgens, Berthold

    2010-01-01

    Development can be viewed as a dynamic progression through regulatory states which characterise the various cell types within a given differentiation cascade. To understand the progression of regulatory states that define the origin and subsequent development of haematopoietic stem cells, the first imperative is to understand the ontogeny of haematopoiesis. We are fortunate that the ontogeny of blood development is one of the best characterized mammalian developmental systems. However, the field is still in its infancy with regard to the reconstruction of gene regulatory networks and their interactions with cell signalling cascades that drive a mesodermal progenitor to adopt the identity of a haematopoietic stem cell and beyond. Nevertheless, a framework to dissect these networks and comprehend the logic of its circuitry does exist and although they may not as yet be available, a sense for the tools that will be required to achieve this aim is also emerging. In this review we cover the fundamentals of network architecture, methods used to reconstruct networks, current knowledge of haematopoietic and related transcriptional networks, current challenges and future outlook.

  11. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    PubMed

    Narang, Vipin; Ramli, Muhamad Azfar; Singhal, Amit; Kumar, Pavanish; de Libero, Gennaro; Poidinger, Michael; Monterola, Christopher

    2015-01-01

    Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

  12. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

    PubMed Central

    Singhal, Amit; Kumar, Pavanish; de Libero, Gennaro; Poidinger, Michael; Monterola, Christopher

    2015-01-01

    Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript. PMID:26393364

  13. Dynamics of gene regulatory networks with cell division cycle

    NASA Astrophysics Data System (ADS)

    Chen, Luonan; Wang, Ruiqi; Kobayashi, Tetsuya J.; Aihara, Kazuyuki

    2004-07-01

    This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA , in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac,tetR , and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

  14. The incorporation of epigenetics in artificial gene regulatory networks.

    PubMed

    Turner, Alexander P; Lones, Michael A; Fuente, Luis A; Stepney, Susan; Caves, Leo S D; Tyrrell, Andy M

    2013-05-01

    Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

  15. Statistical ensemble of gene regulatory networks of macrophage differentiation.

    PubMed

    Castiglione, Filippo; Tieri, Paolo; Palma, Alessandro; Jarrah, Abdul Salam

    2016-12-22

    Macrophages cover a major role in the immune system, being the most plastic cell yielding several key immune functions. Here we derived a minimalistic gene regulatory network model for the differentiation of macrophages into the two phenotypes M1 (pro-) and M2 (anti-inflammatory). To test the model, we simulated a large number of such networks as in a statistical ensemble. In other words, to enable the inter-cellular crosstalk required to obtain an immune activation in which the macrophage plays its role, the simulated networks are not taken in isolation but combined with other cellular agents, thus setting up a discrete minimalistic model of the immune system at the microscopic/intracellular (i.e., genetic regulation) and mesoscopic/intercellular scale. We show that within the mesoscopic level description of cellular interaction and cooperation, the gene regulatory logic is coherent and contributes to the overall dynamics of the ensembles that shows, statistically, the expected behaviour.

  16. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

    PubMed Central

    2016-01-01

    With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies. PMID:27597964

  17. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.

    PubMed

    Li, Junyi; Li, Yi-Xue; Li, Yuan-Yuan

    2016-01-01

    With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

  18. Emerging complexity in the denitrification regulatory network of Bradyrhizobium japonicum.

    PubMed

    Torres, María J; Bueno, Emilio; Mesa, Socorro; Bedmar, Eulogio J; Delgado, María J

    2011-01-01

    Bradyrhizobium japonicum is a Gram-negative soil bacterium symbiotically associated with soya bean plants, which is also able to denitrify under free-living and symbiotic conditions. In B. japonicum, the napEDABC, nirK, norCBQD and nosRZDYFLX genes which encode reductases for nitrate, nitrite, nitric oxide and nitrous oxide respectively are required for denitrification. Similar to many other denitrifiers, expression of denitrification genes in B. japonicum requires both oxygen limitation and the presence of nitrate or a derived nitrogen oxide. In B. japonicum, a sophisticated regulatory network consisting of two linked regulatory cascades co-ordinates the expression of genes required for microaerobic respiration (the FixLJ/FixK2 cascade) and for nitrogen fixation (the RegSR/NifA cascade). The involvement of the FixLJ/FixK2 regulatory cascade in the microaerobic induction of the denitrification genes is well established. In addition, the FNR (fumarase and nitrate reduction regulator)/CRP(cAMP receptor protein)-type regulator NnrR expands the FixLJ/FixK2 regulatory cascade by an additional control level. A role for NifA is suggested in this process by recent experiments which have shown that it is required for full expression of denitrification genes in B. japonicum. The present review summarizes the current understanding of the regulatory network of denitrification in B. japonicum.

  19. Global Analysis of Photosynthesis Transcriptional Regulatory Networks

    PubMed Central

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

    2014-01-01

    Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis. PMID:25503406

  20. Gene regulatory networks elucidating huanglongbing disease mechanisms.

    PubMed

    Martinelli, Federico; Reagan, Russell L; Uratsu, Sandra L; Phu, My L; Albrecht, Ute; Zhao, Weixiang; Davis, Cristina E; Bowman, Kim D; Dandekar, Abhaya M

    2013-01-01

    Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

  1. A gene regulatory network armature for T-lymphocyte specification

    SciTech Connect

    Fung, Elizabeth-sharon

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

  2. Conservation of trans-acting networks during mammalian regulatory evolution

    PubMed Central

    Stergachis, Andrew B.; Neph, Shane; Sandstrom, Richard; Haugen, Eric; Reynolds, Alex P.; Zhang, Miaohua; Byron, Rachel; Canfield, Theresa; Stelhing-Sun, Sandra; Lee, Kristen; Thurman, Robert E.; Vong, Shinny; Bates, Daniel; Neri, Fidencio; Diegel, Morgan; Giste, Erika; Dunn, Douglas; Hansen, R. Scott; Johnson, Audra K.; Sabo, Peter J.; Wilken, Matthew S.; Reh, Thomas A.; Treuting, Piper M.; Kaul, Rajinder; Groudine, Mark; Bender, M.A.; Borenstein, Elhanan; Stamatoyannopoulos, John A.

    2014-01-01

    The fundamental body plan and major physiological axes have been highly conserved during mammalian evolution, despite constraint of only a fraction of the human genome sequence. To quantify cis- vs. trans-regulatory contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining >8.6 million TF occupancy sites at nucleotide resolution. Here we show that mouse TF footprints encode a regulatory lexicon of >600 motifs that is >95% similar with that recognized in vivo by human TFs. However, only ~20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape around each TF gene, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Strikingly, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results suggest that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry. PMID:25409825

  3. Gene regulatory network inference using out of equilibrium statistical mechanics

    PubMed Central

    Benecke, Arndt

    2008-01-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem. PMID:19404429

  4. Gene regulatory network inference using out of equilibrium statistical mechanics.

    PubMed

    Benecke, Arndt

    2008-08-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem.

  5. Additive Functions in Boolean Models of Gene Regulatory Network Modules

    PubMed Central

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H.; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  6. Additive functions in boolean models of gene regulatory network modules.

    PubMed

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  7. Identifying genes of gene regulatory networks using formal concept analysis.

    PubMed

    Gebert, Jutta; Motameny, Susanne; Faigle, Ulrich; Forst, Christian V; Schrader, Rainer

    2008-03-01

    In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

  8. Multilayered Control of Alternative Splicing Regulatory Networks by Transcription Factors.

    PubMed

    Han, Hong; Braunschweig, Ulrich; Gonatopoulos-Pournatzis, Thomas; Weatheritt, Robert J; Hirsch, Calley L; Ha, Kevin C H; Radovani, Ernest; Nabeel-Shah, Syed; Sterne-Weiler, Tim; Wang, Juli; O'Hanlon, Dave; Pan, Qun; Ray, Debashish; Zheng, Hong; Vizeacoumar, Frederick; Datti, Alessandro; Magomedova, Lilia; Cummins, Carolyn L; Hughes, Timothy R; Greenblatt, Jack F; Wrana, Jeffrey L; Moffat, Jason; Blencowe, Benjamin J

    2017-02-02

    Networks of coordinated alternative splicing (AS) events play critical roles in development and disease. However, a comprehensive knowledge of the factors that regulate these networks is lacking. We describe a high-throughput system for systematically linking trans-acting factors to endogenous RNA regulatory events. Using this system, we identify hundreds of factors associated with diverse regulatory layers that positively or negatively control AS events linked to cell fate. Remarkably, more than one-third of the regulators are transcription factors. Further analyses of the zinc finger protein Zfp871 and BTB/POZ domain transcription factor Nacc1, which regulate neural and stem cell AS programs, respectively, reveal roles in controlling the expression of specific splicing regulators. Surprisingly, these proteins also appear to regulate target AS programs via binding RNA. Our results thus uncover a large "missing cache" of splicing regulators among annotated transcription factors, some of which dually regulate AS through direct and indirect mechanisms.

  9. Autonomous Boolean modelling of developmental gene regulatory networks

    PubMed Central

    Cheng, Xianrui; Sun, Mengyang; Socolar, Joshua E. S.

    2013-01-01

    During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development. PMID:23034351

  10. Master regulators, regulatory networks, and pathways of glioblastoma subtypes.

    PubMed

    Bozdag, Serdar; Li, Aiguo; Baysan, Mehmet; Fine, Howard A

    2014-01-01

    Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype- specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

  11. Inferring transcription factor collaborations in gene regulatory networks

    PubMed Central

    2014-01-01

    Background Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation. Results We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones. Conclusions mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM. PMID:24565025

  12. Genetic regulatory network models of biological clocks: evolutionary history matters.

    PubMed

    Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

    2008-01-01

    We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites--each involving a finite number of inhibitory and activatory binding factors--is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

  13. Modeling Regulatory Networks to Understand Plant Development: Small Is Beautiful

    PubMed Central

    Middleton, Alistair M.; Farcot, Etienne; Owen, Markus R.; Vernoux, Teva

    2012-01-01

    We now have unprecedented capability to generate large data sets on the myriad genes and molecular players that regulate plant development. Networks of interactions between systems components can be derived from that data in various ways and can be used to develop mathematical models of various degrees of sophistication. Here, we discuss why, in many cases, it is productive to focus on small networks. We provide a brief and accessible introduction to relevant mathematical and computational approaches to model regulatory networks and discuss examples of small network models that have helped generate new insights into plant biology (where small is beautiful), such as in circadian rhythms, hormone signaling, and tissue patterning. We conclude by outlining some of the key technical and modeling challenges for the future. PMID:23110896

  14. Selecting and Weighting Data for Building Consensus Gene Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Steele, Emma; Tucker, Allan

    Microarrays are the major source of data for gene expression activity, allowing the expression of thousands of genes to be measured simultaneously. Gene regulatory networks (GRNs) describe how the expression level of genes affect the expression of the other genes. Modelling GRNs from expression data is a topic of great interest in current bioinformatics research. Previously, we took advantage of publicly available gene expression datasets generated by similar biological studies by drawing together a richer and/or broader collection of data in order to produce GRN models that are more robust, have greater confidence and place less reliance on a single dataset. In this paper a new approach, Weighted Consensus Bayesian Networks, introduces the use of weights in order to place more influence on certain input networks or remove the least reliable networks from the input with encouraging results on both synthetic data and real world yeast microarray datasets.

  15. Are genetically robust regulatory networks dynamically different from random ones?

    NASA Astrophysics Data System (ADS)

    Sevim, Volkan; Rikvold, Per Arne

    We study a genetic regulatory network model developed to demonstrate that genetic robustness can evolve through stabilizing selection for optimal phenotypes. We report preliminary results on whether such selection could result in a reorganization of the state space of the system. For the chosen parameters, the evolution moves the system slightly toward the more ordered part of the phase diagram. We also find that strong memory effects cause the Derrida annealed approximation to give erroneous predictions about the model's phase diagram.

  16. Gene regulatory networks in the early ascidian embryo.

    PubMed

    Satou, Yutaka; Satoh, Nori; Imai, Kaoru S

    2009-04-01

    Ascidians, or sea squirts, are tunicates that diverged from the vertebrate lineage early in the chordate evolution. The compact and simple organization of the ascidian genome makes this organism an ideal model system for analyzing gene regulatory networks in embryonic development. Embryos contain relatively few cells and gene activities by individual cells have been determined. Here we review and discuss advances in our understanding of the ascidian embryogenesis emerging from genomic expression studies and analyses at the single cell level.

  17. Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic

    DTIC Science & Technology

    2013-03-01

    Researchers can target specific genes or proteins in signal transduction pathways to cure a number of diseases and disorders, with applications in...medication and drug delivery. Genetic Regulatory Networks (GRNs) represent the signal transduction, or the activation and deactivation of genes , as...intelligence, prolog, gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a

  18. Analyzing stationary states of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2006-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  19. Analyzing stationary States of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2011-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  20. Evolution of the mammalian embryonic pluripotency gene regulatory network

    PubMed Central

    Fernandez-Tresguerres, Beatriz; Cañon, Susana; Rayon, Teresa; Pernaute, Barbara; Crespo, Miguel; Torroja, Carlos; Manzanares, Miguel

    2010-01-01

    Embryonic pluripotency in the mouse is established and maintained by a gene-regulatory network under the control of a core set of transcription factors that include octamer-binding protein 4 (Oct4; official name POU domain, class 5, transcription factor 1, Pou5f1), sex-determining region Y (SRY)-box containing gene 2 (Sox2), and homeobox protein Nanog. Although this network is largely conserved in eutherian mammals, very little information is available regarding its evolutionary conservation in other vertebrates. We have compared the embryonic pluripotency networks in mouse and chick by means of expression analysis in the pregastrulation chicken embryo, genomic comparisons, and functional assays of pluripotency-related regulatory elements in ES cells and blastocysts. We find that multiple components of the network are either novel to mammals or have acquired novel expression domains in early developmental stages of the mouse. We also find that the downstream action of the mouse core pluripotency factors is mediated largely by genomic sequence elements nonconserved with chick. In the case of Sox2 and Fgf4, we find that elements driving expression in embryonic pluripotent cells have evolved by a small number of nucleotide changes that create novel binding sites for core factors. Our results show that the network in charge of embryonic pluripotency is an evolutionary novelty of mammals that is related to the comparatively extended period during which mammalian embryonic cells need to be maintained in an undetermined state before engaging in early differentiation events. PMID:21048080

  1. Evolution of the mammalian embryonic pluripotency gene regulatory network.

    PubMed

    Fernandez-Tresguerres, Beatriz; Cañon, Susana; Rayon, Teresa; Pernaute, Barbara; Crespo, Miguel; Torroja, Carlos; Manzanares, Miguel

    2010-11-16

    Embryonic pluripotency in the mouse is established and maintained by a gene-regulatory network under the control of a core set of transcription factors that include octamer-binding protein 4 (Oct4; official name POU domain, class 5, transcription factor 1, Pou5f1), sex-determining region Y (SRY)-box containing gene 2 (Sox2), and homeobox protein Nanog. Although this network is largely conserved in eutherian mammals, very little information is available regarding its evolutionary conservation in other vertebrates. We have compared the embryonic pluripotency networks in mouse and chick by means of expression analysis in the pregastrulation chicken embryo, genomic comparisons, and functional assays of pluripotency-related regulatory elements in ES cells and blastocysts. We find that multiple components of the network are either novel to mammals or have acquired novel expression domains in early developmental stages of the mouse. We also find that the downstream action of the mouse core pluripotency factors is mediated largely by genomic sequence elements nonconserved with chick. In the case of Sox2 and Fgf4, we find that elements driving expression in embryonic pluripotent cells have evolved by a small number of nucleotide changes that create novel binding sites for core factors. Our results show that the network in charge of embryonic pluripotency is an evolutionary novelty of mammals that is related to the comparatively extended period during which mammalian embryonic cells need to be maintained in an undetermined state before engaging in early differentiation events.

  2. Modeling stochasticity and variability in gene regulatory networks.

    PubMed

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Arat, Seda; Laubenbacher, Reinhard

    2012-06-06

    Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.

  3. Modeling stochasticity and robustness in gene regulatory networks

    PubMed Central

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

    2009-01-01

    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 http://si2.epfl.ch/∼garg/genysis.html. Contact: abhishek.garg@epfl.ch PMID:19477975

  4. Modeling stochasticity and robustness in gene regulatory networks.

    PubMed

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

    2009-06-15

    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. 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. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

  5. Modeling stochasticity and variability in gene regulatory networks

    PubMed Central

    2012-01-01

    Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex. PMID:22673395

  6. Gap Gene Regulatory Dynamics Evolve along a Genotype Network

    PubMed Central

    Crombach, Anton; Wotton, Karl R.; Jiménez-Guri, Eva; Jaeger, Johannes

    2016-01-01

    Developmental gene networks implement the dynamic regulatory mechanisms that pattern and shape the organism. Over evolutionary time, the wiring of these networks changes, yet the patterning outcome is often preserved, a phenomenon known as “system drift.” System drift is illustrated by the gap gene network—involved in segmental patterning—in dipteran insects. In the classic model organism Drosophila melanogaster and the nonmodel scuttle fly Megaselia abdita, early activation and placement of gap gene expression domains show significant quantitative differences, yet the final patterning output of the system is essentially identical in both species. In this detailed modeling analysis of system drift, we use gene circuits which are fit to quantitative gap gene expression data in M. abdita and compare them with an equivalent set of models from D. melanogaster. The results of this comparative analysis show precisely how compensatory regulatory mechanisms achieve equivalent final patterns in both species. We discuss the larger implications of the work in terms of “genotype networks” and the ways in which the structure of regulatory networks can influence patterns of evolutionary change (evolvability). PMID:26796549

  7. Dynamic Gene Regulatory Networks of Human Myeloid Differentiation.

    PubMed

    Ramirez, Ricardo N; El-Ali, Nicole C; Mager, Mikayla Anne; Wyman, Dana; Conesa, Ana; Mortazavi, Ali

    2017-03-27

    The reconstruction of gene regulatory networks underlying cell differentiation from high-throughput gene expression and chromatin data remains a challenge. Here, we derive dynamic gene regulatory networks for human myeloid differentiation using a 5-day time series of RNA-seq and ATAC-seq data. We profile HL-60 promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. We find a rapid response in the expression of key transcription factors and lineage markers that only regulate a subset of their targets at a given time, which is followed by chromatin accessibility changes that occur later along with further gene expression changes. We observe differences between promyelocyte- and monocyte-derived macrophages at both the transcriptional and chromatin landscape level, despite using the same differentiation stimulus, which suggest that the path taken by cells in the differentiation landscape defines their end cell state. More generally, our approach of combining neighboring time points and replicates to achieve greater sequencing depth can efficiently infer footprint-based regulatory networks from long series data.

  8. Phase transitions in the evolution of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Skanata, Antun; Kussell, Edo

    The role of gene regulatory networks is to respond to environmental conditions and optimize growth of the cell. A typical example is found in bacteria, where metabolic genes are activated in response to nutrient availability, and are subsequently turned off to conserve energy when their specific substrates are depleted. However, in fluctuating environmental conditions, regulatory networks could experience strong evolutionary pressures not only to turn the right genes on and off, but also to respond optimally under a wide spectrum of fluctuation timescales. The outcome of evolution is predicted by the long-term growth rate, which differentiates between optimal strategies. Here we present an analytic computation of the long-term growth rate in randomly fluctuating environments, by using mean-field and higher order expansion in the environmental history. We find that optimal strategies correspond to distinct regions in the phase space of fluctuations, separated by first and second order phase transitions. The statistics of environmental randomness are shown to dictate the possible evolutionary modes, which either change the structure of the regulatory network abruptly, or gradually modify and tune the interactions between its components.

  9. An algebra-based method for inferring gene regulatory networks.

    PubMed

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  10. An algebra-based method for inferring gene regulatory networks

    PubMed Central

    2014-01-01

    Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also

  11. From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases.

    PubMed

    de la Fuente, Alberto

    2010-07-01

    Understanding diseases requires identifying the differences between healthy and affected tissues. Gene expression data have revolutionized the study of diseases by making it possible to simultaneously consider thousands of genes. The identification of disease-associated genes requires studying the genes in the context of the regulatory systems they are involved in. A major goal is to identify specific regulatory networks that are dysfunctional in a given disease state. Although we still have not reached a stage where the elucidation of differential regulatory networks is commonly feasible, recent advances have described the first steps towards this goal - the identification of differential coexpression networks. This review describes the shift from differential gene expression to differential networking and outlines how this shift will affect the study of the genetic basis of disease.

  12. Modularity and evolutionary constraints in a baculovirus gene regulatory network

    PubMed Central

    2013-01-01

    Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Results We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. Conclusions Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates

  13. Social networks and life satisfaction: The interplay of network density and regulatory focus.

    PubMed

    Zou, Xi; Ingram, Paul; Higgins, E Tory

    We propose that an individual's regulatory focus moderates the significant role social network density-the degree of interconnectedness among a person's social contacts-plays in shaping life satisfaction. Evidence from Study 1 indicates that participants with high prevention effectiveness reported higher life satisfaction when they were embedded in a high-density network, whereas participants with low promotion effectiveness reported lower life satisfaction when they were embedded in a low-density network. Study 2 further specifies the underlying mechanism, namely that participants with high prevention effectiveness are more likely to obtain support for meeting obligations and responsibilities when they are embedded in a high-density network, whereas participants with low promotion effectiveness suffer from the support for creative inspiration and personal development in a low-density network (by highlighting their promotion failure). Implications for studying the interplay between social networks and individuals' self-regulatory motives are discussed.

