Title: Automatic inference of multicellular regulatory networks using informative priors

Authors: Xiaoyun Sun, Pengyu Hong

Addresses: Department of Computer Science, Brandeis University, Waltham, MA 02454, USA. ' Department of Computer Science, National Center for Behavioral Genomics, Brandeis University, Waltham, MA 02454, USA

Abstract: To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

Keywords: multicellular regulatory networks; DBN; dynamic Bayesian networks; animal development; mathematical modelling; cell differentiation; machine learning; computational biology.

DOI: 10.1504/IJCBDD.2009.028820

International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.2, pp.115 - 133

Available online: 03 Oct 2009

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