Information propagation within the Genetic Network of Saccharomyces cerevisiae

BackgroundA gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment.ResultsWe find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance.ConclusionsWe suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.

[1]  Ilya Shmulevich,et al.  Eukaryotic cells are dynamically ordered or critical but not chaotic. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  S. Kauffman,et al.  Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms , 2008, PloS one.

[3]  Bartolome Luque,et al.  Measuring Mutual Information in Random Boolean Networks , 1999, Complex Syst..

[4]  Nils Bertschinger,et al.  Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.

[5]  Ayşe Erzan,et al.  Content-based networks: a pedagogical overview. , 2007, Chaos.

[6]  I. Shmulevich,et al.  Basin entropy in Boolean network ensembles. , 2007, Physical review letters.

[7]  Jason Lloyd-Price,et al.  Mutual information in random Boolean models of regulatory networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  A. Arkin,et al.  Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. , 1998, Genetics.

[9]  A. Ribeiro Stochastic and delayed stochastic models of gene expression and regulation. , 2010, Mathematical biosciences.

[10]  L. Hood,et al.  Gene expression dynamics in the macrophage exhibit criticality , 2008, Proceedings of the National Academy of Sciences.

[11]  Stephen A. Cook,et al.  Upper and Lower Time Bounds for Parallel Random Access Machines without Simultaneous Writes , 1986, SIAM J. Comput..

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

[13]  X. Xie,et al.  Probing Gene Expression in Live Cells, One Protein Molecule at a Time , 2006, Science.

[14]  Ilya Shmulevich,et al.  Critical networks exhibit maximal information diversity in structure-dynamics relationships. , 2008, Physical review letters.

[15]  M Villani,et al.  Genetic network models and statistical properties of gene expression data in knock-out experiments. , 2004, Journal of theoretical biology.

[16]  Olli Yli-Harja,et al.  Tuning cell differentiation patterns and single cell dynamics by regulating proteins' functionalities in a toggle switch. , 2009, Journal of theoretical biology.

[17]  Matthias Dehmer,et al.  Information processing in the transcriptional regulatory network of yeast: Functional robustness , 2009, BMC Systems Biology.

[18]  Z. Šidák Rectangular Confidence Regions for the Means of Multivariate Normal Distributions , 1967 .

[19]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[20]  C. Daub,et al.  BMC Systems Biology , 2007 .

[21]  Antti Häkkinen,et al.  Quantifying local structure effects in network dynamics. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[23]  Charles Boone,et al.  Identifying transcription factor functions and targets by phenotypic activation , 2006, Proceedings of the National Academy of Sciences.

[24]  T. Hughes,et al.  Exploration of Essential Gene Functions via Titratable Promoter Alleles , 2004, Cell.

[25]  K. Struhl Fundamentally Different Logic of Gene Regulation in Eukaryotes and Prokaryotes , 1999, Cell.

[26]  R. Morimoto,et al.  Repression of the heat shock factor 1 transcriptional activation domain is modulated by constitutive phosphorylation , 1997, Molecular and cellular biology.

[27]  D. Balcan,et al.  The Information Coded in the Yeast Response Elements Accounts for Most of the Topological Properties of Its Transcriptional Regulation Network , 2007, PloS one.

[28]  O. Yli-Harja,et al.  Perturbation avalanches and criticality in gene regulatory networks. , 2006, Journal of theoretical biology.

[29]  S. Kauffman,et al.  Activities and sensitivities in boolean network models. , 2004, Physical review letters.

[30]  Edoardo M. Airoldi,et al.  Sampling algorithms for pure network topologies: a study on the stability and the separability of metric embeddings , 2005, SKDD.

[31]  T. Hughes,et al.  Mapping pathways and phenotypes by systematic gene overexpression. , 2006, Molecular cell.

[32]  R. Young,et al.  Negative regulation of Gcn4 and Msn2 transcription factors by Srb10 cyclin-dependent kinase. , 2001, Genes & development.

[33]  Andre S Ribeiro,et al.  Studying genetic regulatory networks at the molecular level: delayed reaction stochastic models. , 2007, Journal of theoretical biology.

[34]  Rui Zhu,et al.  A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics , 2006, J. Comput. Biol..