  14. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors

    PubMed Central

    Molodtsova, Daria; Harpur, Brock A.; Kent, Clement F.; Seevananthan, Kajendra; Zayed, Amro

    2014-01-01

    It is increasingly apparent that genes and networks that influence complex behavior are evolutionary conserved, which is paradoxical considering that behavior is labile over evolutionary timescales. How does adaptive change in behavior arise if behavior is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behavior, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behavior of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behavior can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network. PMID:25566318

  15. Identifying gene regulatory network rewiring using latent differential graphical models

    PubMed Central

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-01-01

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions. PMID:27378774

  16. Stochastic S-system modeling of gene regulatory network.

    PubMed

    Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob

    2015-10-01

    Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.

  17. Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.

    PubMed

    Manioudaki, Maria E; Poirazi, Panayiota

    2013-01-01

    Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their

  18. Innovation and robustness in complex regulatory gene networks

    PubMed Central

    Ciliberti, S.; Martin, O. C.; Wagner, A.

    2007-01-01

    The history of life involves countless evolutionary innovations, a steady stream of ingenuity that has been flowing for more than 3 billion years. Very little is known about the principles of biological organization that allow such innovation. Here, we examine these principles for evolutionary innovation in gene expression patterns. To this end, we study a model for the transcriptional regulation networks that are at the heart of embryonic development. A genotype corresponds to a regulatory network of a given topology, and a phenotype corresponds to a steady-state gene expression pattern. Networks with the same phenotype form a connected graph in genotype space, where two networks are immediate neighbors if they differ by one regulatory interaction. We show that an evolutionary search on this graph can reach genotypes that are as different from each other as if they were chosen at random in genotype space, allowing evolutionary access to different kinds of innovation while staying close to a viable phenotype. Thus, although robustness to mutations may hinder innovation in the short term, we conclude that long-term innovation in gene expression patterns can only emerge in the presence of the robustness caused by connected genotype graphs. PMID:17690244

  19. Regulatory Networks:. Inferring Functional Relationships Through Co-Expression

    NASA Astrophysics Data System (ADS)

    Wanke, Dierk; Hahn, Achim; Kilian, Joachim; Harter, Klaus; Berendzen, Kenneth W.

    2010-01-01

    Gene expression data not only provide us insights into discrete transcript abundance of specific genes, but contain cryptic information that can not readily be assessed without interpretation. We again used data of the plant Arabidopsis thaliana as our reference organism, yet the analysis presented herein can be performed with any organism with various data sources. Within the cell, information is transduced via different signaling cascades and results in differential gene expression responses. The incoming signals are perceived from upstream signaling components and handed to downstream messengers that further deliver the signals to effector proteins which can directly influence gene expression. In most cases, we can assume that proteins, which are connected to other signaling components within such a regulatory network, exhibit similar expression trajectories. Thus, we extracted a known functional network from literature and demonstrated that it is possible to superimpose microarray expression data onto the pathways. Thereby, we could follow the information flow through time reflected by gene expression changes. This allowed us to predict, whether the upstream signal was transmitted from known elements contained in the network or relayed from outside components. We next conducted the vice versa approach and used large scale microarray expression data to build a co-expression matrix for all genes present on the array. From this, we computed a regulatory network, which allowed us to deduce known and novel signaling pathways.

  20. Minimum network constraint on reverse engineering to develop biological regulatory networks.

    PubMed

    Shao, Bin; Wu, Jiayi; Tian, Binghui; Ouyang, Qi

    2015-09-07

    Reconstructing the topological structure of biological regulatory networks from microarray expression data or data of protein expression profiles is one of major tasks in systems biology. In recent years, various mathematical methods have been developed to meet this task. Here, based on our previously reported reverse engineering method, we propose a new constraint, i.e., the minimum network constraint, to facilitate the reconstruction of biological networks. Three well studied regulatory networks (the budding yeast cell cycle network, the fission yeast cell cycle network, and the SOS network of Escherichia coli) were used as the test sets to verify the performance of this method. Numerical results show that the biological networks prefer to use the minimal networks to fulfill their functional tasks, making it possible to apply minimal network criteria in the network reconstruction process. Two scenarios were considered in the reconstruction process: generating data using different initial conditions; and generating data from knock out and over-expression experiments. In both cases, network structures are revealed faithfully in a few steps using our approach.

  1. Inferring orthologous gene regulatory networks using interspecies data fusion

    PubMed Central

    Penfold, Christopher A.; Millar, Jonathan B. A.; Wild, David L.

    2015-01-01

    Motivation: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved ‘hypernetwork’. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression. Results: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase. Availability and implementation: MATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/. Contact: d.l.wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics

  2. A Versatile Overexpression Strategy in the Pathogenic Yeast Candida albicans: Identification of Regulators of Morphogenesis and Fitness

    PubMed Central

    Cabral, Vitor; Znaidi, Sadri; Goyard, Sophie; Bachellier-Bassi, Sophie; Firon, Arnaud; Legrand, Mélanie; Diogo, Dorothée; Naulleau, Claire; Rossignol, Tristan; d’Enfert, Christophe

    2012-01-01

    Candida albicans is the most frequently encountered human fungal pathogen, causing both superficial infections and life-threatening systemic diseases. Functional genomic studies performed in this organism have mainly used knock-out mutants and extensive collections of overexpression mutants are still lacking. Here, we report the development of a first generation C. albicans ORFeome, the improvement of overexpression systems and the construction of two new libraries of C. albicans strains overexpressing genes for components of signaling networks, in particular protein kinases, protein phosphatases and transcription factors. As a proof of concept, we screened these collections for genes whose overexpression impacts morphogenesis or growth rates in C. albicans. Our screens identified genes previously described for their role in these biological processes, demonstrating the functionality of our strategy, as well as genes that have not been previously associated to these processes. This article emphasizes the potential of systematic overexpression strategies to improve our knowledge of regulatory networks in C. albicans. The C. albicans plasmid and strain collections described here are available at the Fungal Genetics Stock Center. Their extension to a genome-wide scale will represent important resources for the C. albicans community. PMID:23049891

  3. The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks.

    PubMed

    Emmert-Streib, Frank; de Matos Simoes, Ricardo; Mullan, Paul; Haibe-Kains, Benjamin; Dehmer, Matthias

    2014-01-01

    In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

  4. Dynamical modeling and analysis of large cellular regulatory networks

    NASA Astrophysics Data System (ADS)

    Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

    2013-06-01

    The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

  5. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks.

    PubMed

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J; Litchfield, David W

    2017-03-05

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks.

  6. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks

    PubMed Central

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J.; Litchfield, David W.

    2017-01-01

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks. PMID:28273877

  7. Regulatory Compliance in Multi-Tier Supplier Networks

    NASA Technical Reports Server (NTRS)

    Goossen, Emray R.; Buster, Duke A.

    2014-01-01

    Over the years, avionics systems have increased in complexity to the point where 1st tier suppliers to an aircraft OEM find it financially beneficial to outsource designs of subsystems to 2nd tier and at times to 3rd tier suppliers. Combined with challenging schedule and budgetary pressures, the environment in which safety-critical systems are being developed introduces new hurdles for regulatory agencies and industry. This new environment of both complex systems and tiered development has raised concerns in the ability of the designers to ensure safety considerations are fully addressed throughout the tier levels. This has also raised questions about the sufficiency of current regulatory guidance to ensure: proper flow down of safety awareness, avionics application understanding at the lower tiers, OEM and 1st tier oversight practices, and capabilities of lower tier suppliers. Therefore, NASA established a research project to address Regulatory Compliance in a Multi-tier Supplier Network. This research was divided into three major study efforts: 1. Describe Modern Multi-tier Avionics Development 2. Identify Current Issues in Achieving Safety and Regulatory Compliance 3. Short-term/Long-term Recommendations Toward Higher Assurance Confidence This report presents our findings of the risks, weaknesses, and our recommendations. It also includes a collection of industry-identified risks, an assessment of guideline weaknesses related to multi-tier development of complex avionics systems, and a postulation of potential modifications to guidelines to close the identified risks and weaknesses.

  8. Genomic Reconstruction of the Transcriptional Regulatory Network in Bacillus subtilis

    PubMed Central

    Leyn, Semen A.; Kazanov, Marat D.; Sernova, Natalia V.; Ermakova, Ekaterina O.; Novichkov, Pavel S.

    2013-01-01

    The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database (http://regprecise.lbl.gov/) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis. PMID:23504016

  9. Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

    PubMed

    Kalender Atak, Zeynep; Imrichova, Hana; Svetlichnyy, Dmitry; Hulselmans, Gert; Christiaens, Valerie; Reumers, Joke; Ceulemans, Hugo; Aerts, Stein

    2017-08-30

    The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .

  10. Reconstruction of transcriptional regulatory networks by stability-based network component analysis.

    PubMed

    Chen, Xi; Xuan, Jianhua; Wang, Chen; Shajahan, Ayesha N; Riggins, Rebecca B; Clarke, Robert

    2013-01-01

    Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performance of NCA is impaired by the high rate of false connections in binding information. In this paper, we integrate stability analysis with NCA to form a novel scheme, namely stability-based NCA (sNCA), for regulatory network identification. The method mainly addresses the inconsistency between gene expression data and binding motif information. Small perturbations are introduced to prior regulatory network, and the distance among multiple estimated transcript factor (TF) activities is computed to reflect the stability for each TF's binding network. For target gene identification, multivariate regression and t-statistic are used to calculate the significance for each TF-gene connection. Simulation studies are conducted and the experimental results show that sNCA can achieve an improved and robust performance in TF identification as compared to NCA. The approach for target gene identification is also demonstrated to be suitable for identifying true connections between TFs and their target genes. Furthermore, we have successfully applied sNCA to breast cancer data to uncover the role of TFs in regulating endocrine resistance in breast cancer.

  11. Implications of Developmental Gene Regulatory Networks Inside and Outside Developmental Biology.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2016-01-01

    The insight that the genomic control of developmental process is encoded in the form of gene regulatory networks has profound impacts on many areas of modern bioscience. Most importantly, it affects developmental biology itself, as it means that a causal understanding of development requires knowledge of the architecture of regulatory network interactions. Furthermore, it follows that functional changes in developmental gene regulatory networks have to be considered as a primary mechanism for evolutionary process. We here discuss some of the recent advances in gene regulatory network biology and how they have affected our current understanding of development, evolution, and regulatory genomics.

  12. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

    PubMed Central

    Goode, Debbie K.; Obier, Nadine; Vijayabaskar, M.S.; Lie-A-Ling, Michael; Lilly, Andrew J.; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A.; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R.; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-01-01

    Summary Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

  13. Signaling and Gene Regulatory Networks in Mammalian Lens Development.

    PubMed

    Cvekl, Ales; Zhang, Xin

    2017-10-01

    Ocular lens development represents an advantageous system in which to study regulatory mechanisms governing cell fate decisions, extracellular signaling, cell and tissue organization, and the underlying gene regulatory networks. Spatiotemporally regulated domains of BMP, FGF, and other signaling molecules in late gastrula-early neurula stage embryos generate the border region between the neural plate and non-neural ectoderm from which multiple cell types, including lens progenitor cells, emerge and undergo initial tissue formation. Extracellular signaling and DNA-binding transcription factors govern lens and optic cup morphogenesis. Pax6, c-Maf, Hsf4, Prox1, Sox1, and a few additional factors regulate the expression of the lens structural proteins, the crystallins. Extensive crosstalk between a diverse array of signaling pathways controls the complexity and order of lens morphogenetic processes and lens transparency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior

    PubMed Central

    Tran, Van; McCall, Matthew N.; McMurray, Helene R.; Almudevar, Anthony

    2013-01-01

    Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation while maintaining their Markovian nature increases the applicability of these models to genetic regulatory networks (GRN). We explore a previously-proposed class of BoNs characterized by linear threshold functions, which we refer to as threshold Boolean networks (TBN). Compared to traditional BoNs with unconstrained transition functions, these models require far fewer parameters and offer a more direct interpretation. However, the functional form of a TBN does result in a reduction in the regulatory relationships which can be modeled. We show that TBNs can be readily extended to permit self-degradation, with explicitly modeled degradation rates. We note that the introduction of variable degradation compromises the Markovian property fundamental to BoN models but show that a simple state augmentation procedure restores their Markovian nature. Next, we study the effect of assumptions regarding self-degradation on the set of possible steady states. Our findings are captured in two theorems relating self-degradation and regulatory feedback to the steady state behavior of a TBN. Finally, we explore assumptions of synchronous gene response and asynergistic regulation and show that TBNs can be easily extended to relax these assumptions. Applying our methods to the budding yeast cell-cycle network revealed that although the network is complex, its steady state is simplified by the presence of self-degradation and lack of purely positive regulatory cycles. PMID:24376454

  15. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    DOE PAGES

    Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; ...

    2015-06-01

    The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond thosemore » of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.« less

  16. Topological effects of data incompleteness of gene regulatory networks

    PubMed Central

    2012-01-01

    Background The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly. Results In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different levels. Conclusions In doing so, we identify the most relevant factors affecting the validity of previous findings, highlighting important caveats to future prokaryotic TRNs topological analysis. PMID:22920968

  17. A Maize Gene Regulatory Network for Phenolic Metabolism.

    PubMed

    Yang, Fan; Li, Wei; Jiang, Nan; Yu, Haidong; Morohashi, Kengo; Ouma, Wilberforce Zachary; Morales-Mantilla, Daniel E; Gomez-Cano, Fabio Andres; Mukundi, Eric; Prada-Salcedo, Luis Daniel; Velazquez, Roberto Alers; Valentin, Jasmin; Mejía-Guerra, Maria Katherine; Gray, John; Doseff, Andrea I; Grotewold, Erich

    2017-03-06

    The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

  18. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    SciTech Connect

    Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; Novichkov, Pavel

    2015-06-01

    The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.

  19. Inferring the role of transcription factors in regulatory networks

    PubMed Central

    Veber, Philippe; Guziolowski, Carito; Le Borgne, Michel; Radulescu, Ovidiu; Siegel, Anne

    2008-01-01

    Background Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays. Results We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of E. coli extracted from the literature (1529 nodes and 3802 edges), and we estimate that 30% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to S. cerevisiae transcriptional network (2419 nodes and 4344 interactions), by combining ChIP-chip data and 15 expression profiles. We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15% of all the interactions). In addition, we report predictions for 14.5% of all interactions. Conclusion Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine a significant portion of

  20. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    PubMed

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  1. GeRNet: a gene regulatory network tool.

    PubMed

    Dussaut, J S; Gallo, C A; Cravero, F; Martínez, M J; Carballido, J A; Ponzoni, I

    2017-08-30

    Gene regulatory networks (GRNs) are crucial in every process of life since they govern the majority of the molecular processes. Therefore, the task of assembling these networks is highly important. In particular, the so called model-free approaches have an advantage modeling the complexities of dynamic molecular networks, since most of the gene networks are hard to be mapped with accuracy by any other mathematical model. A highly abstract model-free approach, called rule-based approach, offers several advantages performing data-driven analysis; such as the requirement of the least amount of data. They also have an important ability to perform inferences: its simplicity allows the inference of large size models with a higher speed of analysis. However, regarding these techniques, the reconstruction of the relational structure of the network is partial, hence incomplete, for an effective biological analysis. This situation motivated us to explore the possibility of hybridizing with other approaches, such as biclustering techniques. This led to incorporate a biclustering tool that finds new relations between the nodes of the GRN. In this work we present a new software, called GeRNeT that integrates the algorithms of GRNCOP2 and BiHEA along a set of tools for interactive visualization, statistical analysis and ontological enrichment of the resulting GRNs. In this regard, results associated with Alzheimer disease datasets are presented that show the usefulness of integrating both bioinformatics tools. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Enhancing gene regulatory network inference through data integration with markov random fields

    DOE PAGES

    Banf, Michael; Rhee, Seung Y.

    2017-02-01

    Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

  3. Enhancing gene regulatory network inference through data integration with markov random fields

    PubMed Central

    Banf, Michael; Rhee, Seung Y.

    2017-01-01

    A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation. PMID:28145456

  4. Enhancing gene regulatory network inference through data integration with markov random fields.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-02-01

    A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

  5. The exploration of network motifs as potential drug targets from post-translational regulatory networks.

    PubMed

    Zhang, Xiao-Dong; Song, Jiangning; Bork, Peer; Zhao, Xing-Ming

    2016-02-08

    Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

  6. Algebraic model checking for Boolean gene regulatory networks.

    PubMed

    Tran, Quoc-Nam

    2011-01-01

    We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

  7. Using shRNA experiments to validate gene regulatory networks.

    PubMed

    Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Quackenbush, John; Haibe-Kains, Benjamin

    2015-06-01

    Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field. In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.

  8. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    PubMed

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  9. Protecting the privacy of patient information in clinical networks: regulatory effectiveness analysis.

    PubMed

    Brannigan, V M

    1992-12-17

    Patient privacy is one of the major issues in the development of modern clinical information system networks. Such networks will have to demonstrate an appropriate concern for privacy as a precondition of operation. Regulatory effectiveness analysis is a novel technique for measuring compliance with a technological regulatory system. By examining the public policies, legal structures, and technical tools involved in the regulatory system, it is possible to discover discontinuities that may result in noncompliance with the regulatory system.

  10. Physiologically-based modeling of sleep-wake regulatory networks.

    PubMed

    Booth, Victoria; Diniz Behn, Cecilia G

    2014-04-01

    Mathematical modeling has played a significant role in building our understanding of sleep-wake and circadian behavior. Over the past 40 years, phenomenological models, including the two-process model and oscillator models, helped frame experimental results and guide progress in understanding the interaction of homeostatic and circadian influences on sleep and understanding the generation of rapid eye movement sleep cycling. Recent advances in the clarification of the neural anatomy and physiology involved in the regulation of sleep and circadian rhythms have motivated the development of more detailed and physiologically-based mathematical models that extend the approach introduced by the classical reciprocal-interaction model. Using mathematical formalisms developed in the field of computational neuroscience to model neuronal population activity, these models investigate the dynamics of proposed conceptual models of sleep-wake regulatory networks with a focus on generating appropriate sleep and wake state transition patterns as well as simulating disease states and experimental protocols. In this review, we discuss several recent physiologically-based mathematical models of sleep-wake regulatory networks. We identify common features among these models in their network structures, model dynamics and approaches for model validation. We describe how the model analysis technique of fast-slow decomposition, which exploits the naturally occurring multiple timescales of sleep-wake behavior, can be applied to understand model dynamics in these networks. Our purpose in identifying commonalities among these models is to propel understanding of both the mathematical models and their underlying conceptual models, and focus directions for future experimental and theoretical work. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence.

    PubMed

    Balasubramanian, Deepak; Schneper, Lisa; Kumari, Hansi; Mathee, Kalai

    2013-01-07

    Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a 'birds-eye' view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies.

  12. A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence

    PubMed Central

    Balasubramanian, Deepak; Schneper, Lisa; Kumari, Hansi; Mathee, Kalai

    2013-01-01

    Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies. PMID:23143271

  13. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

    PubMed

    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5, and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  14. Gene Regulatory Networks in Cardiac Conduction System Development

    PubMed Central

    Munshi, Nikhil V.

    2014-01-01

    The cardiac conduction system is a specialized tract of myocardial cells responsible for maintaining normal cardiac rhythm. Given its critical role in coordinating cardiac performance, a detailed analysis of the molecular mechanisms underlying conduction system formation should inform our understanding of arrhythmia pathophysiology and affect the development of novel therapeutic strategies. Historically, the ability to distinguish cells of the conduction system from neighboring working myocytes presented a major technical challenge for performing comprehensive mechanistic studies. Early lineage tracing experiments suggested that conduction cells derive from cardiomyocyte precursors, and these claims have been substantiated by using more contemporary approaches. However, regional specialization of conduction cells adds an additional layer of complexity to this system, and it appears that different components of the conduction system utilize unique modes of developmental formation. The identification of numerous transcription factors and their downstream target genes involved in regional differentiation of the conduction system has provided insight into how lineage commitment is achieved. Furthermore, by adopting cutting-edge genetic techniques in combination with sophisticated phenotyping capabilities, investigators have made substantial progress in delineating the regulatory networks that orchestrate conduction system formation and their role in cardiac rhythm and physiology. This review describes the connectivity of these gene regulatory networks in cardiac conduction system development and discusses how they provide a foundation for understanding normal and pathological human cardiac rhythms. PMID:22628576

  15. A regulatory network controls nephrocan expression and midgut patterning

    PubMed Central

    Hou, Juan; Wei, Wei; Saund, Ranajeet S.; Xiang, Ping; Cunningham, Thomas J.; Yi, Yuyin; Alder, Olivia; Lu, Daphne Y. D.; Savory, Joanne G. A.; Krentz, Nicole A. J.; Montpetit, Rachel; Cullum, Rebecca; Hofs, Nicole; Lohnes, David; Humphries, R. Keith; Yamanaka, Yojiro; Duester, Gregg; Saijoh, Yukio; Hoodless, Pamela A.

