Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors.

Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes nD needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8% for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles can be associated with potential unstable steady-states where even small changes in binding affinities can cause dramatic rearrangements of the state of the network.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  P. Erdos,et al.  On the strength of connectedness of a random graph , 1964 .

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[5]  B. Palsson,et al.  Transcriptional regulation in constraints-based metabolic models of Escherichia coli Covert , 2002 .

[6]  Markus J. Herrgård,et al.  Integrating high-throughput and computational data elucidates bacterial networks , 2004, Nature.

[7]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[8]  J. Pronk,et al.  Role of Transcriptional Regulation in Controlling Fluxes in Central Carbon Metabolism of Saccharomyces cerevisiae , 2004, Journal of Biological Chemistry.

[9]  A. Zeng,et al.  An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. , 2004, Nucleic acids research.

[10]  Quaid Morris,et al.  Transcriptional networks: reverse-engineering gene regulation on a global scale. , 2004, Current opinion in microbiology.

[11]  Arun K. Ramani,et al.  Protein interaction networks from yeast to human. , 2004, Current opinion in structural biology.

[12]  J. Pronk,et al.  Two-dimensional Transcriptome Analysis in Chemostat Cultures , 2005, Journal of Biological Chemistry.

[13]  J. Pronk,et al.  Physiological and genome-wide transcriptional responses of Saccharomyces cerevisiae to high carbon dioxide concentrations. , 2005, FEMS yeast research.

[14]  Ole Winther,et al.  Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae , 2006, Genome Biology.

[15]  Pooja Jain,et al.  The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae , 2005, Nucleic Acids Res..

[16]  Jean-Marc Daran,et al.  Exploiting combinatorial cultivation conditions to infer transcriptional regulation , 2007, BMC Genomics.

[17]  M. Gerstein,et al.  Genomic analysis of the hierarchical structure of regulatory networks , 2006, Proceedings of the National Academy of Sciences.

[18]  Markus J. Herrgård,et al.  Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. , 2006, Genome research.

[19]  Jean-Marc Daran,et al.  Transcriptional responses of Saccharomyces cerevisiae to preferred and nonpreferred nitrogen sources in glucose-limited chemostat cultures. , 2007, FEMS yeast research.

[20]  Marcel J T Reinders,et al.  Generic and specific transcriptional responses to different weak organic acids in anaerobic chemostat cultures of Saccharomyces cerevisiae. , 2007, FEMS yeast research.

[21]  J. Pronk,et al.  Physiological and Transcriptional Responses of Saccharomyces cerevisiae to Zinc Limitation in Chemostat Cultures † , 2007 .

[22]  Barbara M. Bakker,et al.  The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels , 2007, Proceedings of the National Academy of Sciences.

[23]  Marcel JT Reinders,et al.  Combinatorial effects of environmental parameters on transcriptional regulation in Saccharomyces cerevisiae: A quantitative analysis of a compendium of chemostat-based transcriptome data , 2009, BMC Genomics.

[24]  J. Pronk,et al.  Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: A three factor design , 2008, BMC Genomics.

[25]  Pascale Daran-Lapujade,et al.  Revisiting the role of yeast Sfp1 in ribosome biogenesis and cell size control: a chemostat study. , 2008, Microbiology.

[26]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[27]  N. Price,et al.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis , 2010, Proceedings of the National Academy of Sciences.

[28]  Avi Ma'ayan,et al.  ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments , 2010, Bioinform..

[29]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[30]  Julio Collado-Vides,et al.  RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units) , 2010, Nucleic Acids Res..

[31]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[32]  I. Nookaew,et al.  Fifteen years of large scale metabolic modeling of yeast: developments and impacts. , 2012, Biotechnology advances.

[33]  W. Marsden.  I and J , 2012 .

[34]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[35]  Graziano Pesole,et al.  Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets , 2012, Nucleic Acids Res..

[36]  Timothy R. Hughes,et al.  Mapping Yeast Transcriptional Networks , 2013, Genetics.

[37]  Intawat Nookaew,et al.  The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum , 2013, PLoS Comput. Biol..

[38]  J. Nielsen,et al.  Applications of computational modeling in metabolic engineering of yeast. , 2014, FEMS yeast research.

[39]  I. Borodina,et al.  Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae. , 2014, FEMS yeast research.

[40]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.