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Abstract:
Modeling the complex mechanisms by which regulatory networks control the expression of genes in response to external signals, is still a major challenge. One approach to address this complexity relies on Bayesian networks to describe the dependencies between the expression levels of many genes. Here we present the Physical Module Networks — an integrative model that combines protein interactions, binding data and expression data into a unified framework. The model consists of two components: a "Module Network" (Segal et al, Nat Gen 2003), a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing protein-protein interactions and protein-DNA binding events. Both components are stochastic and can be learned from observations. To ensure the model's coherence we rely on a set of rules that define which interactions are permissible given the modules' configuration. To learn a model from data, we use a greedy heuristic that follows local changes in the modules' composition and their choice of regulators. Importantly, each change is examined simultaneously in the module network and in the interaction graph, while the target function (a Bayesian score), is being optimized. In this talk I will present the Physical Module Networks model, and show some results on S. cerevisiae data.