    2014-01-01

    Although many regulatory networks involved in defining definitive endoderm have been identified, the mechanisms through which these networks interact to pattern the endoderm are less well understood. To explore the mechanisms involved in midgut patterning, we dissected the transcriptional regulatory elements of nephrocan (Nepn), the earliest known midgut specific gene in mice. We observed that Nepn expression is dramatically reduced in Sox17−/− and Raldh2−/− embryos compared with wild-type embryos. We further show that Nepn is directly regulated by Sox17 and the retinoic acid (RA) receptor via two enhancer elements located upstream of the gene. Moreover, Nepn expression is modulated by Activin signaling, with high levels inhibiting and low levels enhancing RA-dependent expression. In Foxh1−/− embryos in which Nodal signaling is reduced, the Nepn expression domain is expanded into the anterior gut region, confirming that Nodal signaling can modulate its expression in vivo. Together, Sox17 is required for Nepn expression in the definitive endoderm, while RA signaling restricts expression to the midgut region. A balance of Nodal/Activin signaling regulates the anterior boundary of the midgut expression domain. PMID:25209250

  16. Comparison of evolutionary algorithms in gene regulatory network model inference

    PubMed Central

    2010-01-01

    Background The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. Results This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established. PMID:20105328

  17. Comparison of evolutionary algorithms in gene regulatory network model inference.

    PubMed

    Sîrbu, Alina; Ruskin, Heather J; Crane, Martin

    2010-01-27

    The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

  18. How difficult is inference of mammalian causal gene regulatory networks?

    PubMed

    Djordjevic, Djordje; Yang, Andrian; Zadoorian, Armella; Rungrugeecharoen, Kevin; Ho, Joshua W K

    2014-01-01

    Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for

  19. Framework for engineering finite state machines in gene regulatory networks.

    PubMed

    Oishi, Kevin; Klavins, Eric

    2014-09-19

    Finite state machines are fundamental computing devices at the core of many models of computation. In biology, finite state machines are commonly used as models of development in multicellular organisms. However, it remains unclear to what extent cells can remember state, how they can transition from one state to another reliably, and whether the existing parts available to the synthetic biologist are sufficient to implement specified finite state machines in living cells. Furthermore, how complex multicellular behaviors can be realized by multiple cells coordinating their states with signaling, growth, and division is not well understood. Here, we describe a method by which any finite state machine can be built using nothing more than a suitably engineered network of readily available repressing transcription factors. In particular, we show the mathematical equivalence of finite state machines with a Boolean model of gene regulatory networks. We describe how such networks can be realized with a small class of promoters and transcription factors. To demonstrate the effectiveness of our approach, we show that the behavior of the coarse grained ideal Boolean network model approximates a fine grained delay differential equation model of gene expression. Finally, we explore a framework for the design of more complex systems via an example, synthetic bacterial microcolony edge detection, that illustrates how finite state machines could be used together with cell signaling to construct novel multicellular behaviors.

  20. A review on the computational approaches for gene regulatory network construction.

    PubMed

    Chai, Lian En; Loh, Swee Kuan; Low, Swee Thing; Mohamad, Mohd Saberi; Deris, Safaai; Zakaria, Zalmiyah

    2014-05-01

    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Beyond antioxidant genes in the ancient NRF2 regulatory network

    PubMed Central

    Lacher, Sarah E.; Lee, Joslynn S.; Wang, Xuting; Campbell, Michelle R.; Bell, Douglas A.; Slattery, Matthew

    2016-01-01

    NRF2, a basic leucine zipper transcription factor encoded by the gene NFE2L2, is a master regulator of the transcriptional response to oxidative stress. NRF2 is structurally and functionally conserved from insects to humans, and it heterodimerizes with the small MAF transcription factors to bind a consensus DNA sequence (the antioxidant response element, or ARE) and regulate gene expression. We have used genome-wide chromatin immunoprecipitation (ChIP-seq) and gene expression data to identify direct NRF2 target genes in Drosophila and humans. These data have allowed us to construct the deeply conserved ancient NRF2 regulatory network – target genes that are conserved from Drosophila to human. The ancient network consists of canonical antioxidant genes, as well as genes related to proteasomal pathways, metabolism, and a number of less expected genes. We have also used enhancer reporter assays and electrophoretic mobility shift assays to confirm NRF2-mediated regulation of ARE (antioxidant response element) activity at a number of these novel target genes. Interestingly, the ancient network also highlights a prominent negative feedback loop; this, combined with the finding that and NRF2-mediated regulatory output is tightly linked to the quality of the ARE it is targeting, suggests that precise regulation of nuclear NRF2 concentration is necessary to achieve proper quantitative regulation of distinct gene sets. Together, these findings highlight the importance of balance in the NRF2-ARE pathway, and indicate that NRF2-mediated regulation of xenobiotic metabolism, glucose metabolism, and proteostasis have been central to this pathway since its inception. PMID:26163000

  2. Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification

    PubMed Central

    Baker, Christopher R.; Booth, Lauren N.; Sorrells, Trevor R.; Johnson, Alexander D.

    2012-01-01

    Summary We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all contribute to the evolution of a novel regulatory mode. The formation of this derived mode of regulation did not disrupt the ancestral mode and thereby created a hybrid regulatory state where both means of transcription regulation (ancestral and derived) contribute to the conserved expression pattern of the network. Finally, we show how this hybrid regulatory state has resolved in different ways in different lineages to generate the diversity of regulatory network structures observed in modern species. PMID:23021217

  3. Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

    PubMed

    Baker, Christopher R; Booth, Lauren N; Sorrells, Trevor R; Johnson, Alexander D

    2012-09-28

    We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all contribute to the evolution of a novel regulatory mode. The formation of this derived mode of regulation did not disrupt the ancestral mode and thereby created a hybrid regulatory state where both means of transcription regulation (ancestral and derived) contribute to the conserved expression pattern of the network. Finally, we show how this hybrid regulatory state has resolved in different ways in different lineages to generate the diversity of regulatory network structures observed in modern species.

  4. Boolean networks using the chi-square test for inferring large-scale gene regulatory networks.

    PubMed

    Kim, Haseong; Lee, Jae K; Park, Taesung

    2007-02-01

    Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN. The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms.

  5. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse.

    PubMed

    Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

    2015-01-01

    Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org. © The Author(s) 2015. Published by Oxford University Press.

  6. Pharyngeal mesoderm regulatory network controls cardiac and head muscle morphogenesis.

    PubMed

    Harel, Itamar; Maezawa, Yoshiro; Avraham, Roi; Rinon, Ariel; Ma, Hsiao-Yen; Cross, Joe W; Leviatan, Noam; Hegesh, Julius; Roy, Achira; Jacob-Hirsch, Jasmine; Rechavi, Gideon; Carvajal, Jaime; Tole, Shubha; Kioussi, Chrissa; Quaggin, Susan; Tzahor, Eldad

    2012-11-13

    The search for developmental mechanisms driving vertebrate organogenesis has paved the way toward a deeper understanding of birth defects. During embryogenesis, parts of the heart and craniofacial muscles arise from pharyngeal mesoderm (PM) progenitors. Here, we reveal a hierarchical regulatory network of a set of transcription factors expressed in the PM that initiates heart and craniofacial organogenesis. Genetic perturbation of this network in mice resulted in heart and craniofacial muscle defects, revealing robust cross-regulation between its members. We identified Lhx2 as a previously undescribed player during cardiac and pharyngeal muscle development. Lhx2 and Tcf21 genetically interact with Tbx1, the major determinant in the etiology of DiGeorge/velo-cardio-facial/22q11.2 deletion syndrome. Furthermore, knockout of these genes in the mouse recapitulates specific cardiac features of this syndrome. We suggest that PM-derived cardiogenesis and myogenesis are network properties rather than properties specific to individual PM members. These findings shed new light on the developmental underpinnings of congenital defects.

  7. Pharyngeal mesoderm regulatory network controls cardiac and head muscle morphogenesis

    PubMed Central

    Harel, Itamar; Maezawa, Yoshiro; Avraham, Roi; Rinon, Ariel; Ma, Hsiao-Yen; Cross, Joe W.; Leviatan, Noam; Hegesh, Julius; Roy, Achira; Jacob-Hirsch, Jasmine; Rechavi, Gideon; Carvajal, Jaime; Tole, Shubha; Kioussi, Chrissa; Quaggin, Susan; Tzahor, Eldad

    2012-01-01

    The search for developmental mechanisms driving vertebrate organogenesis has paved the way toward a deeper understanding of birth defects. During embryogenesis, parts of the heart and craniofacial muscles arise from pharyngeal mesoderm (PM) progenitors. Here, we reveal a hierarchical regulatory network of a set of transcription factors expressed in the PM that initiates heart and craniofacial organogenesis. Genetic perturbation of this network in mice resulted in heart and craniofacial muscle defects, revealing robust cross-regulation between its members. We identified Lhx2 as a previously undescribed player during cardiac and pharyngeal muscle development. Lhx2 and Tcf21 genetically interact with Tbx1, the major determinant in the etiology of DiGeorge/velo-cardio-facial/22q11.2 deletion syndrome. Furthermore, knockout of these genes in the mouse recapitulates specific cardiac features of this syndrome. We suggest that PM-derived cardiogenesis and myogenesis are network properties rather than properties specific to individual PM members. These findings shed new light on the developmental underpinnings of congenital defects. PMID:23112163

  8. Regulatory networks and connected components of the neutral space. A look at functional islands

    NASA Astrophysics Data System (ADS)

    Boldhaus, G.; Klemm, K.

    2010-09-01

    The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

  9. An Arabidopsis gene regulatory network for secondary cell wall synthesis

    SciTech Connect

    Taylor-Teeples, M.; Lin, L.; de Lucas, M.; Turco, G.; Toal, T. W.; Gaudinier, A.; Young, N. F.; Trabucco, G. M.; Veling, M. T.; Lamothe, R.; Handakumbura, P. P.; Xiong, G.; Wang, C.; Corwin, J.; Tsoukalas, A.; Zhang, L.; Ware, D.; Pauly, M.; Kliebenstein, D. J.; Dehesh, K.; Tagkopoulos, I.; Breton, G.; Pruneda-Paz, J. L.; Ahnert, S. E.; Kay, S. A.; Hazen, S. P.; Brady, S. M.

    2014-12-24

    The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. In this paper, we present a protein–DNA network between Arabidopsis thaliana transcription factors and secondary cell wall metabolic genes with gene expression regulated by a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. Finally, these interactions will serve as a foundation for understanding the regulation of a complex, integral plant component.

  10. ERIC DAVIDSON: STEPS TO A GENE REGULATORY NETWORK FOR DEVELOPMENT

    PubMed Central

    Rothenberg, Ellen V.

    2016-01-01

    Eric Harris Davidson was a unique and creative intellectual force who grappled with the diversity of developmental processes used by animal embryos and wrestled them into an intelligible set of principles, then spent his life translating these process elements into molecularly definable terms through the architecture of gene regulatory networks. He took speculative risks in his theoretical writing but ran a highly organized, rigorous experimental program that yielded an unprecedentedly full characterization of a developing organism. His writings created logical order and a framework for mechanism from the complex phenomena at the heart of advanced multicellular organism development. This is a reminiscence of intellectual currents in his work as observed by the author through the last 30–35 years of Davidson’s life. PMID:26825392

  11. Physiological regulatory networks: ecological roles and evolutionary constraints.

    PubMed

    Cohen, Alan A; Martin, Lynn B; Wingfield, John C; McWilliams, Scott R; Dunne, Jennifer A

    2012-08-01

    Ecological and evolutionary physiology has traditionally focused on one aspect of physiology at a time. Here, we discuss the implications of considering physiological regulatory networks (PRNs) as integrated wholes, a perspective that reveals novel roles for physiology in organismal ecology and evolution. For example, evolutionary response to changes in resource abundance might be constrained by the role of dietary micronutrients in immune response regulation, given a particular pathogen environment. Because many physiological components impact more than one process, organismal homeostasis is maintained, individual fitness is determined and evolutionary change is constrained (or facilitated) by interactions within PRNs. We discuss how PRN structure and its system-level properties could determine both individual performance and patterns of physiological evolution.

  12. Transcriptional Regulatory Networks for CD4 T Cell Differentiation

    PubMed Central

    Zhu, Jinfang

    2015-01-01

    CD4+ T cells play a central role in controlling the adaptive immune response by secreting cytokines to activate target cells. Naïve CD4+ T cells differentiate into at least four subsets, Th1, Th2, Th17, and inducible regulatory T cells, each with unique functions for pathogen elimination. The differentiation of these subsets is induced in response to cytokine stimulation, which is translated into Stat activation, followed by induction of master regulator transcription factors. In addition to these factors, multiple other transcription factors, both subset specific and shared, are also involved in promoting subset differentiation. This review will focus on the network of transcription factors that control CD4+ T cell differentiation. PMID:24839135

  13. Event-based cluster synchronization of coupled genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  14. Evolutionary expansion of a regulatory network by counter-silencing

    PubMed Central

    Will, William R.; Bale, Denise H.; Reid, Philip J.; Libby, Stephen J.; Fang, Ferric C.

    2014-01-01

    Horizontal gene transfer plays a major role in bacterial evolution. Successful acquisition of new genes requires their incorporation into existing regulatory networks. This study compares the regulation of conserved genes in the PhoPQ regulon of Salmonella enterica serovar Typhimurium with that of PhoPQ-regulated horizontally-acquired genes, which are silenced by the histone-like protein H-NS. We demonstrate that PhoP up-regulates conserved and horizontally-acquired genes by distinct mechanisms. Conserved genes are regulated by classical PhoP-mediated activation and are invariant in promoter architecture, whereas horizontally-acquired genes exhibit variable promoter architecture and are regulated by PhoP-mediated counter-silencing. Biochemical analyses show that a horizontally-acquired promoter adopts different structures in the silenced and counter-silenced states, implicating the remodeling of the H-NS nucleoprotein filament and the subsequent restoration of open complex formation as the central mechanism of counter-silencing. Our results indicate that counter-silencing is favored in the regulatory integration of newly-acquired genes because it is able to accommodate multiple promoter architectures. PMID:25348042

  15. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    SciTech Connect

    Rodionov, Dmitry A; Novichkov, Pavel S

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  16. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

    PubMed Central

    Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

    2015-01-01

    Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org PMID:26424082

  17. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

    PubMed Central

    Martin, Alberto J. M.; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S.; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). PMID:27695050

  18. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks.

    PubMed

    Martin, Alberto J M; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto).

  19. Integrated module and gene-specific regulatory inference implicates upstream signaling networks.

    PubMed

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A; Stewart, Ron; Gasch, Audrey P

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.

  20. Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

    PubMed Central

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

  1. Regulatory Networks Controlling Plant Cold Acclimation or Low Temperature Regulatory Networks Controlling Cold Acclimation in Arabidopsis (2011 JGI User Meeting)

    ScienceCinema

    Thomashow, Mike

    2016-07-12

    The U.S. Department of Energy Joint Genome Institute (JGI) invited scientists interested in the application of genomics to bioenergy and environmental issues, as well as all current and prospective users and collaborators, to attend the annual DOE JGI Genomics of Energy & Environment Meeting held March 22-24, 2011 in Walnut Creek, Calif. The emphasis of this meeting was on the genomics of renewable energy strategies, carbon cycling, environmental gene discovery, and engineering of fuel-producing organisms. The meeting features presentations by leading scientists advancing these topics. Mike Thomashow of Michigan State University gives a presentation on on "Low Temperature Regulatory Networks Controlling Cold Acclimation in Arabidopsis" at the 6th annual Genomics of Energy & Environment Meeting on March 23, 2011. «

  2. A validated regulatory network for Th17 cell specification

    PubMed Central

    Ciofani, Maria; Madar, Aviv; Galan, Carolina; Sellars, Maclean; Mace, Kieran; Pauli, Florencia; Agarwal, Ashish; Huang, Wendy; Parkhurst, Christopher N.; Muratet, Michael; Newberry, Kim M.; Meadows, Sarah; Greenfield, Alex; Yang, Yi; Jain, Preti; Kirigin, Francis F.; Birchmeier, Carmen; Wagner, Erwin F.; Murphy, Kenneth M.; Myers, Richard M.; Bonneau, Richard; Littman, Dan R.

    2012-01-01

    Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively-bound BATF and IRF4 contribute to initial chromatin accessibility, and with STAT3 initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple datasets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease. PMID:23021777

  3. Comprehensive Mapping of the Escherichia coli Flagellar Regulatory Network

    PubMed Central

    Fitzgerald, Devon M.; Bonocora, Richard P.; Wade, Joseph T.

    2014-01-01

    Flagellar synthesis is a highly regulated process in all motile bacteria. In Escherichia coli and related species, the transcription factor FlhDC is the master regulator of a multi-tiered transcription network. FlhDC activates transcription of a number of genes, including some flagellar genes and the gene encoding the alternative Sigma factor FliA. Genes whose expression is required late in flagellar assembly are primarily transcribed by FliA, imparting temporal regulation of transcription and coupling expression to flagellar assembly. In this study, we use ChIP-seq and RNA-seq to comprehensively map the E. coli FlhDC and FliA regulons. We define a surprisingly restricted FlhDC regulon, including two novel regulated targets and two binding sites not associated with detectable regulation of surrounding genes. In contrast, we greatly expand the known FliA regulon. Surprisingly, 30 of the 52 FliA binding sites are located inside genes. Two of these intragenic promoters are associated with detectable noncoding RNAs, while the others either produce highly unstable RNAs or are inactive under these conditions. Together, our data redefine the E. coli flagellar regulatory network, and provide new insight into the temporal orchestration of gene expression that coordinates the flagellar assembly process. PMID:25275371

  4. Complex Dynamic Behavior in Simple Gene Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Santillán Zerón, Moisés

    2007-02-01

    Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..

  5. Regulatory dynamics of synthetic gene networks with positive feedback.

    PubMed

    Maeda, Yusuke T; Sano, Masaki

    2006-06-16

    Biological processes are governed by complex networks ranging from gene regulation to signal transduction. Positive feedback is a key element in such networks. The regulation enables cells to adopt multiple internal expression states in response to a single external input signal. However, past works lacked a dynamical aspect of this system. To address the dynamical property of the positive feedback system, we employ synthetic gene circuits in Escherichia coli to measure the rise-time of both the no-feedback system and the positive feedback system. We show that the kinetics of gene expression is slowed down if the gene regulatory system includes positive feedback. We also report that the transition of gene switching behaviors from the hysteretic one to the graded one occurs. A mathematical model based on the chemical reactions shows that the response delay is an inherited property of the positive feedback system. Furthermore, with the aid of the phase diagram, we demonstrate the decline of the feedback activation causes the transition of switching behaviors. Our findings provide a further understanding of a positive feedback system in a living cell from a dynamical point of view.

  6. Automated large-scale control of gene regulatory networks.

    PubMed

    Tan, Mehmet; Alhajj, Reda; Polat, Faruk

    2010-04-01

    Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.

  7. Regulatory component analysis: a semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge

    PubMed Central

    Wang, Chen; Xuan, Jianhua; Shih, Ie-Ming; Clarke, Robert; Wang, Yue

    2011-01-01

    With the advent of high-throughput biotechnology capable of monitoring genomic signals, it becomes increasingly promising to understand molecular cellular mechanisms through systems biology approaches. One of the active research topics in systems biology is to infer gene transcriptional regulatory networks using various genomic data; this inference problem can be formulated as a linear model with latent signals associated with some regulatory proteins called transcription factors (TFs). As common statistical assumptions may not hold for genomic signals, typical latent variable algorithms such as independent component analysis (ICA) are incapable to reveal underlying true regulatory signals. Liao et al. [1] proposed to perform inference using an approach named network component analysis (NCA), the optimization of which is achieved by a least-squares fitting approach with biological knowledge constraints. However, the incompleteness of biological knowledge and its inconsistency with gene expression data are not considered in the original NCA solution, which could greatly affect the inference accuracy. To overcome these limitations, we propose a linear extraction scheme, namely regulatory component analysis (RCA), to infer underlying regulatory signals even with partial biological knowledge. Numerical simulations show a significant improvement of our proposed RCA over NCA, not only when signal-to-noise-ratio (SNR) is low, but also when the given biological knowledge is incomplete and inconsistent to gene expression data. Furthermore, real biological experiments on E. coli are performed for regulatory network inference in comparison with several typical linear latent variable methods, which again demonstrates the effectiveness and improved performance of the proposed algorithm. PMID:22685363

  8. Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi.

    PubMed

    Lavoie, Hugo; Hogues, Hervé; Whiteway, Malcolm

    2009-12-01

    Growing evidence suggests that transcriptional regulatory networks in many organisms are highly flexible. Here, we discuss the evolution of transcriptional regulatory networks governing the metabolic machinery of sequenced ascomycetes. In particular, recent work has shown that transcriptional rewiring is common in regulons controlling processes such as production of ribosome components and metabolism of carbohydrates and lipids. We note that dramatic rearrangements of the transcriptional regulatory components of metabolic functions have occurred among ascomycetes species.

  9. A Parallel Attractor Finding Algorithm Based on Boolean Satisfiability for Genetic Regulatory Networks

    PubMed Central

    Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. PMID:24718686

  10. A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

    PubMed

    Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

  11. Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

    PubMed Central

    Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

    2016-01-01

    Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

  12. Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.

