Constrained inference of protein interaction networks for invadopodium formation in cancer.

ABSTRACT: Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on

SUBMITTER: Wang H 

PROVIDER: S-EPMC4804358 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature