The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states.

A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.

[1]  C. F. Kossack,et al.  Rank Correlation Methods , 1949 .

[2]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[3]  Bernhard Ø Palsson,et al.  Integrated analysis of metabolic phenotypes in Saccharomyces cerevisiae , 2004, BMC Genomics.

[4]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[5]  Albert-László Barabási,et al.  Life's Complexity Pyramid , 2002, Science.

[6]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[7]  M. Kendall Rank Correlation Methods , 1949 .

[8]  G. Sawers,et al.  Transcriptional activation by FNR and CRP: reciprocity of binding‐site recognition , 1997, Molecular microbiology.

[9]  Chrystala Constantinidou,et al.  Identification of the CRP regulon using in vitro and in vivo transcriptional profiling. , 2004, Nucleic acids research.

[10]  George M Church,et al.  On the complete determination of biological systems. , 2003, Trends in biotechnology.

[11]  Stephen S Fong,et al.  Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes , 2004, Nature Genetics.

[12]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[13]  H. Kitano,et al.  Computational systems biology , 2002, Nature.

[14]  J. Collado-Vides,et al.  Identifying global regulators in transcriptional regulatory networks in bacteria. , 2003, Current opinion in microbiology.

[15]  J E Bailey,et al.  Metabolic capacity of Bacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid. , 1998, Biotechnology and bioengineering.

[16]  S. Busby,et al.  Transcription activation by Escherichia coli FNR protein: similarities to, and differences from, the CRP paradigm. , 1998, Nucleic acids research.

[17]  B. Palsson,et al.  Toward Metabolic Phenomics: Analysis of Genomic Data Using Flux Balances , 1999, Biotechnology progress.

[18]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[19]  H. Bonarius,et al.  Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. , 1997 .

[20]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[21]  Xueqiao Liu,et al.  Probing the ArcA-P Modulon of Escherichia coli by Whole Genome Transcriptional Analysis and Sequence Recognition Profiling* , 2004, Journal of Biological Chemistry.

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

[23]  D. Lauffenburger Cell signaling pathways as control modules: complexity for simplicity? , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[24]  B. Palsson,et al.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data , 2001, Nature Biotechnology.

[25]  G. Church,et al.  Modular epistasis in yeast metabolism , 2005, Nature Genetics.

[26]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[27]  C. Pál,et al.  Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast , 2004, Nature.

[28]  Bernhard O. Palsson,et al.  Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions , 2000, BMC Bioinformatics.

[29]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.

[30]  L. Altucci,et al.  Tumor-selective action of HDAC inhibitors involves TRAIL induction in acute myeloid leukemia cells , 2005, Nature Medicine.

[31]  B. Palsson,et al.  Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. , 2005, Genome research.

[32]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[33]  B. Palsson,et al.  Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. , 2003, Omics : a journal of integrative biology.

[34]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[35]  B. Palsson,et al.  Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth , 2002, Nature.

[36]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.