    PubMed

    Chasman, Deborah; Walters, Kevin B; Lopes, Tiago J S; Eisfeld, Amie J; Kawaoka, Yoshihiro; Roy, Sushmita

    2016-07-01

    Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

  13. Stochastic models and numerical algorithms for a class of regulatory gene networks.

    PubMed

    Fournier, Thomas; Gabriel, Jean-Pierre; Pasquier, Jerôme; Mazza, Christian; Galbete, José; Mermod, Nicolas

    2009-08-01

    Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

  14. Bacterial regulatory networks are extremely flexible in evolution

    PubMed Central

    Lozada-Chávez, Irma; Janga, Sarath Chandra; Collado-Vides, Julio

    2006-01-01

    Over millions of years the structure and complexity of the transcriptional regulatory network (TRN) in bacteria has changed, reorganized and enabled them to adapt to almost every environmental niche on earth. In order to understand the plasticity of TRNs in bacteria, we studied the conservation of currently known TRNs of the two model organisms Escherichia coli K12 and Bacillus subtilis across complete genomes including Bacteria, Archaea and Eukarya at three different levels: individual components of the TRN, pairs of interactions and regulons. We found that transcription factors (TFs) evolve much faster than the target genes (TGs) across phyla. We show that global regulators are poorly conserved across the phylogenetic spectrum and hence TFs could be the major players responsible for the plasticity and evolvability of the TRNs. We also found that there is only a small fraction of significantly conserved transcriptional regulatory interactions among different phyla of bacteria and that there is no constraint on the elements of the interaction to co-evolve. Finally our results suggest that majority of the regulons in bacteria are rapidly lost implying a high-order flexibility in the TRNs. We hypothesize that during the divergence of bacteria certain essential cellular processes like the synthesis of arginine, biotine and ribose, transport of amino acids and iron, availability of phosphate, replication process and the SOS response are well conserved in evolution. From our comparative analysis, it is possible to infer that transcriptional regulation is more flexible than the genetic component of the organisms and its complexity and structure plays an important role in the phenotypic adaptation. PMID:16840530

  15. Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks.

    PubMed

    Feiglin, Ariel; Hacohen, Adar; Sarusi, Avital; Fisher, Jasmin; Unger, Ron; Ofran, Yanay

    2012-11-01

    Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure. We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes. While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.

  16. Candida albicans Kinesin Kar3 Depends on a Cik1-Like Regulatory Partner Protein for Its Roles in Mating, Cell Morphogenesis, and Bipolar Spindle Formation

    PubMed Central

    Frazer, Corey; Joshi, Monika; Delorme, Caroline; Davis, Darlene; Bennett, Richard J.

    2015-01-01

    Candida albicans is a major fungal pathogen whose virulence is associated with its ability to transition from a budding yeast form to invasive hyphal filaments. The kinesin-14 family member CaKar3 is required for transition between these morphological states, as well as for mitotic progression and karyogamy. While kinesin-14 proteins are ubiquitous, CaKar3 homologs in hemiascomycete fungi are unique because they form heterodimers with noncatalytic kinesin-like proteins. Thus, CaKar3-based motors may represent a novel antifungal drug target. We have identified and examined the roles of a kinesin-like regulator of CaKar3. We show that orf19.306 (dubbed CaCIK1) encodes a protein that forms a heterodimer with CaKar3, localizes CaKar3 to spindle pole bodies, and can bind microtubules and influence CaKar3 mechanochemistry despite lacking an ATPase activity of its own. Similar to CaKar3 depletion, loss of CaCik1 results in cell cycle arrest, filamentation defects, and an inability to undergo karyogamy. Furthermore, an examination of the spindle structure in cells lacking either of these proteins shows that a large proportion have a monopolar spindle or two dissociated half-spindles, a phenotype unique to the C. albicans kinesin-14 homolog. These findings provide new insights into mitotic spindle structure and kinesin motor function in C. albicans and identify a potentially vulnerable target for antifungal drug development. PMID:26024903

  17. An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks.

    PubMed

    He, Qinbin; Xia, Zhile; Lin, Bin

    2016-11-07

    Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

  18. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    PubMed

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.

  19. Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome.

    PubMed

    Galardini, Marco; Brilli, Matteo; Spini, Giulia; Rossi, Matteo; Roncaglia, Bianca; Bani, Alessia; Chiancianesi, Manuela; Moretto, Marco; Engelen, Kristof; Bacci, Giovanni; Pini, Francesco; Biondi, Emanuele G; Bazzicalupo, Marco; Mengoni, Alessio

    2015-09-01

    Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial

  20. Dose response relationship in anti-stress gene regulatory networks.

    PubMed

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

  1. A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components.

    PubMed

    Knaack, Sara A; Siahpirani, Alireza Fotuhi; Roy, Sushmita

    2014-01-01

    Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell

  2. Learning a Markov Logic network for supervised gene regulatory network inference.

    PubMed

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  3. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    PubMed Central

    2012-01-01

    Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T

  4. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening

    PubMed Central

    Arhondakis, Stilianos; Bita, Craita E.; Perrakis, Andreas; Manioudaki, Maria E.; Krokida, Afroditi; Kaloudas, Dimitrios; Kalaitzis, Panagiotis

    2016-01-01

    Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening. PMID:27625653

  5. Transcriptional Regulatory Network for the Development of Innate Lymphoid Cells

    PubMed Central

    Zhong, Chao; Zhu, Jinfang

    2015-01-01

    Recent studies on innate lymphoid cells (ILCs) have expanded our knowledge about the innate arm of the immune system. Helper-like ILCs share both the “innate” feature of conventional natural killer (cNK) cells and the “helper” feature of CD4+ T helper (Th) cells. With this combination, helper-like ILCs are capable of initiating early immune responses similar to cNK cells, but via secretion of a set of effector cytokines similar to those produced by Th cells. Although many studies have revealed the functional similarity between helper-like ILCs and Th cells, some aspects of ILCs including the development of this lineage remain elusive. It is intriguing that the majority of transcription factors involved in multiple stages of T cell development, differentiation, and function also play critical roles during ILC development. Regulators such as Id2, GATA-3, Nfil3, TOX, and TCF-1 are expressed and function at various stages of ILC development. In this review, we will summarize the expression and functions of these transcription factors shared by ILCs and Th cells. We will also propose a complex transcriptional regulatory network for the lineage commitment of ILCs. PMID:26379372

  6. An Arabidopsis gene regulatory network for secondary cell wall synthesis

    DOE PAGES

    Taylor-Teeples, M.; Lin, L.; de Lucas, M.; ...

    2014-12-24

    The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. In this paper, we present a protein–DNA network between Arabidopsis thaliana transcription factors and secondary cell wall metabolic genes with gene expression regulated bymore » a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. Finally, these interactions will serve as a foundation for understanding the regulation of a complex, integral plant component.« less

  7. Identification of a gene regulatory network associated with prion replication

    PubMed Central

    Marbiah, Masue M; Harvey, Anna; West, Billy T; Louzolo, Anais; Banerjee, Priya; Alden, Jack; Grigoriadis, Anita; Hummerich, Holger; Kan, Ho-Man; Cai, Ying; Bloom, George S; Jat, Parmjit; Collinge, John; Klöhn, Peter-Christian

    2014-01-01

    Prions consist of aggregates of abnormal conformers of the cellular prion protein (PrPC). They propagate by recruiting host-encoded PrPC although the critical interacting proteins and the reasons for the differences in susceptibility of distinct cell lines and populations are unknown. We derived a lineage of cell lines with markedly differing susceptibilities, unexplained by PrPC expression differences, to identify such factors. Transcriptome analysis of prion-resistant revertants, isolated from highly susceptible cells, revealed a gene expression signature associated with susceptibility and modulated by differentiation. Several of these genes encode proteins with a role in extracellular matrix (ECM) remodelling, a compartment in which disease-related PrP is deposited. Silencing nine of these genes significantly increased susceptibility. Silencing of Papss2 led to undersulphated heparan sulphate and increased PrPC deposition at the ECM, concomitantly with increased prion propagation. Moreover, inhibition of fibronectin 1 binding to integrin α8 by RGD peptide inhibited metalloproteinases (MMP)-2/9 whilst increasing prion propagation. In summary, we have identified a gene regulatory network associated with prion propagation at the ECM and governed by the cellular differentiation state. PMID:24843046

  8. A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; Otim, Ochan; Brown, C. Titus; Livi, Carolina B.; Lee, Pei Yun; Revilla, Roger; Schilstra, Maria J.; Clarke, Peter J C.; Rust, Alistair G.; Pan, Zhengjun; Arnone, Maria I.; Rowen, Lee; Cameron, R. Andrew; McClay, David R.; Hood, Leroy; Bolouri, Hamid

    2002-01-01

    We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.caltech.edu/mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the beta-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentrotus purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of

  9. A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; hide

    2002-01-01

    We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.caltech.edu/mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the beta-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentrotus purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of

  10. Impact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria

    PubMed Central

    González Pérez, Abel D; González González, Evelyn; Espinosa Angarica, Vladimir; Vasconcelos, Ana Tereza R; Collado-Vides, Julio

    2008-01-01

    Background In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as E. coli among the prokaryotes. Results In this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes. Conclusion The rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events – are important sources of variation of gene regulation at this evolutionary scale. PMID:18366643

  11. Impact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria.

    PubMed

    González Pérez, Abel D; González González, Evelyn; Espinosa Angarica, Vladimir; Vasconcelos, Ana Tereza R; Collado-Vides, Julio

    2008-03-17

    In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as E. coli among the prokaryotes. In this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes. The rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events - are important sources of variation of gene regulation at this evolutionary scale.

  12. Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach

    PubMed Central

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown. PMID:24058904

  13. Optimal control of gene regulatory networks with effectiveness of multiple drugs: a Boolean network approach.

    PubMed

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown.

  14. Signal Correlations in Ecological Niches Can Shape the Organization and Evolution of Bacterial Gene Regulatory Networks

    PubMed Central

    Dufour, Yann S.; Donohue, Timothy J.

    2015-01-01

    Transcriptional regulation plays a significant role in the biological response of bacteria to changing environmental conditions. Therefore, mapping transcriptional regulatory networks is an important step not only in understanding how bacteria sense and interpret their environment but also to identify the functions involved in biological responses to specific conditions. Recent experimental and computational developments have facilitated the characterization of regulatory networks on a genome-wide scale in model organisms. In addition, the multiplication of complete genome sequences has encouraged comparative analyses to detect conserved regulatory elements and infer regulatory networks in other less well-studied organisms. However, transcription regulation appears to evolve rapidly, thus, creating challenges for the transfer of knowledge to nonmodel organisms. Nevertheless, the mechanisms and constraints driving the evolution of regulatory networks have been the subjects of numerous analyses, and several models have been proposed. Overall, the contributions of mutations, recombination, and horizontal gene transfer are complex. Finally, the rapid evolution of regulatory networks plays a significant role in the remarkable capacity of bacteria to adapt to new or changing environments. Conversely, the characteristics of environmental niches determine the selective pressures and can shape the structure of regulatory network accordingly. PMID:23046950

  15. Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate

    PubMed Central

    Coulombe-Huntington, Jasmin; Xia, Yu

    2012-01-01

    The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level. PMID:23093926

  16. Data- and knowledge-based modeling of gene regulatory networks: an update

    PubMed Central

    Linde, Jörg; Schulze, Sylvie; Henkel, Sebastian G.; Guthke, Reinhard

    2015-01-01

    Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions. PMID:27047314

  17. Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

    PubMed Central

    2013-01-01

    Background 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. Results 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. Conclusions 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

  18. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    PubMed

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  19. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

    PubMed Central

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case. PMID:27298752

  20. Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

    PubMed

    Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

    2015-01-01

    Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

  1. Efficient computation of minimal perturbation sets in gene regulatory networks

    PubMed Central

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; Degueurce, Gwendoline; Ibberson, Mark; Dorier, Julien; Xenarios, Ioannis

    2013-01-01

    In the last few decades, technological and experimental advancements have enabled a more precise understanding of the mode of action of drugs with respect to human cell signaling pathways and have positively influenced the design of new drug compounds. However, as the design of compounds has become increasingly target-specific, the overall effects of a drug on adjacent cellular signaling pathways remain difficult to predict because of the complexity of the interactions involved. Off-target effects of drugs are known to influence their efficacy and safety. Similarly, drugs which are more target-specific also suffer from lack of efficacy because their scope might be too limited in the context of cellular signaling. Even in situations where the signaling pathways targeted by a drug are known, the presence of point mutations in some of the components of the pathways can render a therapy ineffective in a considerable target subpopulation. Some of these issues can be addressed by predicting Minimal Intervention Sets (MIS) of elements of the signaling pathways that when perturbed give rise to a pre-defined cellular phenotype. These minimal gene perturbation sets can then be further used to screen a library of drug compounds in order to discover effective drug therapies. This manuscript describes algorithms that can be used to discover MIS in a gene regulatory network that can lead to a defined cellular phenotype. Algorithms are implemented in our Boolean modeling toolbox, GenYsis. The software binaries of GenYsis are available for download from http://www.vital-it.ch/software/genYsis/. PMID:24391592

  2. The human disease network in terms of dysfunctional regulatory mechanisms.

    PubMed

    Yang, Jing; Wu, Su-Juan; Dai, Wen-Tao; Li, Yi-Xue; Li, Yuan-Yuan

    2015-10-08

    Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases. We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways' differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test. Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular

  3. Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfural tolerance for ethanologenic yeast

    USDA-ARS?s Scientific Manuscript database

    Composed of linear difference equations, a discrete dynamic system model was designed to reconstruct transcriptional regulations in gene regulatory networks in response to 5-hydroxymethylfurfural, a bioethanol conversion inhibitor for ethanologenic yeast Saccharomyces cerevisiae. The modeling aims ...

  4. Exponential stability of discrete-time genetic regulatory networks with delays.

    PubMed

    Cao, Jinde; Ren, Fengli

    2008-03-01

    Discrete-time versions of the continuous-time genetic regulatory networks (GRNs) with SUM regulatory functions are formulated and studied in this letter. Sufficient conditions are derived to ensure the global exponential stability of the discrete-time GRNs with delays. An illustrative example is given to demonstrate the effectiveness of the obtained results.

  5. Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs.

    PubMed

    Zhang, Junpeng; Duy Le, Thuc; Liu, Lin; He, Jianfeng; Li, Jiuyong

    2016-02-01

    Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide important insights into the mechanisms of complex human diseases caused by miRNA regulation. Therefore, it is important to identify miRNA synergism and study miRNA characteristics in miRNA synergistic regulatory networks. A number of methods have been proposed to identify miRNA synergism. However, most of the methods only use downstream target genes of miRNAs to infer miRNA synergism when miRNAs can also be regulated by upstream transcription factors (TFs) at the transcriptional level. Additionally, most methods are based on statistical associations identified from data without considering the causal nature of gene regulation. In this paper, we present a causality based framework, called mirSRN (miRNA synergistic regulatory network), to infer miRNA synergism in human molecular systems by considering both downstream miRNA targets and upstream TF regulation. We apply the proposed framework to two real world datasets and discover that almost all the top 10 miRNAs with the largest node degree in the mirSRNs are associated with different human diseases, including cancer, and that the mirSRNs are approximately scale-free and small-world networks. We also find that most miRNAs in the networks are frequently synergistic with other miRNAs, and miRNAs related to the same disease are likely to be synergistic and in a cluster linked to a biological function. Synergistic miRNA pairs show higher co-expression level, and may have potential functional relationships indicating collaboration between the miRNAs. Functional validation of the identified synergistic miRNAs demonstrates that these miRNAs cause different kinds of diseases. These results deepen our understanding of the biological meaning of miRNA synergism.

  6. A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

    PubMed

    Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

  7. The contribution of transposable elements to the evolution of regulatory networks

    PubMed Central

    Feschotte, Cédric

    2008-01-01

    Preface The control and coordination of eukaryotic gene expression rely on transcriptional and post-transcriptional regulatory networks. Although progress has been made in mapping the components and deciphering the function of these networks, the mechanisms by which such intricate circuits originate and evolve remain poorly understood. Here I revisit and expand earlier models proposing that genomic repeats, and in particular transposable elements, have been a rich source of material for the assembly and tinkering of eukaryotic gene regulatory systems. PMID:18368054

  8. Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

    PubMed Central

    Yamanaka, Ryota; Kitano, Hiroaki

    2013-01-01

    Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

  9. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    PubMed

    Hase, Takeshi; Ghosh, Samik; Yamanaka, Ryota; Kitano, Hiroaki

    2013-01-01

    Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  10. One hub-one process: a tool based view on regulatory network topology

    PubMed Central

    Axelsen, Jacob Bock; Bernhardsson, Sebastian; Sneppen, Kim

    2008-01-01

    Background The relationship between the regulatory design and the functionality of molecular networks is a key issue in biology. Modules and motifs have been associated to various cellular processes, thereby providing anecdotal evidence for performance based localization on molecular networks. Results To quantify structure-function relationship we investigate similarities of proteins which are close in the regulatory network of the yeast Saccharomyces Cerevisiae. We find that the topology of the regulatory network only show weak remnants of its history of network reorganizations, but strong features of co-regulated proteins associated to similar tasks. These functional correlations decreases strongly when one consider proteins separated by more than two steps in the regulatory network. The network topology primarily reflects the processes that is orchestrated by each individual hub, whereas there is nearly no remnants of the history of protein duplications. Conclusion Our results suggests that local topological features of regulatory networks, including broad degree distributions, emerge as an implicit result of matching a number of needed processes to a finite toolbox of proteins. PMID:18318890

  11. Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes.

    PubMed

    Sotiropoulos, Vassilios; Kaznessis, Yiannis N

    2007-01-09

    Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA) and Tet-On (rtTA). Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory networks and rationally engineer novel ones with useful

  12. Steady-State Analysis of Genetic Regulatory Networks Modelled by Probabilistic Boolean Networks

    PubMed Central

    Gluhovsky, Ilya; Hashimoto, Ronaldo F.; Dougherty, Edward R.; Zhang, Wei

    2003-01-01

    Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes. PMID:18629023

  13. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

    NASA Astrophysics Data System (ADS)

    Martin, O. C.; Krzywicki, A.; Zagorski, M.

    2016-07-01

    Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent ;motifs;, that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.

  14. Using consensus bayesian network to model the reactive oxygen species regulatory pathway.

    PubMed

    Hu, Liangdong; Wang, Limin

    2013-01-01

    Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.

  15. Using Consensus Bayesian Network to Model the Reactive Oxygen Species Regulatory Pathway

    PubMed Central

    Hu, Liangdong; Wang, Limin

    2013-01-01

    Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the Bayesian network from microarray data directly. Although large numbers of Bayesian network learning algorithms have been developed, when applying them to learn Bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn Bayesian networks contain too few microarray data. In this paper, we propose a consensus Bayesian network which is constructed by combining Bayesian networks from relevant literatures and Bayesian networks learned from microarray data. It would have a higher accuracy than the Bayesian networks learned from one database. In the experiment, we validated the Bayesian network combination algorithm on several classic machine learning databases and used the consensus Bayesian network to model the 's ROS pathway. PMID:23457624

  16. Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

    DOE PAGES

    Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.; ...

    2016-03-18

    Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome

  17. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data.

    PubMed

    Faria, José P; Overbeek, Ross; Taylor, Ronald C; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S

    2016-01-01

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same "ON" and "OFF" gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions

  18. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

    PubMed Central

    Faria, José P.; Overbeek, Ross; Taylor, Ronald C.; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S.

    2016-01-01

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental

  19. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

    PubMed

    Liu, Zhi-Ping

    2015-02-01

    Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

  20. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

    PubMed Central

    Liu, Zhi-Ping

    2015-01-01

    Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented. PMID:25937810

  1. CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

    PubMed

    Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

    2016-12-23

    A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

  2. Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

    PubMed Central

    2014-01-01

    It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism. PMID:24517200

  3. A Boolean Model of the Cardiac Gene Regulatory Network Determining First and Second Heart Field Identity

    PubMed Central

    Zhou, Dao; Kestler, Hans A.; Kühl, Michael

    2012-01-01

    Two types of distinct cardiac progenitor cell populations can be identified during early heart development: the first heart field (FHF) and second heart field (SHF) lineage that later form the mature heart. They can be characterized by differential expression of transcription and signaling factors. These regulatory factors influence each other forming a gene regulatory network. Here, we present a core gene regulatory network for early cardiac development based on published temporal and spatial expression data of genes and their interactions. This gene regulatory network was implemented in a Boolean computational model. Simulations reveal stable states within the network model, which correspond to the regulatory states of the FHF and the SHF lineages. Furthermore, we are able to reproduce the expected temporal expression patterns of early cardiac factors mimicking developmental progression. Additionally, simulations of knock-down experiments within our model resemble published phenotypes of mutant mice. Consequently, this gene regulatory network retraces the early steps and requirements of cardiogenic mesoderm determination in a way appropriate to enhance the understanding of heart development. PMID:23056457

  4. [Comparative analysis of conservation and regulatory network on core transcription factors in mouse inner ear development].

