Motivation: C. 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, biologi-cal 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 sub-networks corresponding to six VPCs. Each VPC sub-network contains 20 components. The causal relation-ships 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 compo-nents, and hence can serve as guidance for designing future ex-periments. 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.

(c) Supplementary materials contains the data and other information that was used to automatically learn our model.

Reference:

Xiaoyun Sun and Pengyu Hong, Computational Modeling of C. elegans Vulval Development, Bioinformatics (PubMed)