title: Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection.
abstract: Understanding the structure and function of complex gene regulatory networks using classical genetic assays is an error-prone procedure that frequently generates ambiguous outcomes. Even some of the best-characterized gene networks contain interactions whose validity is not conclusively proven. Founded on dynamic experimental data, mechanistic mathematical models are able to offer detailed insights that would otherwise require prohibitively large numbers of genetic experiments. Here we attempt mechanistic modeling of the transcriptional network formed by the four GATA-factor proteins, a well-studied system of central importance for nitrogen-source regulation of transcription in the yeast Saccharomyces cerevisiae. To resolve ambiguities in the network organization, we encoded a set of five interactions hypothesized in the literature into a set of 32 mathematical models, and employed Bayesian model selection to identify the most plausible set of interactions based on dynamic gene expression data. The top-ranking model was validated on newly generated GFP reporter dynamic data and was subsequently used to gain a better understanding of how yeast cells organize their transcriptional response to dynamic changes of nitrogen sources. Our work constitutes a necessary and important step towards obtaining a holistic view of the yeast nitrogen regulation mechanisms; on the computational side, it provides a demonstration of how powerful Monte Carlo techniques can be creatively combined and used to address the great challenges of large-scale dynamical system inference.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
authors.forename: Andreas
authors.initials: A
authors.lastname: Milias-Argeitis
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
authors.forename: Ana Paula
authors.initials: AP
authors.lastname: Oliveira
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
authors.forename: Luca
authors.initials: L
authors.lastname: Gerosa
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
authors.forename: Laura
authors.initials: L
authors.lastname: Falter
authors.affiliation: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
authors.forename: Uwe
authors.initials: U
authors.lastname: Sauer
authors.affiliation: Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
authors.forename: John
authors.initials: J
authors.lastname: Lygeros
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D050980:GATA Transcription Factors / Q000235:genetics / Q000378:metabolism*; D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D009584:Nitrogen / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology
chemical_list: D050980:GATA Transcription Factors; D029701:Saccharomyces cerevisiae Proteins; D009584:Nitrogen