    PubMed

    Chen, Zhi-Qiang; Han, Xin-Huan; Wei, Qin-Jun; Xing, Guang-Qian; Cao, Xin

    2013-10-01

    During vertebrate inner ear development, several core genes, such as Six1, Six4, Pax2, Pax8, Foxi1, Dlx5, Gbx2, Irx2/3, and Msx1, are crucial to the regulation of the otic placode induction. In order to get the gene regulatory network during inner ear development, bioinformatics methods were adopted to analyze conservation and regulation of the core transcription factors in mice. Pax2, Pax8, Foxi1, and Dlx5 remained to be the main regulators during inner ear development, which was consistent with the gene regulatory network from literature. Six1 was regulated by many transcription factors, and Gbx2, Irx2/3, and Msx1 played important roles in the regulatory network. The differences in the constructed regulatory network were reasonably analyzed. It was predicted that Msxl regulated the expression of Six1 and Gbx2. In addition, several transcription factors, such as Sox5, Lhx2, Rax, Otx1, Otx2, Pitxl, Pitx2, Nkx2-5, Irx4, Irx6, Dlx2, Hmx1/2/3, Pou4f3, Pax4 and Tlx2, were found to be involved in the regulatory network. Our results provide an improved understanding of the regulatory mechanism during inner ear development.

  5. Gene regulatory network inference using fused LASSO on multiple data sets.

    PubMed

    Omranian, Nooshin; Eloundou-Mbebi, Jeanne M O; Mueller-Roeber, Bernd; Nikoloski, Zoran

    2016-02-11

    Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.

  6. Regulatory Aspects of Smart Water Networks in the U.S.

    EPA Science Inventory

    The presentation addresses regulatory aspects of smart water networks in the U.S. It will be presented at the Smart Water Networks Forum (SWAN) annual conference in London, England from April 29-30, 2015. The conference will bring together key voices in the smart water space f...

  7. Regulatory Aspects of Smart Water Networks in the U.S.

    EPA Science Inventory

    The presentation addresses regulatory aspects of smart water networks in the U.S. It will be presented at the Smart Water Networks Forum (SWAN) annual conference in London, England from April 29-30, 2015. The conference will bring together key voices in the smart water space f...

  8. Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity.

    PubMed

    Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem N

    2012-01-01

    This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.

  9. Extended evolution: A conceptual framework for integrating regulatory networks and niche construction.

    PubMed

    Laubichler, Manfred D; Renn, Jürgen

    2015-11-01

    This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path-dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems.

  10. Anticipated Ethics and Regulatory Challenges in PCORnet: The National Patient-Centered Clinical Research Network.

    PubMed

    Ali, Joseph; Califf, Robert; Sugarman, Jeremy

    2016-01-01

    PCORnet, the National Patient-Centered Clinical Research Network, seeks to establish a robust national health data network for patient-centered comparative effectiveness research. This article reports the results of a PCORnet survey designed to identify the ethics and regulatory challenges anticipated in network implementation. A 12-item online survey was developed by leadership of the PCORnet Ethics and Regulatory Task Force; responses were collected from the 29 PCORnet networks. The most pressing ethics issues identified related to informed consent, patient engagement, privacy and confidentiality, and data sharing. High priority regulatory issues included IRB coordination, privacy and confidentiality, informed consent, and data sharing. Over 150 IRBs and five different approaches to managing multisite IRB review were identified within PCORnet. Further empirical and scholarly work, as well as practical and policy guidance, is essential if important initiatives that rely on comparative effectiveness research are to move forward.

  11. Base pairing small RNAs and their roles in global regulatory networks

    PubMed Central

    Beisel, Chase L.; Storz, Gisela

    2010-01-01

    Bacteria employ a range of RNA regulators collectively termed small RNAs (sRNAs) to help respond to changes in the environment. Many sRNAs regulate their target mRNAs through limited base pairing interactions. Ongoing characterization of base pairing sRNAs in bacteria has started to reveal how these sRNAs participate in global regulatory networks. These networks can be broken down into smaller regulatory circuits that have characteristic behaviors and functions. In this review, we describe the specific regulatory circuits that incorporate base pairing sRNAs and the importance of each circuit in global regulation. Since most of these circuits were originally identified as network motifs in transcriptional networks, we also discuss why sRNAs may be employed over protein transcription factors to help transduce environmental signals. PMID:20662934

  12. Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell

    PubMed Central

    Lim, Wendell A.; Lee, Connie M.; Tang, Chao

    2013-01-01

    A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241

  13. Community Structure Reveals Biologically Functional Modules in MEF2C Transcriptional Regulatory Network

    PubMed Central

    Alcalá-Corona, Sergio A.; Velázquez-Caldelas, Tadeo E.; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcription factor interactions downstream from MEF2C, a human transcription factor master regulator. By analyzing the connectivity structure of such network, we were able to find different biologically functional processes and specific biochemical pathways statistically enriched in communities of genes into the network, such processes are related to cell signaling, cell cycle and metabolism. In this way we further support the hypothesis that structural properties of biological networks encode an important part of their functional behavior in eukaryotic cells. PMID:27252657

  14. Design principles of regulatory networks: searching for the molecular algorithms of the cell.

    PubMed

    Lim, Wendell A; Lee, Connie M; Tang, Chao

    2013-01-24

    A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks.

  15. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

    PubMed

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

    2012-07-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

  16. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

    PubMed Central

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

    2012-01-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

  17. Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

    PubMed

    Chang, Chunqi; Ding, Zhi; Hung, Yeung Sam; Fung, Peter Chin Wan

    2008-06-01

    Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm

  18. Pin1: Intimate involvement with the regulatory protein kinase networks in the global phosphorylation landscape.

    PubMed

    Litchfield, David W; Shilton, Brian H; Brandl, Christopher J; Gyenis, Laszlo

    2015-10-01

    Protein phosphorylation is a universal regulatory mechanism that involves an extensive network of protein kinases. The discovery of the phosphorylation-dependent peptidyl-prolyl isomerase Pin1 added an additional layer of complexity to these regulatory networks. We have evaluated interactions between Pin1 and the regulatory kinome and proline-dependent phosphoproteome taking into consideration findings from targeted studies as well as data that has emerged from systematic phosphoproteomic workflows and from curated protein interaction databases. The relationship between Pin1 and the regulatory protein kinase networks is not restricted simply to the recognition of proteins that are substrates for proline-directed kinases. In this respect, Pin1 itself is phosphorylated in cells by protein kinases that modulate its functional properties. Furthermore, the phosphorylation-dependent targets of Pin1 include a number of protein kinases as well as other enzymes such as phosphatases and regulatory subunits of kinases that modulate the actions of protein kinases. As a result of its interactions with numerous protein kinases and their substrates, as well as itself being a target for phosphorylation, Pin1 has an intricate relationship with the regulatory protein kinase and phosphoproteomic networks that orchestrate complex cellular processes and respond to environmental cues. This article is part of a Special Issue entitled Proline-directed Foldases: Cell Signaling Catalysts and Drug Targets. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

    PubMed Central

    Liu, Wei; Zhu, Wen; Liao, Bo; Chen, Xiangtao

    2016-01-01

    Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method. PMID:27829000

  20. Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.

    PubMed

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.

  1. Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks

    PubMed Central

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    Background Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. Methodology To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. Principal Findings We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks. PMID:26927540

  2. MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

    PubMed Central

    Sadeghi, Mehdi; Ranjbar, Bijan; Ganjalikhany, Mohamad Reza; M. Khan, Faiz; Schmitz, Ulf; Wolkenhauer, Olaf; Gupta, Shailendra K.

    2016-01-01

    Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches. PMID:28005952

  3. MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression.

    PubMed

    Sadeghi, Mehdi; Ranjbar, Bijan; Ganjalikhany, Mohamad Reza; M Khan, Faiz; Schmitz, Ulf; Wolkenhauer, Olaf; Gupta, Shailendra K

    2016-01-01

    Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

  4. Architecture and dynamics of overlapped RNA regulatory networks.

    PubMed

    Lapointe, Christopher P; Preston, Melanie A; Wilinski, Daniel; Saunders, Harriet A J; Campbell, Zachary T; Wickens, Marvin

    2017-08-02

    A single protein can bind and regulate many mRNAs. Multiple proteins with similar specificities often bind and control overlapping sets of mRNAs. Yet, little is known about the architecture or dynamics of overlapped networks. We focused on three proteins with similar structures and related RNA-binding specificities ‒ Puf3p, Puf4p, and Puf5p of S. cerevisiae. Using RNA Tagging, we identified a "super-network" comprised of four sub-networks: Puf3p, Puf4p, and Puf5p sub-networks, and one controlled by both Puf4p and Puf5p. The architecture of individual sub-networks, and thus the super-network, are determined by competition among particular PUF proteins to bind mRNAs, their affinities for binding elements, and the abundances of the proteins. The super-network responds dramatically: the remaining network can either expand or contract. These strikingly opposite outcomes are determined by an interplay between the relative abundance of the RNAs and proteins, and their affinities for one another. The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  5. Reconstruction of gene regulatory network related to photosynthesis in Arabidopsis thaliana

    PubMed Central

    Yu, Xianbin; Zheng, Guangyong; Shan, Lanlan; Meng, Guofeng; Vingron, Martin; Liu, Qi; Zhu, Xin-Guang

    2014-01-01

    Photosynthesis is one of the most important biological processes on the earth. So far, though the molecular mechanisms underlying photosynthesis is well understood, however, the regulatory networks of photosynthesis are poorly studied. Given the current interest in improving photosynthetic efficiency for greater crop yield, elucidating the detailed regulatory networks controlling the construction and maintenance of photosynthetic machinery is not only scientifically significant but also holding great potential in agricultural application. In this study, we first identified transcription factors (TFs) related to photosynthesis through the TRAP approach using position weight matrix information. Then, for TFs related to photosynthesis, interactions between them and their targets were also determined by the ARACNE approach. Finally, a gene regulatory network was established by combining TF-targets information generated by these two approaches. Topological analysis of the regulatory network suggested that (a) the regulatory network of photosynthesis has a property of “small world”; (b) there is substantial coordination mediated by transcription factors between different components in photosynthesis. PMID:24982665

  6. DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data.

    PubMed

    Schulz, Marcel H; Devanny, William E; Gitter, Anthony; Zhong, Shan; Ernst, Jason; Bar-Joseph, Ziv

    2012-08-16

    Modeling dynamic regulatory networks is a major challenge since much of the protein-DNA interaction data available is static. The Dynamic Regulatory Events Miner (DREM) uses a Hidden Markov Model-based approach to integrate this static interaction data with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM has been used successfully in diverse areas of biological research. However, several issues were not addressed by the original version. DREM 2.0 is a comprehensive software for reconstructing dynamic regulatory networks that supports interactive graphical or batch mode. With version 2.0 a set of new features that are unique in comparison with other softwares are introduced. First, we provide static interaction data for additional species. Second, DREM 2.0 now accepts continuous binding values and we added a new method to utilize TF expression levels when searching for dynamic models. Third, we added support for discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. Finally, we improved the visualization to support the new features. Combined, these changes improve the ability of DREM 2.0 to accurately recover dynamic regulatory networks and make it much easier to use it for analyzing such networks in several species with varying degrees of interaction information. DREM 2.0 provides a unique framework for constructing and visualizing dynamic regulatory networks. DREM 2.0 can be downloaded from: http://www.sb.cs.cmu.edu/drem.

  7. Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

    PubMed Central

    2010-01-01

    Background RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. Results In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. Conclusions We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches. PMID:20550706

  8. Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks.

    PubMed

    Zhu, Shijia; Wang, Yadong

    2015-12-18

    Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is 'stationarity', and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

  9. Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhu, Shijia; Wang, Yadong

    2015-12-01

    Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

  10. An electronic regulatory document management system for a clinical trial network.

    PubMed

    Zhao, Wenle; Durkalski, Valerie; Pauls, Keith; Dillon, Catherine; Kim, Jaemyung; Kolk, Deneil; Silbergleit, Robert; Stevenson, Valerie; Palesch, Yuko

    2010-01-01

    A computerized regulatory document management system has been developed as a module in a comprehensive Clinical Trial Management System (CTMS) designed for an NIH-funded clinical trial network in order to more efficiently manage and track regulatory compliance. Within the network, several institutions and investigators are involved in multiple trials, and each trial has regulatory document requirements. Some of these documents are trial specific while others apply across multiple trials. The latter causes a possible redundancy in document collection and management. To address these and other related challenges, a central regulatory document management system was designed. This manuscript shares the design of the system as well as examples of it use in current studies. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  11. Computer-assisted curation of a human regulatory core network from the biological literature.

    PubMed

    Thomas, Philippe; Durek, Pawel; Solt, Illés; Klinger, Bertram; Witzel, Franziska; Schulthess, Pascal; Mayer, Yvonne; Tikk, Domonkos; Blüthgen, Nils; Leser, Ulf

    2015-04-15

    A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plagued by high false-positive rates. Meanwhile, a large body of knowledge on high-quality regulatory interactions remains largely unexplored, as it is available only in natural language descriptions scattered over millions of scientific publications. Such data are hard to extract and regulatory data currently contain together only 503 regulatory relations between human TFs. We developed a text-mining-assisted workflow to systematically extract knowledge about regulatory interactions between human TFs from the biological literature. We applied this workflow to the entire Medline, which helped us to identify more than 45 000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained ∼900 sentences that encompass relations already known in databases. By manually curating the remaining 1625 top-ranking sentences, we obtained more than 300 validated regulatory relationships that were not present in a regulatory database before. Full-text curation allowed us to obtain detailed information on the strength of experimental evidences supporting a relationship. We were able to increase curated information about the human core transcriptional network by >60% compared with the current content of regulatory databases. We observed improved performance when using the network for disease gene prioritization compared with the state-of-the-art. Web-service is freely accessible at http://fastforward.sys-bio.net/. leser@informatik.hu-berlin.de or nils.bluethgen@charite.de Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For

  12. Gene regulatory networks that control the specification of neural-crest cells in the lamprey.

    PubMed

    Nikitina, Natalya V; Bronner-Fraser, Marianne

    2009-04-01

    The lamprey is the only basal vertebrate in which large-scale gene perturbation analyses are feasible at present. Studies on this unique animal model promise to contribute both to the understanding of the basic neural-crest gene regulatory network architecture, and evolution of the neural crest. In this review, we summarize the currently known regulatory relationships underlying formation of the vertebrate neural crest, and discuss new ways of addressing the many remaining questions using lamprey as an experimental model.

  13. Identification of critical regulatory genes in cancer signaling network using controllability analysis

    NASA Astrophysics Data System (ADS)

    Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

    2017-05-01

    Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

  14. Gene regulatory network plasticity predates a switch in function of a conserved transcription regulator.

    PubMed

    Nocedal, Isabel; Mancera, Eugenio; Johnson, Alexander D

    2017-03-22

    The rewiring of gene regulatory networks can generate phenotypic novelty. It remains an open question, however, how the large number of connections needed to form a novel network arise over evolutionary time. Here, we address this question using the network controlled by the fungal transcription regulator Ndt80. This conserved protein has undergone a dramatic switch in function-from an ancestral role regulating sporulation to a derived role regulating biofilm formation. This switch in function corresponded to a large-scale rewiring of the genes regulated by Ndt80. However, we demonstrate that the Ndt80-target gene connections were undergoing extensive rewiring prior to the switch in Ndt80's regulatory function. We propose that extensive drift in the Ndt80 regulon allowed for the exploration of alternative network structures without a loss of ancestral function, thereby facilitating the formation of a network with a new function.

  15. Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors

    PubMed Central

    Sun, Mengyang; Cheng, Xianrui; Socolar, Joshua E. S.

    2013-01-01

    A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks. PMID:23822502

  16. Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

    PubMed Central

    Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E.; Balázsi, Gábor; Gennaro, Maria Laura

    2016-01-01

    Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics. PMID:27029515

  17. Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis.

    PubMed

    Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E; Balázsi, Gábor; Gennaro, Maria Laura

    2016-03-31

    Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics.

  18. Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors.

    PubMed

    Sun, Mengyang; Cheng, Xianrui; Socolar, Joshua E S

    2013-06-01

    A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks.

  19. A new approach for modelling gene regulatory networks using fuzzy petri nets.

    PubMed

    Hamed, Raed I; Ahson, S I; Parveen, R

    2010-02-04

    Gene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rulebased reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.

  20. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

    PubMed Central

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

  1. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe.

    PubMed

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

  2. Principles of dynamical modularity in biological regulatory networks

    PubMed Central

    Deritei, Dávid; Aird, William C.; Ercsey-Ravasz, Mária; Regan, Erzsébet Ravasz

    2016-01-01

    Intractable diseases such as cancer are associated with breakdown in multiple individual functions, which conspire to create unhealthy phenotype-combinations. An important challenge is to decipher how these functions are coordinated in health and disease. We approach this by drawing on dynamical systems theory. We posit that distinct phenotype-combinations are generated by interactions among robust regulatory switches, each in control of a discrete set of phenotypic outcomes. First, we demonstrate the advantage of characterizing multi-switch regulatory systems in terms of their constituent switches by building a multiswitch cell cycle model which points to novel, testable interactions critical for early G2/M commitment to division. Second, we define quantitative measures of dynamical modularity, namely that global cell states are discrete combinations of switch-level phenotypes. Finally, we formulate three general principles that govern the way coupled switches coordinate their function. PMID:26979940

  3. Principles of dynamical modularity in biological regulatory networks.

    PubMed

    Deritei, Dávid; Aird, William C; Ercsey-Ravasz, Mária; Regan, Erzsébet Ravasz

    2016-03-16

    Intractable diseases such as cancer are associated with breakdown in multiple individual functions, which conspire to create unhealthy phenotype-combinations. An important challenge is to decipher how these functions are coordinated in health and disease. We approach this by drawing on dynamical systems theory. We posit that distinct phenotype-combinations are generated by interactions among robust regulatory switches, each in control of a discrete set of phenotypic outcomes. First, we demonstrate the advantage of characterizing multi-switch regulatory systems in terms of their constituent switches by building a multiswitch cell cycle model which points to novel, testable interactions critical for early G2/M commitment to division. Second, we define quantitative measures of dynamical modularity, namely that global cell states are discrete combinations of switch-level phenotypes. Finally, we formulate three general principles that govern the way coupled switches coordinate their function.

  4. Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.

    PubMed

    Tian, Tianhai

    2016-01-01

    The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.

  5. Using gene expression programming to infer gene regulatory networks from time-series data.

    PubMed

    Zhang, Yongqing; Pu, Yifei; Zhang, Haisen; Su, Yabo; Zhang, Lifang; Zhou, Jiliu

    2013-12-01

    Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Novel players in the AP2-miR172 regulatory network for common bean nodulation.

    PubMed

    Íñiguez, Luis P; Nova-Franco, Bárbara; Hernández, Georgina

    2015-01-01

    The intricate regulatory network for floral organogenesis in plants that includes AP2/ERF, SPL and AGL transcription factors, miR172 and miR156 along with other components is well documented, though its complexity and size keep increasing. The miR172/AP2 node was recently proposed as essential regulator in the legume-rhizobia nitrogen-fixing symbiosis. Research from our group contributed to demonstrate the control of common bean (Phaseolus vulgaris) nodulation by miR172c/AP2-1, however no other components of such regulatory network have been reported. Here we propose AGLs as new protagonists in the regulation of common bean nodulation and discuss the relevance of future deeper analysis of the complex AP2 regulatory network for nodule organogenesis in legumes.

  7. Duplication of a promiscuous transcription factor drives the emergence of a new regulatory network

    PubMed Central

    Pougach, Ksenia; Voet, Arnout; Kondrashov, Fyodor A.; Voordeckers, Karin; Christiaens, Joaquin F.; Baying, Bianka; Benes, Vladimir; Sakai, Ryo; Aerts, Jan; Zhu, Bo; Van Dijck, Patrick; Verstrepen, Kevin J.

    2014-01-01

    The emergence of new genes throughout evolution requires rewiring and extension of regulatory networks. However, the molecular details of how the transcriptional regulation of new gene copies evolves remain largely unexplored. Here we show how duplication of a transcription factor gene allowed the emergence of two independent regulatory circuits. Interestingly, the ancestral transcription factor was promiscuous and could bind different motifs in its target promoters. After duplication, one paralogue evolved increased binding specificity so that it only binds one type of motif, whereas the other copy evolved a decreased activity so that it only activates promoters that contain multiple binding sites. Interestingly, only a few mutations in both the DNA-binding domains and in the promoter binding sites were required to gradually disentangle the two networks. These results reveal how duplication of a promiscuous transcription factor followed by concerted cis and trans mutations allows expansion of a regulatory network. PMID:25204769

  8. Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development.

    PubMed

    Monzón-Sandoval, Jimena; Castillo-Morales, Atahualpa; Urrutia, Araxi O; Gutierrez, Humberto

    2016-05-12

    During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known. Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of pronounced changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each targeted by a distinct set of transcriptional regulators and associated to specific biological functions. Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.

  9. Genome-wide network of regulatory genes for construction of a chordate embryo.

    PubMed

    Shoguchi, Eiichi; Hamaguchi, Makoto; Satoh, Nori

    2008-04-15

    Animal development is controlled by gene regulation networks that are composed of sequence-specific transcription factors (TF) and cell signaling molecules (ST). Although housekeeping genes have been reported to show clustering in the animal genomes, whether the genes comprising a given regulatory network are physically clustered on a chromosome is uncertain. We examined this question in the present study. Ascidians are the closest living relatives of vertebrates, and their tadpole-type larva represents the basic body plan of chordates. The Ciona intestinalis genome contains 390 core TF genes and 119 major ST genes. Previous gene disruption assays led to the formulation of a basic chordate embryonic blueprint, based on over 3000 genetic interactions among 79 zygotic regulatory genes. Here, we mapped the regulatory genes, including all 79 regulatory genes, on the 14 pairs of Ciona chromosomes by fluorescent in situ hybridization (FISH). Chromosomal localization of upstream and downstream regulatory genes demonstrates that the components of coherent developmental gene networks are evenly distributed over the 14 chromosomes. Thus, this study provides the first comprehensive evidence that the physical clustering of regulatory genes, or their target genes, is not relevant for the genome-wide control of gene expression during development.

