Automatic inference of multicellular regulatory networks using informative priors. (5th October 2009)

• Record Type: Journal Article
• Authors: Sun, Xiaoyun; Hong, Pengyu
• Is Part Of: International journal of computational biology and drug design. Volume 2:Number 2(2009)
• Page Start: 115
• Page End: 133
• Publication Date: 2009-10-05
• Subjects: multicellular regulatory networks -- DBN -- dynamic Bayesian networks -- animal development -- mathematical modelling -- cell differentiation -- machine learning -- computational biology; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

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.