Abstract

Abstract Motivation: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the object of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks apply to time series microarray data. Results: We extend the framework of dynamic Bayesian networks in order to handle perturbations. A new discretization method, specialized for datasets from time series perturbations experiments, is also introduced. We compare networks inferred from realistic simulations data by our method and by dynamic Bayesian networks learning techniques. We conclude that application of our method substantially improves inferring. 1 Introduction As most genetic regulatory systems involve many components connected through complex networks of interactions, formal methods and computer tools for modeling and simulating are needed. Therefore, various formalisms were proposed to describe genetic regulatory systems, including Boolean networks and their generalizations, ordinary and partial differential equations, stochastic equations and Bayesian networks (see [4] for a review). While differential and stochastic equations describe the biophysical processes at a very refined level of detail and prove useful in simulations of well studied systems, Bayesian networks appear attractive in the field of inferring the regulatory network structure from gene expression data. The reason is that their learning techniques have solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way.

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