  10. Characterization and merger of oscillatory mechanisms in an artificial genetic regulatory network

    NASA Astrophysics Data System (ADS)

    Yang, D.; Li, Y.; Kuznetsov, A.

    2009-09-01

    Regulatory molecular networks have numerous pharmacological and medical applications. The oscillatory mechanisms and the role of oscillations in these regulatory networks are not fully understood. In this paper, we explore two oscillatory mechanisms: the hysteresis-based relaxation oscillator and the repressilator. We combine these mechanisms into one regulatory network so that only two parameters, the strength of an additional regulatory connection and the timescale separation for one of the variables, control the transition from one mechanism to the other. Our data support a qualitative difference between the oscillatory mechanisms, but in the parameter space, we found a single oscillatory region, suggesting that the two mechanisms support each other. We examine interactions in a basic population: that is, a pair of the composite oscillators. We found that the relaxation oscillation mechanism is much more resistant to oscillatory death as the cells are diffusively coupled in a population. Additionally, stationary pattern formation has been found to accompany the relaxation oscillation but not the repressilator mechanism. These properties may guide the identification of oscillatory mechanisms in complex natural regulatory networks.

  11. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

    PubMed Central

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F. Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R.; Shachar-Hill, Yair; Hicks, Leslie M.; Gang, David R.

    2015-01-01

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. Evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism. PMID:26022256

  12. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

    SciTech Connect

    Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; Kim, Min -Jeong; Wang, Hongxia; Deshpande, Rahul R.; Shachar-Hill, Yair; Hicks, Leslie M.; Gang, David R.

    2015-05-28

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.

  13. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii.

    PubMed

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R; Shachar-Hill, Yair; Hicks, Leslie M; Gang, David R

    2015-08-01

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. Evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.

  14. Identification of cancer-related genes and motifs in the human gene regulatory network.

    PubMed

    Carson, Matthew B; Gu, Jianlei; Yu, Guangjun; Lu, Hui

    2015-08-01

    The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes. Potential mechanisms of these motifs and positions are discussed. These results could be used to identify other disease- and cancer-related genes and could also suggest mechanisms for how these genes relate to co-occurring diseases.

  15. Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma

    PubMed Central

    Sun, Jingchun; Gong, Xue; Purow, Benjamin; Zhao, Zhongming

    2012-01-01

    Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important

  16. A method for developing regulatory gene set networks to characterize complex biological systems.

    PubMed

    Suphavilai, Chayaporn; Zhu, Liugen; Chen, Jake Y

    2015-01-01

    Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

  17. Inference of gene regulatory networks from genome-wide knockout fitness data

    PubMed Central

    Wang, Liming; Wang, Xiaodong; Arkin, Adam P.; Samoilov, Michael S.

    2013-01-01

    Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis. Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/ Contact: wangx@ee.columbia.edu or mssamoilov@lbl.gov Supplementary information

  18. Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

    PubMed Central

    2012-01-01

    Background Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks. Results We developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses. Conclusions The bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean

  19. Stochasticity, Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks

    PubMed Central

    Sorek, Matan; Balaban, Nathalie Q.; Loewenstein, Yonatan

    2013-01-01

    It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population. PMID:23990765

  20. A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer

    PubMed Central

    Debily, Marie-Anne; Marhomy, Sandrine El; Boulanger, Virginie; Eveno, Eric; Mariage-Samson, Régine; Camarca, Alessandra; Auffray, Charles; Piatier-Tonneau, Dominique; Imbeaud, Sandrine

    2009-01-01

    Background The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. Principal Findings Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP−] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. Conclusions Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator. PMID:19262752

  1. Complex and unexpected dynamics in simple genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

    2014-03-01

    One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

  2. Analysis of Gene Sets Based on the Underlying Regulatory Network

    PubMed Central

    Michailidis, George

    2009-01-01

    Abstract Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast. PMID:19254181

  3. Characterizing the interplay betwen mulitple levels of organization within bacterial sigma factor regulatory networks

    SciTech Connect

    Yu, Qiu; Nagarajan, Harish; Embree, Mallory; Shieu, Wendy; Abate, Elisa; Juarez, Katy; Cho, Byung-Kwan; Elkins, James G; Nevin, Kelly P.; Barrett, Christian; Lovley, Derek; Palsson, Bernhard O.; Zengler, Karsten

    2013-01-01

    Bacteria contain multiple sigma factors, each targeting diverse, but often overlapping sets of promoters, thereby forming a complex network. The layout and deployment of such a sigma factor network directly impacts global transcriptional regulation and ultimately dictates the phenotype. Here we integrate multi-omic data sets to determine the topology, the operational, and functional states of the sigma factor network in Geobacter sulfurreducens, revealing a unique network topology of interacting sigma factors. Analysis of the operational state of the sigma factor network shows a highly modular structure with sN being the major regulator of energy metabolism. Surprisingly, the functional state of the network during the two most divergent growth conditions is nearly static, with sigma factor binding profiles almost invariant to environmental stimuli. This first comprehensive elucidation of the interplay between different levels of the sigma factor network organization is fundamental to characterize transcriptional regulatory mechanisms in bacteria.

  4. Regulatory network analysis of genes and microRNAs in human hepatoblastoma

    PubMed Central

    He, Jimin; Guo, Xiaoxin; Sun, Linlin; Wang, Ning; Bao, Jiwei

    2016-01-01

    Hepatoblastoma (HB) is a common type of primary tumor in children. Previous studies have examined the expression of genes, including transcription factors (TFs), target genes, host genes and microRNAs (miRNAs or miRs) associated with HB. However, the regulatory pathways of miRNAs and genes remain unclear. In the present study, a novel perspective is proposed, which focuses on HB and the associated regulatory pathways, to construct three networks at various levels, including a differentially expressed network, an associated network and a global network. Genes and miRNAs are considered as key factors in the network. In the three networks, the associations between each pair of factors, including TFs that regulate miRNAs, miRNAs that interact with target genes and miRNAs that are located at host genes, were analyzed. The differentially expressed network is considered to be the most crucial of the three networks. All factors in the differentially expressed network were mutated or differentially expressed, which indicated that the majority of the factors were cancerogenic factors that may lead to HB. In addition, the network contained numerous abnormal linkages that may trigger HB. If the expression of each factor was corrected to a normal level, HB may be successfully treated. The associated network included more HB-associated genes and miRNAs, and was useful for analyzing the pathogenesis of HB. By analyzing these close associations, the first and the last factor of the regulatory pathways were revealed to have important roles in HB. For example, v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) was observed to regulate Homo sapiens (hsa)-miR-221, hsa-miR-18a and hsa-miR-17-5p, but no miRNAs targeted MYCN. In conclusion, the pathways and mechanisms underlying HB were expounded in the present study, which proposed a fundamental hypothesis for additional studies. PMID:27895778

  5. Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

    PubMed Central

    2012-01-01

    Background Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling. Methods By utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks. Results Globally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis. Conclusion In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics. PMID:23282077

  6. Dissecting neural differentiation regulatory networks through epigenetic footprinting.

    PubMed

    Ziller, Michael J; Edri, Reuven; Yaffe, Yakey; Donaghey, Julie; Pop, Ramona; Mallard, William; Issner, Robbyn; Gifford, Casey A; Goren, Alon; Xing, Jeffrey; Gu, Hongcang; Cacchiarelli, Davide; Tsankov, Alexander M; Epstein, Charles; Rinn, John L; Mikkelsen, Tarjei S; Kohlbacher, Oliver; Gnirke, Andreas; Bernstein, Bradley E; Elkabetz, Yechiel; Meissner, Alexander

    2015-02-19

    Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.

  7. Dissecting the brown adipogenic regulatory network using integrative genomics

    PubMed Central

    Pradhan, Rachana N.; Bues, Johannes J.; Gardeux, Vincent; Schwalie, Petra C.; Alpern, Daniel; Chen, Wanze; Russeil, Julie; Raghav, Sunil K.; Deplancke, Bart

    2017-01-01

    Brown adipocytes regulate energy expenditure via mitochondrial uncoupling, which makes them attractive therapeutic targets to tackle obesity. However, the regulatory mechanisms underlying brown adipogenesis are still poorly understood. To address this, we profiled the transcriptome and chromatin state during mouse brown fat cell differentiation, revealing extensive gene expression changes and chromatin remodeling, especially during the first day post-differentiation. To identify putatively causal regulators, we performed transcription factor binding site overrepresentation analyses in active chromatin regions and prioritized factors based on their expression correlation with the bona-fide brown adipogenic marker Ucp1 across multiple mouse and human datasets. Using loss-of-function assays, we evaluated both the phenotypic effect as well as the transcriptomic impact of several putative regulators on the differentiation process, uncovering ZFP467, HOXA4 and Nuclear Factor I A (NFIA) as novel transcriptional regulators. Of these, NFIA emerged as the regulator yielding the strongest molecular and cellular phenotypes. To examine its regulatory function, we profiled the genomic localization of NFIA, identifying it as a key early regulator of terminal brown fat cell differentiation. PMID:28181539

  8. Dissecting neural differentiation regulatory networks through epigenetic footprinting

    PubMed Central

    Yaffe, Yakey; Donaghey, Julie; Pop, Ramona; Mallard, William; Issner, Robbyn; Gifford, Casey A.; Goren, Alon; Xing, Jeff; Gu, Hongcang; Cachiarelli, Davide; Tsankov, Alexander; Epstein, Chuck; Rinn, John R.; Mikkelsen, Tarjei S.; Kohlbacher, Oliver; Gnirke, Andreas; Bernstein, Bradley E.

    2014-01-01

    Human pluripotent stem cell derived models that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signaling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells (NPCs) in the embryonic and adult nervous system1-3. This can be exploited to isolate distinct populations of human embryonic stem (ES) cell derived NPCs4. Here, we report the transcriptional and epigenomic analysis of six consecutive stages derived from a HES5-GFP reporter ES cell line5 differentiated along the neural trajectory aimed at modeling key cell fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect the regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell state transition based on the progressive remodeling of the epigenetic landscape and then validated these through a pooled shRNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and show here that they are mediated by combinations of core and stage- specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation. PMID:25533951

  9. Sensing your surroundings: how transcription-regulatory networks of the cell discern environmental signals.

    PubMed

    Balázsi, Gábor; Oltvai, Zoltán N

    2005-05-03

    Accumulating evidence indicates that cells differentially regulate parts of their biochemical networks in various environmental conditions. Two recent studies, focused on the yeast transcription-regulatory network, have identified the characteristics and some of the regulatory logic that defines such conditional regulation on a system level. But what is the underlying basis of such environment-dependent dynamic network utilization? We propose that with simultaneous changes in many environmental variables, cells detect and process the incoming pieces of information individually with the use of receptors and sensor transcription factors specialized to a given type of signal. In turn, transcriptional subnetworks affected by the activity of these proteins reassemble the processed signals deeper inside the network, ultimately resulting in the development of an integrated cellular response.

  10. Ingeneue: a software tool to simulate and explore genetic regulatory networks.

    PubMed

    Kim, Kerry J

    2009-01-01

    Here I describe how to use Ingeneue, a software tool for constructing, simulating, and exploring models of gene regulatory networks. Ingeneue is an open source, extensible Java application that allows users to rapidly build ordinary differential equation models of a gene regulatory network without requiring extensive programming or mathematical skills. Models can be in a single cell or 2D sheet of cells, and Ingeneue is well suited for simulating both oscillatory and pattern forming networks. Ingeneue provides features to allow rapid model construction and debugging, sophisticated visualization and statistical tools for model exploration, and a powerful framework for searching parameter space for desired behavior. This chapter provides an overview of the mathematical theory and operation of Ingeneue, and detailed walkthroughs demonstrating how to use the main features and how to construct networks in Ingeneue.

  11. Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

    PubMed Central

    Batt, Gregory; Page, Michel; Cantone, Irene; Goessler, Gregor; Monteiro, Pedro; de Jong, Hidde

    2010-01-01

    Motivation: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior. Results: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust. Availability: GNA and the IRMA model are available at http://ibis.inrialpes.fr/ Contact: gregory.batt@inria.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20823328

  12. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

    PubMed Central

    2012-01-01

    Background Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis

  13. Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

    PubMed Central

    Villegas, Pablo; Ruiz-Franco, José; Hidalgo, Jorge; Muñoz, Miguel A.

    2016-01-01

    Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way –even for asynchronous updating rules– and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity. PMID:27713479

  14. Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

    NASA Astrophysics Data System (ADS)

    Villegas, Pablo; Ruiz-Franco, José; Hidalgo, Jorge; Muñoz, Miguel A.

    2016-10-01

    Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way -even for asynchronous updating rules- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

  15. Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks.

    PubMed

    Villegas, Pablo; Ruiz-Franco, José; Hidalgo, Jorge; Muñoz, Miguel A

    2016-10-07

    Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way -even for asynchronous updating rules- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

  16. DAX1 regulatory networks unveil conserved and potentially new functions.

    PubMed

    Martins, Rute S T; Power, Deborah M; Fuentes, Juan; Deloffre, Laurence A M; Canário, Adelino V M

    2013-11-01

    DAX1 is an orphan nuclear receptor with actions in mammalian sex determination, regulation of steroidogenesis, embryonic development and neural differentiation. Conserved patterns of DAX1 gene expression from mammals to fish have been taken to suggest conserved function. In the present study, the European sea bass, Dicentrarchus labrax, DAX1 promoter was isolated and its conserved features compared to other fish and mammalian DAX1 promoters in order to derive common regulators and functional gene networks. Fish and mammalian DAX1 promoters share common sets of transcription factor frameworks which were also present in the promoter region of another 127 genes. Pathway analysis clustered these into candidate gene networks associated with the fish and mammalian DAX1. The networks identified are concordant with described functions for DAX1 in embryogenesis, regulation of transcription, endocrine development and steroid production. Novel candidate gene network partners were also identified, which implicate DAX1 in ion homeostasis and transport, lipid transport and skeletal development. Experimental evidence is provided supporting roles for DAX1 in steroid signalling and osmoregulation in fish. These results highlight the usefulness of the in silico comparative approach to analyse gene regulation for hypothesis generation. Conserved promoter architecture can be used also to predict potentially new gene functions. The approach reported can be applied to genes from model and non-model species.

  17. Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

    PubMed Central

    2011-01-01

    Background Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. Results This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by

  18. Characterization of the Neisseria gonorrhoeae Iron and Fur Regulatory Network.

    PubMed

    Yu, Chunxiao; McClure, Ryan; Nudel, Kathleen; Daou, Nadine; Genco, Caroline Attardo

    2016-08-15

    The Neisseria gonorrhoeae ferric uptake regulator (Fur) protein controls expression of iron homeostasis genes in response to intracellular iron levels. In this study, using transcriptome sequencing (RNA-seq) analysis of an N. gonorrhoeae fur strain, we defined the gonococcal Fur and iron regulons and characterized Fur-controlled expression of an ArsR-like DNA binding protein. We observed that 158 genes (8% of the genome) showed differential expression in response to iron in an N. gonorrhoeae wild-type or fur strain, while 54 genes exhibited differential expression in response to Fur. The Fur regulon was extended to additional regulators, including NrrF and 13 other small RNAs (sRNAs), and two transcriptional factors. One transcriptional factor, coding for an ArsR-like regulator (ArsR), exhibited increased expression under iron-replete conditions in the wild-type strain but showed decreased expression across iron conditions in the fur strain, an effect that was reversed in a fur-complemented strain. Fur was shown to bind to the promoter region of the arsR gene downstream of a predicted σ(70) promoter region. Electrophoretic mobility shift assay (EMSA) analysis confirmed binding of the ArsR protein to the norB promoter region, and sequence analysis identified two additional putative targets, NGO1411 and NGO1646. A gonococcal arsR strain demonstrated decreased survival in human endocervical epithelial cells compared to that of the wild-type and arsR-complemented strains, suggesting that the ArsR regulon includes genes required for survival in host cells. Collectively, these results demonstrate that the N. gonorrhoeae Fur functions as a global regulatory protein to repress or activate expression of a large repertoire of genes, including additional transcriptional regulatory proteins. Gene regulation in bacteria in response to environmental stimuli, including iron, is of paramount importance to both bacterial replication and, in the case of pathogenic bacteria

  19. Characterization of the Neisseria gonorrhoeae Iron and Fur Regulatory Network

    PubMed Central

    Yu, Chunxiao; McClure, Ryan; Daou, Nadine

    2016-01-01

    ABSTRACT The Neisseria gonorrhoeae ferric uptake regulator (Fur) protein controls expression of iron homeostasis genes in response to intracellular iron levels. In this study, using transcriptome sequencing (RNA-seq) analysis of an N. gonorrhoeae fur strain, we defined the gonococcal Fur and iron regulons and characterized Fur-controlled expression of an ArsR-like DNA binding protein. We observed that 158 genes (8% of the genome) showed differential expression in response to iron in an N. gonorrhoeae wild-type or fur strain, while 54 genes exhibited differential expression in response to Fur. The Fur regulon was extended to additional regulators, including NrrF and 13 other small RNAs (sRNAs), and two transcriptional factors. One transcriptional factor, coding for an ArsR-like regulator (ArsR), exhibited increased expression under iron-replete conditions in the wild-type strain but showed decreased expression across iron conditions in the fur strain, an effect that was reversed in a fur-complemented strain. Fur was shown to bind to the promoter region of the arsR gene downstream of a predicted σ70 promoter region. Electrophoretic mobility shift assay (EMSA) analysis confirmed binding of the ArsR protein to the norB promoter region, and sequence analysis identified two additional putative targets, NGO1411 and NGO1646. A gonococcal arsR strain demonstrated decreased survival in human endocervical epithelial cells compared to that of the wild-type and arsR-complemented strains, suggesting that the ArsR regulon includes genes required for survival in host cells. Collectively, these results demonstrate that the N. gonorrhoeae Fur functions as a global regulatory protein to repress or activate expression of a large repertoire of genes, including additional transcriptional regulatory proteins. IMPORTANCE Gene regulation in bacteria in response to environmental stimuli, including iron, is of paramount importance to both bacterial replication and, in the case of pathogenic

  20. Coding and non-coding gene regulatory networks underlie the immune response in liver cirrhosis

    PubMed Central

    Zhang, Xueming; Huang, Yongming; Yang, Zhengpeng; Zhang, Yuguo; Zhang, Weihui; Gao, Zu-hua; Xue, Dongbo

    2017-01-01

    Liver cirrhosis is recognized as being the consequence of immune-mediated hepatocyte damage and repair processes. However, the regulation of these immune responses underlying liver cirrhosis has not been elucidated. In this study, we used GEO datasets and bioinformatics methods to established coding and non-coding gene regulatory networks including transcription factor-/lncRNA-microRNA-mRNA, and competing endogenous RNA interaction networks. Our results identified 2224 mRNAs, 70 lncRNAs and 46 microRNAs were differentially expressed in liver cirrhosis. The transcription factor -/lncRNA- microRNA-mRNA network we uncovered that results in immune-mediated liver cirrhosis is comprised of 5 core microRNAs (e.g., miR-203; miR-219-5p), 3 transcription factors (i.e., FOXP3, ETS1 and FOS) and 7 lncRNAs (e.g., ENTS00000671336, ENST00000575137). The competing endogenous RNA interaction network we identified includes a complex immune response regulatory subnetwork that controls the entire liver cirrhosis network. Additionally, we found 10 overlapping GO terms shared by both liver cirrhosis and hepatocellular carcinoma including “immune response” as well. Interestingly, the overlapping differentially expressed genes in liver cirrhosis and hepatocellular carcinoma were enriched in immune response-related functional terms. In summary, a complex gene regulatory network underlying immune response processes may play an important role in the development and progression of liver cirrhosis, and its development into hepatocellular carcinoma. PMID:28355233

  1. Regulatory Networks in Pollen Development under Cold Stress

    PubMed Central

    Sharma, Kamal D.; Nayyar, Harsh

    2016-01-01

    Cold stress modifies anthers’ metabolic pathways to induce pollen sterility. Cold-tolerant plants, unlike the susceptible ones, produce high proportion of viable pollen. Anthers in susceptible plants, when exposed to cold stress, increase abscisic acid (ABA) metabolism and reduce ABA catabolism. Increased ABA negatively regulates expression of tapetum cell wall bound invertase and monosaccharide transport genes resulting in distorted carbohydrate pool in anther. Cold-stress also reduces endogenous levels of the bioactive gibberellins (GAs), GA4 and GA7, in susceptible anthers by repression of the GA biosynthesis genes. Here, we discuss recent findings on mechanisms of cold susceptibility in anthers which determine pollen sterility. We also discuss differences in regulatory pathways between cold-stressed anthers of susceptible and tolerant plants that decide pollen sterility or viability. PMID:27066044

  2. Enhancer Variants Synergistically Drive Dysfunction of a Gene Regulatory Network In Hirschsprung Disease

    SciTech Connect

    Chatterjee, Sumantra; Kapoor, Ashish; Akiyama, Jennifer A.; Auer, Dallas R.; Lee, Dongwon; Gabriel, Stacey; Berrios, Courtney; Pennacchio, Len A.; Chakravarti, Aravinda

    2016-10-01

    Common sequence variants in cis-regulatory elements (CREs) are suspected etiological causes of complex disorders. We previously identified an intronic enhancer variant in the RET gene disrupting SOX10 binding and increasing Hirschsprung disease (HSCR) risk 4-fold. We now show that two other functionally independent CRE variants, one binding Gata2 and the other binding Rarb, also reduce Ret expression and increase risk 2- and 1.7-fold. By studying human and mouse fetal gut tissues and cell lines, we demonstrate that reduced RET expression propagates throughout its gene regulatory network, exerting effects on both its positive and negative feedback components. We also provide evidence that the presence of a combination of CRE variants synergistically reduces RET expression and its effects throughout the GRN. These studies show how the effects of functionally independent non-coding variants in a coordinated gene regulatory network amplify their individually small effects, providing a model for complex disorders.

