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.
International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.2, pp.115 - 133
Available online: 03 Oct 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article