Designing Logical Rules to Model the Response of Biomolecular Networks with Complex Interactions: An Application to Cancer Modeling.
Résumé
We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with post-translational interactions we consider two types of nodes in the network, corresponding to proteins and to RNA. Micro-array data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and non-significant model predictions, mainly because rules used to propagate information -- usually disjunctive constraints -- are weak. Here we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between micro-array and interaction data. We use these rules to identify which nodes are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in the Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page.