  3. Toward a complete in silico, multi-layered embryonic stem cell regulatory network

    PubMed Central

    Xu, Huilei; Schaniel, Christoph; Lemischka, Ihor R.; Ma’ayan, Avi

    2010-01-01

    Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multilayered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature. PMID:20890967

  4. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle

    USDA-ARS?s Scientific Manuscript database

    Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e., hypothal...

  5. Development of Bioinformatic and Experimental Technologies for Identification of Prokaryotic Regulatory Networks

    SciTech Connect

    Lawrence, Charles E; McCue, Lee Ann

    2008-07-31

    The transcription regulatory network is arguably the most important foundation of cellular function, since it exerts the most fundamental control over the abundance of virtually all of a cell’s functional macromolecules. The two major components of a prokaryotic cell’s transcription regulation network are the transcription factors (TFs) and the transcription factor binding sites (TFBS); these components are connected by the binding of TFs to their cognate TFBS under appropriate environmental conditions. Comparative genomics has proven to be a powerful bioinformatics method with which to study transcription regulation on a genome-wide level. We have further extended comparative genomics technologies that we introduced over the last several years. Specifically, we developed and applied statistical approaches to analysis of correlated sequence data (i.e., sequences from closely related species). We also combined these technologies with functional genomic, proteomic and sequence data from multiple species, and developed computational technologies that provide inferences on the regulatory network connections, identifying the cognate transcription factor for predicted regulatory sites. Arguably the most important contribution of this work emerged in the course of the project. Specifically, the development of novel procedures of estimation and prediction in discrete high-D settings has broad implications for biology, genomics and well beyond. We showed that these procedures enjoy advantages over existing technologies in the identification of TBFS. These efforts are aimed toward identifying a cell’s complete transcription regulatory network and underlying molecular mechanisms.

  6. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    USDA-ARS?s Scientific Manuscript database

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  7. Identification and reconstitution of genetic regulatory networks for improved microbial tolerance to isooctane.

    PubMed

    Kang, Aram; Chang, Matthew Wook

    2012-04-01

    Microbial tolerance to hydrocarbons has been studied in an effort to improve the productivity of biochemical processes and to enhance the efficiency of hydrocarbon bioremediation. Despite these studies, few attempts have been made to design rational strategies to improve microbial tolerance to hydrocarbons. Herein, we present an engineering framework that enables us to harness our understanding of genetic regulatory networks to improve hydrocarbon tolerance. In this study, isooctane was used as a representative hydrocarbon due to its use in petroleum refining and in biochemical processes. To increase isooctane tolerance, we first identified essential transcriptional determinants and genetic regulatory networks underlying cellular responses to isooctane in Escherichia coli using genome-wide microarray analysis. Based on functional transcriptome and bioinformatics analysis, a range of combinations of transcription factors whose activity was predictably perturbed by isooctane were knocked out and overexpressed to reconstitute the regulatory networks. We demonstrated that the reconstitution of the regulatory networks led to a significant improvement in isooctane tolerance, and especially, engineered E. coli strains lacking and overexpressing some of the perturbed transcription factors showed 3- to 5-fold improvement. This microbe with high tolerance to isooctane can be harnessed for biochemical processes, fuel oil bioremediation and metabolic engineering for biofuel production. Furthermore, we envision that the engineering framework employed to improve the tolerance in this study can be exploited for developing other microbes with desired phenotypes.

  8. Statistical inference and reverse engineering of gene regulatory networks from observational expression data.

    PubMed

    Emmert-Streib, Frank; Glazko, Galina V; Altay, Gökmen; de Matos Simoes, Ricardo

    2012-01-01

    In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.

  9. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data

    PubMed Central

    2013-01-01

    Background High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. Results We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. Conclusions We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments. PMID:24053776

  10. Integration of Metabolic and Gene Regulatory Networks Modulates The C. elegans Dietary Response

    PubMed Central

    Arda, H. Efsun; Zhu, Lihua Julie; Walhout, Albertha J.M.

    2013-01-01

    SUMMARY Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network comprised of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. PMID:23540702

  11. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    PubMed

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus.

  12. Reverse engineering gene regulatory network from microarray data using linear time-variant model

    PubMed Central

    2010-01-01

    Background Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm. Results To assess the potency of the proposed work, a well known nonlinear synthetic network has been used. The reconstruction method has inferred this synthetic network topology and the associated regulatory parameters with high accuracy from both the noise-free and noisy time-series data. For validation purposes, the proposed approach is also applied to the simulated expression dataset of cAMP oscillations in Dictyostelium discoideum and has proved it's strength in finding the correct regulations. The strength of this work has also been verified by analyzing the real expression dataset of SOS DNA repair system in Escherichia coli and it has succeeded in finding more correct and reasonable regulations as compared to various existing works. Conclusion By the proposed approach, the gene interaction networks have been inferred in an efficient manner from both the synthetic, simulated cAMP oscillation expression data and real expression data. The computational time of this approach is also considerably smaller, which makes it to be more suitable for larger network reconstruction. Thus the proposed approach can serve as an initiate for the future researches regarding the associated area. PMID:20122231

  13. Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations

    PubMed Central

    Cai, Xiaodong; Bazerque, Juan Andrés; Giannakis, Georgios B.

    2013-01-01

    Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request. PMID:23717196

  14. DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data

    PubMed Central

    2012-01-01

    Background Modeling dynamic regulatory networks is a major challenge since much of the protein-DNA interaction data available is static. The Dynamic Regulatory Events Miner (DREM) uses a Hidden Markov Model-based approach to integrate this static interaction data with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM has been used successfully in diverse areas of biological research. However, several issues were not addressed by the original version. Results DREM 2.0 is a comprehensive software for reconstructing dynamic regulatory networks that supports interactive graphical or batch mode. With version 2.0 a set of new features that are unique in comparison with other softwares are introduced. First, we provide static interaction data for additional species. Second, DREM 2.0 now accepts continuous binding values and we added a new method to utilize TF expression levels when searching for dynamic models. Third, we added support for discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. Finally, we improved the visualization to support the new features. Combined, these changes improve the ability of DREM 2.0 to accurately recover dynamic regulatory networks and make it much easier to use it for analyzing such networks in several species with varying degrees of interaction information. Conclusions DREM 2.0 provides a unique framework for constructing and visualizing dynamic regulatory networks. DREM 2.0 can be downloaded from: www.sb.cs.cmu.edu/drem. PMID:22897824

  15. CyTargetLinker: a cytoscape app to integrate regulatory interactions in network analysis.

    PubMed

    Kutmon, Martina; Kelder, Thomas; Mandaviya, Pooja; Evelo, Chris T A; Coort, Susan L

    2013-01-01

    The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes. We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers. Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation. CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable

  16. Assessment of transfer methods for comparative genomics of regulatory networks in bacteria.

    PubMed

    Kılıç, Sefa; Erill, Ivan

    2016-08-31

    Comparative genomics can leverage the vast amount of available genomic sequences to reconstruct and analyze transcriptional regulatory networks in Bacteria, but the efficacy of this approach hinges on the ability to transfer regulatory network information from reference species to the genomes under analysis. Several methods have been proposed to transfer regulatory information between bacterial species, but the paucity and distributed nature of experimental information on bacterial transcriptional networks have prevented their systematic evaluation. We report the compilation of a large catalog of transcription factor-binding sites across Bacteria and its use to systematically benchmark proposed transfer methods across pairs of bacterial species. We evaluate motif- and accuracy-based metrics to assess the results of regulatory network transfer and we identify the precision-recall area-under-the-curve as the best metric for this purpose due to the large class-imbalanced nature of the problem. Methods assuming conservation of the transcription factor-binding motif (motif-based) are shown to substantially outperform those assuming conservation of regulon composition (network-based), even though their efficiency can decrease sharply with increasing phylogenetic distance. Variations of the basic motif-based transfer method do not yield significant improvements in transfer accuracy. Our results indicate that detection of a large enough number of regulated orthologs is critical for network-based transfer methods, but that relaxing orthology requirements does not improve results. Using the transcriptional regulators LexA and Fur as case examples, we also show how DNA-binding domain sequence similarity can yield confounding results as an indicator of transfer efficiency for motif-based methods. Counter to standard practice, our evaluation of metrics to assess the efficiency of methods for regulatory network information transfer reveals that the area under precision

  17. Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle.

    PubMed

    Bushel, Pierre R; Heard, Nicholas A; Gutman, Roee; Liu, Liwen; Peddada, Shyamal D; Pyne, Saumyadipta

    2009-09-16

    Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast. By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs. Using a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we

  18. Structures and Boolean Dynamics in Gene Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Szedlak, Anthony

    This dissertation discusses the topological and dynamical properties of GRNs in cancer, and is divided into four main chapters. First, the basic tools of modern complex network theory are introduced. These traditional tools as well as those developed by myself (set efficiency, interset efficiency, and nested communities) are crucial for understanding the intricate topological properties of GRNs, and later chapters recall these concepts. Second, the biology of gene regulation is discussed, and a method for disease-specific GRN reconstruction developed by our collaboration is presented. This complements the traditional exhaustive experimental approach of building GRNs edge-by-edge by quickly inferring the existence of as of yet undiscovered edges using correlations across sets of gene expression data. This method also provides insight into the distribution of common mutations across GRNs. Third, I demonstrate that the structures present in these reconstructed networks are strongly related to the evolutionary histories of their constituent genes. Investigation of how the forces of evolution shaped the topology of GRNs in multicellular organisms by growing outward from a core of ancient, conserved genes can shed light upon the ''reverse evolution'' of normal cells into unicellular-like cancer states. Next, I simulate the dynamics of the GRNs of cancer cells using the Hopfield model, an infinite range spin-glass model designed with the ability to encode Boolean data as attractor states. This attractor-driven approach facilitates the integration of gene expression data into predictive mathematical models. Perturbations representing therapeutic interventions are applied to sets of genes, and the resulting deviations from their attractor states are recorded, suggesting new potential drug targets for experimentation. Finally, I extend the Hopfield model to modular networks, cyclic attractors, and complex attractors, and apply these concepts to simulations of the cell cycle

  19. Computational studies of gene regulatory networks: in numero molecular biology.

    PubMed

    Hasty, J; McMillen, D; Isaacs, F; Collins, J J

    2001-04-01

    Remarkable progress in genomic research is leading to a complete map of the building blocks of biology. Knowledge of this map is, in turn, setting the stage for a fundamental description of cellular function at the DNA level. Such a description will entail an understanding of gene regulation, in which proteins often regulate their own production or that of other proteins in a complex web of interactions. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.

  20. The Pho regulon: a huge regulatory network in bacteria

    PubMed Central

    Santos-Beneit, Fernando

    2015-01-01

    One of the most important achievements of bacteria is its capability to adapt to the changing conditions of the environment. The competition for nutrients with other microorganisms, especially in the soil, where nutritional conditions are more variable, has led bacteria to evolve a plethora of mechanisms to rapidly fine-tune the requirements of the cell. One of the essential nutrients that are normally found in low concentrations in nature is inorganic phosphate (Pi). Bacteria, as well as other organisms, have developed several systems to cope for the scarcity of this nutrient. To date, the unique mechanism responding to Pi starvation known in detail is the Pho regulon, which is normally controlled by a two component system and constitutes one of the most sensible and efficient regulatory mechanisms in bacteria. Many new members of the Pho regulon have emerged in the last years in several bacteria; however, there are still many unknown questions regarding the activation and function of the whole system. This review describes the most important findings of the last three decades in relation to Pi regulation in bacteria, including: the PHO box, the Pi signaling pathway and the Pi starvation response. The role of the Pho regulon in nutritional regulation cross-talk, secondary metabolite production, and pathogenesis is discussed in detail. PMID:25983732

  1. Targeting Immune Regulatory Networks to Counteract Immune Suppression in Cancer.

    PubMed

    Camisaschi, Chiara; Vallacchi, Viviana; Vergani, Elisabetta; Tazzari, Marcella; Ferro, Simona; Tuccitto, Alessandra; Kuchuk, Olga; Shahaj, Eriomina; Sulsenti, Roberta; Castelli, Chiara; Rodolfo, Monica; Rivoltini, Licia; Huber, Veronica

    2016-11-04

    The onset of cancer is unavoidably accompanied by suppression of antitumor immunity. This occurs through mechanisms ranging from the progressive accumulation of regulatory immune cells associated with chronic immune stimulation and inflammation, to the expression of immunosuppressive molecules. Some of them are being successfully exploited as therapeutic targets, with impressive clinical results achieved in patients, as in the case of immune checkpoint inhibitors. To limit immune attack, tumor cells exploit specific pathways to render the tumor microenvironment hostile for antitumor effector cells. Local acidification might, in fact, anergize activated T cells and facilitate the accumulation of immune suppressive cells. Moreover, the release of extracellular vesicles by tumor cells can condition distant immune sites contributing to the onset of systemic immune suppression. Understanding which mechanisms may be prevalent in specific cancers or disease stages, and identifying possible strategies to counterbalance would majorly contribute to improving clinical efficacy of cancer immunotherapy. Here, we intend to highlight these mechanisms, how they could be targeted and the tools that might be available in the near future to achieve this goal.

  2. Targeting Immune Regulatory Networks to Counteract Immune Suppression in Cancer

    PubMed Central

    Camisaschi, Chiara; Vallacchi, Viviana; Vergani, Elisabetta; Tazzari, Marcella; Ferro, Simona; Tuccitto, Alessandra; Kuchuk, Olga; Shahaj, Eriomina; Sulsenti, Roberta; Castelli, Chiara; Rodolfo, Monica; Rivoltini, Licia; Huber, Veronica

    2016-01-01

    The onset of cancer is unavoidably accompanied by suppression of antitumor immunity. This occurs through mechanisms ranging from the progressive accumulation of regulatory immune cells associated with chronic immune stimulation and inflammation, to the expression of immunosuppressive molecules. Some of them are being successfully exploited as therapeutic targets, with impressive clinical results achieved in patients, as in the case of immune checkpoint inhibitors. To limit immune attack, tumor cells exploit specific pathways to render the tumor microenvironment hostile for antitumor effector cells. Local acidification might, in fact, anergize activated T cells and facilitate the accumulation of immune suppressive cells. Moreover, the release of extracellular vesicles by tumor cells can condition distant immune sites contributing to the onset of systemic immune suppression. Understanding which mechanisms may be prevalent in specific cancers or disease stages, and identifying possible strategies to counterbalance would majorly contribute to improving clinical efficacy of cancer immunotherapy. Here, we intend to highlight these mechanisms, how they could be targeted and the tools that might be available in the near future to achieve this goal. PMID:27827921

  3. Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

    PubMed

    Vinciotti, Veronica; Augugliaro, Luigi; Abbruzzo, Antonino; Wit, Ernst C

    2016-06-01

    Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order - some entries of the precision matrix are a priori zeros - or equal dependency strengths across time lags - some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.

  4. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

    PubMed

    Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

    2017-05-01

    Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

  5. Lessons from the modular organization of the transcriptional regulatory network of Bacillus subtilis.

    PubMed

    Freyre-González, Julio A; Manjarrez-Casas, Alejandra M; Merino, Enrique; Martinez-Nuñez, Mario; Perez-Rueda, Ernesto; Gutiérrez-Ríos, Rosa-María

    2013-11-16

    The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution. We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the cascades that define relevant cellular processes in this organism. We discussed that some particular functions were distributed in more than one module and that some modules contained more than one related function. We confirm that the presence of paralogous proteins confers advantages to B. subtilis to adapt and select strategies to successfully face the extreme and changing environmental conditions in which it lives. The intricate organization is the product of a non-random network evolution that primarily follows a hierarchical organization based on the presence of transcription and σ factor, which is reflected in the connections that exist within and between modules.

  6. Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS

    PubMed Central

    Stelniec-Klotz, Iwona; Legewie, Stefan; Tchernitsa, Oleg; Witzel, Franziska; Klinger, Bertram; Sers, Christine; Herzel, Hanspeter; Blüthgen, Nils; Schäfer, Reinhold

    2012-01-01

    RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here, we used a reverse-engineering approach in an ovarian cancer model to reconstruct KRAS oncogene-dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT–PCR and western blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions, we analysed growth parameters and transcriptional deregulation in the KRAS-transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype. PMID:22864383

  7. MicroRNA regulatory networks in idiopathic pulmonary fibrosis.

    PubMed

    Pandit, Kusum V; Milosevic, Jadranka

    2015-04-01

    Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal scarring lung disease of unknown etiology, characterized by changes in microRNA expression. Activation of transforming growth factor (TGF-β) is a key event in the development of IPF. Recent reports have also identified epigenetic modification as an important player in the pathogenesis of IPF. In this review, we summarize the main results of studies that address the role of microRNAs in IPF and highlight the synergistic actions of these microRNAs in regulating TGF-β, the primary fibrogenic mediator. We outline epigenetic regulation of microRNAs by methylation. Functional studies identify microRNAs that alter proliferative and migratory properties of fibroblasts, and induce phenotypic changes in epithelial cells consistent with epithelial-mesenchymal transition. Though these studies were performed in isolation, we identify multiple co-operative actions after assembling the results into a network. Construction of such networks will help identify disease-propelling hubs that can be targeted for therapeutic purposes.

  8. Sweet immunity in the plant circadian regulatory network.

    PubMed

    Bolouri Moghaddam, Mohammad Reza; Van den Ende, Wim

    2013-04-01

    All organisms have an internal timing mechanism, termed the circadian clock, to anticipate the light/dark cycle. The clock, with an oscillating rhythm that approximates 24h, is a rather robust system persisting to a great extent in continuous light and dark. It is widely accepted that plant growth and development are regulated by the clock, hormones, and sugar signals. On the one hand, sugar signalling can affect circadian rhythms by altering the expression pattern of clock-regulated genes. More in particular, the clock seems to be particularly sensitive to sucrose-mediated signalling which is also associated with immunity and abiotic stress responses. Also, hormonal interaction with the clock can contribute to appropriate plant immune responses. Recent data show a prominent role for the clock in growth and stress responses. On the other hand, the clock seems to be essential in controlling the gene expression and activity of an array of carbohydrate-metabolizing enzymes, suggesting a complex reciprocal relationship between the clock and metabolic signalling processes. Therefore, the clock fulfils a crucial role at the heart of cellular networks. The players involved in the complex plant circadian network and their possible contribution to the novel 'sweet immunity' concept are discussed.

  9. Extended evolution: A conceptual framework for integrating regulatory networks and niche construction

    PubMed Central

    Renn, Jürgen

    2015-01-01

    ABSTRACT This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path‐dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 565–577, 2015. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc. PMID:26097188

  10. Edge usage, motifs, and regulatory logic for cell cycling genetic networks

    NASA Astrophysics Data System (ADS)

    Zagorski, M.; Krzywicki, A.; Martin, O. C.

    2013-01-01

    The cell cycle is a tightly controlled process, yet it shows marked differences across species. Which of its structural features follow solely from the ability to control gene expression? We tackle this question in silico by examining the ensemble of all regulatory networks which satisfy the constraint of producing a given sequence of gene expressions. We focus on three cell cycle profiles coming from baker's yeast, fission yeast, and mammals. First, we show that the networks in each of the ensembles use just a few interactions that are repeatedly reused as building blocks. Second, we find an enrichment in network motifs that is similar in the two yeast cell cycle systems investigated. These motifs do not have autonomous functions, yet they reveal a regulatory logic for cell cycling based on a feed-forward cascade of activating interactions.

  11. Genome-wide analyses for dissecting gene regulatory networks in the shoot apical meristem.

    PubMed

    Bustamante, Mariana; Matus, José Tomás; Riechmann, José Luis

    2016-03-01

    Shoot apical meristem activity is controlled by complex regulatory networks in which components such as transcription factors, miRNAs, small peptides, hormones, enzymes and epigenetic marks all participate. Many key genes that determine the inherent characteristics of the shoot apical meristem have been identified through genetic approaches. Recent advances in genome-wide studies generating extensive transcriptomic and DNA-binding datasets have increased our understanding of the interactions within the regulatory networks that control the activity of the meristem, identifying new regulators and uncovering connections between previously unlinked network components. In this review, we focus on recent studies that illustrate the contribution of whole genome analyses to understand meristem function. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  12. Developmental gene regulatory networks in sea urchins and what we can learn from them

    PubMed Central

    Martik, Megan L.; Lyons, Deirdre C.; McClay, David R.

