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Bioinformatics 2007 23(13):i499-i507; doi:10.1093/bioinformatics/btm214
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational modeling of Caenorhabditis elegans vulval induction

Xiaoyun Sun and Pengyu Hong *

Department of Computer Science, National Center of Behavioral Genomics, 415 South Street, Waltham, MA 02454, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Caenorhabditis elegans vulval development is a paradigmatic example of animal organogenesis with extensive experimental data. During vulval induction, each of the six multipotent vulval precursor cells (VPCs) commits to one of three fates (1°, 2°, 3°). The precise 1°-2°-3° formation of VPC fates is controlled by a network of intercellular signaling, intracellular signal transduction and transcriptional regulation. The construction of mathematical models for this network will enable hypothesis generation, biological mechanism discovery and system behavior analysis.

Results: We have developed a mathematical model based on dynamic Bayesian networks to model the biological network that governs the VPC 1°-2°-3° pattern formation process. Our model has six interconnected subnetworks corresponding to six VPCs. Each VPC subnetwork contains 20 components. The causal relationships among network components are quantitatively encoded in the structure and parameters of the model. Statistical machine learning techniques were developed to automatically learn both the structure and parameters of the model from data collected from literatures. The learned model is capable of simulating vulval induction under 36 different genetic conditions. Our model also contains a few hypothetical causal relationships between network components, and hence can serve as guidance for designing future experiments. The statistical learning nature of our methodology makes it easy to not only handle noise in data but also automatically incorporate new experimental data to refine the model.

Contact: hong{at}cs.brandeis.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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