Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches

A common situation in System Biology is to use several alternative models of a given biochemical system, each with a different structure reflecting different biological hypotheses. These models then have to be ranked according to their ability to reproduce experimental data. In this paper, we use Bayesian model selection to test four alternative models of the yeast GATA-factor genetic network. We employ three different computational methods to calculate the necessary probabilities and evaluate their performance for medium-scale biochemical systems.

[1]  D. Balding,et al.  Approximate Bayesian computation in population genetics. , 2002, Genetics.

[2]  Paul Marjoram,et al.  Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Arnaud Doucet,et al.  An adaptive sequential Monte Carlo method for approximate Bayesian computation , 2011, Statistics and Computing.

[4]  Mark M. Tanaka,et al.  Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.

[5]  Mark A. Girolami,et al.  Bayesian ranking of biochemical system models , 2008, Bioinform..

[6]  Radford M. Neal.  Annealed importance sampling , 1998, Stat. Comput..

[7]  J. Stelling,et al.  Ensemble modeling for analysis of cell signaling dynamics , 2007, Nature Biotechnology.

[8]  S. Chib,et al.  Marginal Likelihood From the Metropolis–Hastings Output , 2001 .

[9]  Edward I. George,et al.  The Practical Implementation of Bayesian Model Selection , 2001 .

[10]  Michael R. Green,et al.  Dissecting the Regulatory Circuitry of a Eukaryotic Genome , 1998, Cell.

[11]  Jean-Michel Marin,et al.  ABC methods for model choice in Gibbs random fields , 2008 .

[12]  E. O’Shea,et al.  Quantification of protein half-lives in the budding yeast proteome , 2006, Proceedings of the National Academy of Sciences.

[13]  E. Dubois,et al.  The Yeast GATA Factor Gat1 Occupies a Central Position in Nitrogen Catabolite Repression-Sensitive Gene Activation , 2009, Molecular and Cellular Biology.

[14]  C. Kaiser,et al.  Nitrogen regulation in Saccharomyces cerevisiae. , 2002, Gene.

[15]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[16]  Michael P. H. Stumpf,et al.  Simulation-based model selection for dynamical systems in systems and population biology , 2009, Bioinform..

[17]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[18]  E. O’Shea,et al.  Global analysis of protein expression in yeast , 2003, Nature.