    2016-01-01

    Sea urchin embryos begin zygotic transcription shortly after the egg is fertilized.  Throughout the cleavage stages a series of transcription factors are activated and, along with signaling through a number of pathways, at least 15 different cell types are specified by the beginning of gastrulation.  Experimentally, perturbation of contributing transcription factors, signals and receptors and their molecular consequences enabled the assembly of an extensive gene regulatory network model.  That effort, pioneered and led by Eric Davidson and his laboratory, with many additional insights provided by other laboratories, provided the sea urchin community with a valuable resource.  Here we describe the approaches used to enable the assembly of an advanced gene regulatory network model describing molecular diversification during early development.  We then provide examples to show how a relatively advanced authenticated network can be used as a tool for discovery of how diverse developmental mechanisms are controlled and work. PMID:26962438

  13. Reverse engineering of gene regulatory network using restricted gene expression programming.

    PubMed

    Yang, Bin; Liu, Sanrong; Zhang, Wei

    2016-10-01

    Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

  14. Functional splicing network reveals extensive regulatory potential of the core spliceosomal machinery.

    PubMed

    Papasaikas, Panagiotis; Tejedor, J Ramón; Vigevani, Luisa; Valcárcel, Juan

    2015-01-08

    Pre-mRNA splicing relies on the poorly understood dynamic interplay between >150 protein components of the spliceosome. The steps at which splicing can be regulated remain largely unknown. We systematically analyzed the effect of knocking down the components of the splicing machinery on alternative splicing events relevant for cell proliferation and apoptosis and used this information to reconstruct a network of functional interactions. The network accurately captures known physical and functional associations and identifies new ones, revealing remarkable regulatory potential of core spliceosomal components, related to the order and duration of their recruitment during spliceosome assembly. In contrast with standard models of regulation at early steps of splice site recognition, factors involved in catalytic activation of the spliceosome display regulatory properties. The network also sheds light on the antagonism between hnRNP C and U2AF, and on targets of antitumor drugs, and can be widely used to identify mechanisms of splicing regulation.

  15. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    PubMed

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

    PubMed Central

    2011-01-01

    Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our

  17. Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

    PubMed Central

    Vignes, Matthieu; Vandel, Jimmy; Allouche, David; Ramadan-Alban, Nidal; Cierco-Ayrolles, Christine; Schiex, Thomas; Mangin, Brigitte; de Givry, Simon

    2011-01-01

    Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics. PMID:22216195

  18. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

    PubMed

    Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

    2010-04-21

    The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

  19. NetDiff – Bayesian model selection for differential gene regulatory network inference

    PubMed Central

    Thorne, Thomas

    2016-01-01

    Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation. PMID:27982083

  20. NetDiff - Bayesian model selection for differential gene regulatory network inference.

    PubMed

    Thorne, Thomas

    2016-12-16

    Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.

  1. Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms

    NASA Astrophysics Data System (ADS)

    Hernández-Lemus, Enrique; Velázquez-Fernández, David; Estrada-Gil, Jesús K.; Silva-Zolezzi, Irma; Herrera-Hernández, Miguel F.; Jiménez-Sánchez, Gerardo

    2009-12-01

    Most common pathologies in humans are not caused by the mutation of a single gene, rather they are complex diseases that arise due to the dynamic interaction of many genes and environmental factors. This plethora of interacting genes generates a complexity landscape that masks the real effects associated with the disease. To construct dynamic maps of gene interactions (also called genetic regulatory networks) we need to understand the interplay between thousands of genes. Several issues arise in the analysis of experimental data related to gene function: on the one hand, the nature of measurement processes generates highly noisy signals; on the other hand, there are far more variables involved (number of genes and interactions among them) than experimental samples. Another source of complexity is the highly nonlinear character of the underlying biochemical dynamics. To overcome some of these limitations, we generated an optimized method based on the implementation of a Maximum Entropy Formalism (MaxEnt) to deconvolute a genetic regulatory network based on the most probable meta-distribution of gene-gene interactions. We tested the methodology using experimental data for Papillary Thyroid Cancer (PTC) and Thyroid Goiter tissue samples. The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions. The group of observed interactions was validated by several (mostly in silico) means and sources. For the associated Papillary Thyroid Cancer Gene Regulatory Network (PTC-GRN) the majority of the nodes (genes) have very few links (interactions) whereas a small number of nodes are highly connected. PTC-GRN is also characterized by high clustering coefficients and network heterogeneity. These properties have been recognized as characteristic of topological robustness, and they have been largely described in relation to biological networks. A number of biological validity outcomes are discussed with regard to both the

  2. Initial deployment of the cardiogenic gene regulatory network in the basal chordate, Ciona intestinalis.

    PubMed

    Woznica, Arielle; Haeussler, Maximilian; Starobinska, Ella; Jemmett, Jessica; Li, Younan; Mount, David; Davidson, Brad

    2012-08-01

    The complex, partially redundant gene regulatory architecture underlying vertebrate heart formation has been difficult to characterize. Here, we dissect the primary cardiac gene regulatory network in the invertebrate chordate, Ciona intestinalis. The Ciona heart progenitor lineage is first specified by Fibroblast Growth Factor/Map Kinase (FGF/MapK) activation of the transcription factor Ets1/2 (Ets). Through microarray analysis of sorted heart progenitor cells, we identified the complete set of primary genes upregulated by FGF/Ets shortly after heart progenitor emergence. Combinatorial sequence analysis of these co-regulated genes generated a hypothetical regulatory code consisting of Ets binding sites associated with a specific co-motif, ATTA. Through extensive reporter analysis, we confirmed the functional importance of the ATTA co-motif in primary heart progenitor gene regulation. We then used the Ets/ATTA combination motif to successfully predict a number of additional heart progenitor gene regulatory elements, including an intronic element driving expression of the core conserved cardiac transcription factor, GATAa. This work significantly advances our understanding of the Ciona heart gene network. Furthermore, this work has begun to elucidate the precise regulatory architecture underlying the conserved, primary role of FGF/Ets in chordate heart lineage specification.

  3. Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

    PubMed Central

    Barah, Pankaj; B N, Mahantesha Naika; Jayavelu, Naresh Doni; Sowdhamini, Ramanathan; Shameer, Khader; Bones, Atle M.

    2016-01-01

    Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants. PMID:26681689

  4. Regulatory network analysis of microRNAs and genes in imatinib-resistant chronic myeloid leukemia.

    PubMed

    Soltani, Ismael; Gharbi, Hanen; Hassine, Islem Ben; Bouguerra, Ghada; Douzi, Kais; Teber, Mouheb; Abbes, Salem; Menif, Samia

    2016-09-16

    Targeted therapy in the form of selective breakpoint cluster region-abelson (BCR/ABL) tyrosine kinase inhibitor (imatinib mesylate) has successfully been introduced in the treatment of the chronic myeloid leukemia (CML). However, acquired resistance against imatinib mesylate (IM) has been reported in nearly half of patients and has been recognized as major issue in clinical practice. Multiple resistance genes and microRNAs (miRNAs) are thought to be involved in the IM resistance process. These resistance genes and miRNAs tend to interact with each other through a regulatory network. Therefore, it is crucial to study the impact of these interactions in the IM resistance process. The present study focused on miRNA and gene network analysis in order to elucidate the role of interacting elements and to understand their functional contribution in therapeutic failure. Unlike previous studies which were centered only on genes or miRNAs, the prime focus of the present study was on relationships. To this end, three regulatory networks including differentially expressed, related, and global networks were constructed and analyzed in search of similarities and differences. Regulatory associations between miRNAs and their target genes, transcription factors and miRNAs, as well as miRNAs and their host genes were also macroscopically investigated. Certain key pathways in the three networks, especially in the differentially expressed network, were featured. The differentially expressed network emerged as a fault map of IM-resistant CML. Theoretically, the IM resistance process could be prevented by correcting the included errors. The present network-based approach to study resistance miRNAs and genes might help in understanding the molecular mechanisms of IM resistance in CML as well as in the improvement of CML therapy.

  5. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    PubMed

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  6. Gibberellins - a multifaceted hormone in plant growth regulatory network.

    PubMed

    Gantait, Saikat; Sinniah, Uma Rani; Ali, Md Nasim; Sahu, Narayan Chandra

    2015-01-01

    Plants tend to acclimatize to unfavourable environs by integrating growth and development to environmentally activated signals. Phytohormones strongly regulate convergent developmental and stress adaptive procedures and synchronize cellular reaction to the exogenous and endogenous conditions within the adaptive signaling networks. Gibberellins (GA), a group of tetracyclic diterpenoids, being vital regulators of plant growth, are accountable for regulating several aspects of growth and development of higher plants. If the element of reproduction is considered as an absolute requisite then for a majority of the higher plants GA signaling is simply indispensable. Latest reports have revealed unique conflicting roles of GA and other phytohormones in amalgamating growth and development in plants through environmental signaling. Numerous physiological researches have detailed substantial crosstalk between GA and other hormones like abscisic acid, auxin, cytokinin, and jasmonic acid. In this review, a number of explanations and clarifications for this discrepancy are explored based on the crosstalk among GA and other phytohormones.

  7. Expanding the Regulatory Network for Meristem Size in Plants.

    PubMed

    Galli, Mary; Gallavotti, Andrea

    2016-06-01

    The remarkable plasticity of post-embryonic plant development is due to groups of stem-cell-containing structures called meristems. In the shoot, meristems continuously produce organs such as leaves, flowers, and stems. Nearly two decades ago the WUSCHEL/CLAVATA (WUS/CLV) negative feedback loop was established as being essential for regulating the size of shoot meristems by maintaining a delicate balance between stem cell proliferation and cell recruitment for the differentiation of lateral primordia. Recent research in various model species (Arabidopsis, tomato, maize, and rice) has led to discoveries of additional components that further refine and improve the current model of meristem regulation, adding new complexity to a vital network for plant growth and productivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Integration of Troponin I Phosphorylation with Cardiac Regulatory Networks

    PubMed Central

    Solaro, R. John; Henze, Marcus; Kobayashi, Tomoyoshi

    2013-01-01

    We focus here on the modulation of thin filament activity by cardiac troponin I (cTnI) phosphorylation as an integral and adaptive mechanism in cardiac homeostasis and as a mechanism vulnerable to maladaptive response to stress. We discuss a current concept of cTnI function in the A-band region of the sarcomere, and potential signaling to cTnI in a network involving the ends of the thin filaments at the Z-disk and the M-band regions. The cardiac sarcomere represents a remarkable set of interacting proteins that functions not only as a molecular machine generating the heartbeat, but also as a hub of signaling. We review how phosphorylation signaling to cardiac troponin I is integrated with parallel signals controlling excitation-contraction coupling, hypertrophy, and metabolism. PMID:23329791

  9. The role of DNA-binding specificity in the evolution of bacterial regulatory networks

    PubMed Central

    Lozada-Chávez, Irma; Angarica, Vladimir Espinosa; Collado-Vides, Julio; Contreras-Moreira, Bruno

    2008-01-01

    Understanding the mechanisms by which transcriptional regulatory networks (TRNs) change through evolution is a fundamental problem. Here we analyze this question using data from Escherichia coli and Bacillus subtilis, finding that paralogy relationships are insufficient to explain the global or local role observed for transcription factors (TFs) within regulatory networks. Our results provide a picture in which DNA-binding specificity, a molecular property that can be measured in different ways, is a predictor of the role of transcription factors. In particular, we observe that global regulators consistently display low binding specificities, while displaying comparatively higher expression values in microarray experiments. In addition, in this work we find a strong negative correlation between binding specificity and the number of co-regulators which help coordinate genetic expression at a genomic scale. A close look at several orthologous TFs, including FNR, a regulator found to be global in E. coli and local in B. subtilis, confirms the diagnostic value of specificity in order to understand their regulatory function, and also highlights the importance of evaluating the metabolic and ecological relevance of effectors as another variable in the evolutionary equation of regulatory networks. Finally, a general model is presented that integrates some evolutionary forces and molecular properties, aiming to explain how regulons grow and shrink, as bacteria tune their regulation to increase adaptation. PMID:18466918

  10. Establishment of apoptotic regulatory network for genetic markers of colorectal cancer.

    PubMed

    Hao, Yibin; Shan, Guoyong; Nan, Kejun

    2017-03-01

    Our purpose is to screen out genetic markers applicable to early diagnosis for colorectal cancer and to establish apoptotic regulatory network model for colorectal cancer, thereby providing theoretical evidence and targeted therapy for early diagnosis of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers applied to early diagnosis of colorectal cancer were searched to perform comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to establish apoptotic regulatory network model based on screened genetic markers, and then verification experiment was conducted. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, among which DCC shows highest diagnostic efficiency. GO analysis of genetic markers found that six genetic markers played role in biological process, molecular function and cellular component. It was indicated in apoptotic regulatory network built by KEGG analysis and verification experiment that WWOX could promote tumor cell apoptotic in colorectal cancer and elevate expression level of p53. The apoptotic regulatory model of colorectal cancer established in this study provides clinically theoretical evidence and targeted therapy for early diagnosis of colorectal cancer.

  11. Decoding regulatory landscape of somatic embryogenesis reveals differential regulatory networks between japonica and indica rice subspecies

    PubMed Central

    Indoliya, Yuvraj; Tiwari, Poonam; Chauhan, Abhisekh Singh; Goel, Ridhi; Shri, Manju; Bag, Sumit Kumar; Chakrabarty, Debasis

    2016-01-01

    Somatic embryogenesis is a unique process in plants and has considerable interest for biotechnological application. Compare to japonica, indica rice has been less responsive to in vitro culture. We used Illumina Hiseq 2000 sequencing platform for comparative transcriptome analysis between two rice subspecies at six different developmental stages combined with a tag-based digital gene expression profiling. Global gene expression among different samples showed greater complexity in japonica rice compared to indica which may be due to polyphyletic origin of two rice subspecies. Expression pattern in initial stage indicate major differences in proembryogenic callus induction phase that may serve as key regulator to observe differences between both subspecies. Our data suggests that phytohormone signaling pathways consist of elaborate networks with frequent crosstalk, thereby allowing plants to regulate somatic embryogenesis pathway. However, this crosstalk varies between the two rice subspecies. Down regulation of positive regulators of meristem development (i.e. KNOX, OsARF5) and up regulation of its counterparts (OsRRs, MYB, GA20ox1/GA3ox2) in japonica may be responsible for its better regeneration and differentiation of somatic embryos. Comprehensive gene expression information in the present experiment may also facilitate to understand the monocot specific meristem regulation for dedifferentiation of somatic cell to embryogenic cells. PMID:26973288

  12. Decoding regulatory landscape of somatic embryogenesis reveals differential regulatory networks between japonica and indica rice subspecies.

    PubMed

    Indoliya, Yuvraj; Tiwari, Poonam; Chauhan, Abhisekh Singh; Goel, Ridhi; Shri, Manju; Bag, Sumit Kumar; Chakrabarty, Debasis

    2016-03-14

    Somatic embryogenesis is a unique process in plants and has considerable interest for biotechnological application. Compare to japonica, indica rice has been less responsive to in vitro culture. We used Illumina Hiseq 2000 sequencing platform for comparative transcriptome analysis between two rice subspecies at six different developmental stages combined with a tag-based digital gene expression profiling. Global gene expression among different samples showed greater complexity in japonica rice compared to indica which may be due to polyphyletic origin of two rice subspecies. Expression pattern in initial stage indicate major differences in proembryogenic callus induction phase that may serve as key regulator to observe differences between both subspecies. Our data suggests that phytohormone signaling pathways consist of elaborate networks with frequent crosstalk, thereby allowing plants to regulate somatic embryogenesis pathway. However, this crosstalk varies between the two rice subspecies. Down regulation of positive regulators of meristem development (i.e. KNOX, OsARF5) and up regulation of its counterparts (OsRRs, MYB, GA20ox1/GA3ox2) in japonica may be responsible for its better regeneration and differentiation of somatic embryos. Comprehensive gene expression information in the present experiment may also facilitate to understand the monocot specific meristem regulation for dedifferentiation of somatic cell to embryogenic cells.

  13. Intergenic GWAS SNPs are key components of the spatial and regulatory network for human growth.

    PubMed

    Schierding, William; Antony, Jisha; Cutfield, Wayne S; Horsfield, Julia A; O'Sullivan, Justin M

    2016-08-01

    Meta-analysis of genome-wide association studies has resulted in the identification of hundreds of genetic variants associated with growth and stature. Determining how these genetic variants influence growth is important, but most are non-coding, and there is little understanding of how these variants contribute to adult height. To determine the mechanisms by which human variation contributes to growth, we combined spatial genomic connectivity (high-throughput conformation capture) with functional (gene expression, expression Quantitative Trait Loci) data to determine how non-genic loci associated with infant length, pubertal and adult height and contribute to gene regulatory networks. This approach identified intergenic single-nucleotide polymorphisms (SNPs) ∼85 kb upstream of FBXW11 that spatially connect with distant loci. These regulatory connections are reinforced by evidence of SNP-enhancer effects and altered expression in genes influencing the action of human growth hormone. Functional assays provided evidence for enhancer activity of the intergenic region near FBXW11 that harbors SNP rs12153391, which is associated with an expression Quantitative Trait Loci. Our results suggest that variants in this locus have genome-wide effects as key modifiers of growth (both overgrowth and short stature) acting through a regulatory network. We believe that the genes and pathways connected with this regulatory network are potential targets that could be investigated for diagnostic, prenatal and carrier testing for growth disorders. Finally, the regulatory networks we generated illustrate the power of using existing datasets to interrogate the contribution of intergenic SNPs to common syndromes/diseases.

  14. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

    DOE PAGES

    Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

    2015-05-28

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combinedmore » omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.« less

  15. Experimental approaches for gene regulatory network construction: the chick as a model system

    PubMed Central

    Streit, Andrea; Tambalo, Monica; Chen, Jingchen; Grocott, Timothy; Anwar, Maryam; Sosinsky, Alona; Stern, Claudio D.

    2012-01-01

    Setting up the body plan during embryonic development requires the coordinated action of many signals and transcriptional regulators in a precise temporal sequence and spatial pattern. The last decades have seen an explosion of information describing the molecular control of many developmental processes. The next challenge is to integrate this information into logic ‘wiring diagrams’ that visualise gene actions and outputs, have predictive power and point to key control nodes. Here we provide an experimental workflow on how to construct gene regulatory networks using the chick as model system. Keywords: transcription factors, transcriptome analysis, conserved regulatory elements PMID:23174848

  16. Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

    NASA Astrophysics Data System (ADS)

    Ma'ayan, Avi; Jenkins, Sherry L.; Neves, Susana; Hasseldine, Anthony; Grace, Elizabeth; Dubin-Thaler, Benjamin; Eungdamrong, Narat J.; Weng, Gehzi; Ram, Prahlad T.; Rice, J. Jeremy; Kershenbaum, Aaron; Stolovitzky, Gustavo A.; Blitzer, Robert D.; Iyengar, Ravi

    2005-08-01

    We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.

  17. JCell--a Java-based framework for inferring regulatory networks from time series data.

    PubMed

    Spieth, C; Supper, J; Streichert, F; Speer, N; Zell, A

    2006-08-15

    JCell is a Java-based application for reconstructing gene regulatory networks from experimental data. The framework provides several algorithms to identify genetic and metabolic dependencies based on experimental data conjoint with mathematical models to describe and simulate regulatory systems. Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers. The software is freely available for download at http://www-ra.informatik.uni-tuebingen.de/software/JCell.

  18. Reverse engineering gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum.

    PubMed

    Lin, Kuang; Husmeier, Dirk; Dondelinger, Frank; Mayer, Claus D; Liu, Hui; Prichard, Leighton; Salmond, George P C; Toth, Ian K; Birch, Paul R J

    2010-01-01

    The objective of the project reported in the present chapter was the reverse engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the wild-type and eight knockout strains. To this end, we have applied various recent methods from multivariate statistics and machine learning: graphical Gaussian models, sparse Bayesian regression, LASSO (least absolute shrinkage and selection operator), Bayesian networks, and nested effects models. We have investigated the degree of similarity between the predictions obtained with the different approaches, and we have assessed the consistency of the reconstructed networks in terms of global topological network properties, based on the node degree distribution. The chapter concludes with a biological evaluation of the predicted network structures.

  19. The Transcriptional and Gene Regulatory Network of Lactococcus lactis MG1363 during Growth in Milk

    PubMed Central

    de Jong, Anne; Hansen, Morten E.; Kuipers, Oscar P.; Kilstrup, Mogens; Kok, Jan

    2013-01-01

    In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs) are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks. PMID:23349698

  20. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    PubMed

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.

  1. Statistical identification of gene association by CID in application of constructing ER regulatory network.

    PubMed

    Liu, Li-Yu D; Chen, Chien-Yu; Chen, Mei-Ju M; Tsai, Ming-Shian; Lee, Cho-Han S; Phang, Tzu L; Chang, Li-Yun; Kuo, Wen-Hung; Hwa, Hsiao-Lin; Lien, Huang-Chun; Jung, Shih-Ming; Lin, Yi-Shing; Chang, King-Jen; Hsieh, Fon-Jou

    2009-03-17

    A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examinin