title: Automatic inference of multicellular regulatory networks using informative priors.
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.
authors.affiliation: Department of Computer Science, Brandeis University, Waltham, MA 02454, USA. xsun@brandeis.edu
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Sun
authors.forename: Pengyu
authors.initials: P
authors.lastname: Hong
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D017173:Caenorhabditis elegans / Q000196:embryology*; D002454:Cell Differentiation; D019295:Computational Biology / Q000379:methods*; D008954:Models, Biological