Parallel Algorithms for Bayesian Networks Structure Learning with Applications to Systems Biology

Bayesian networks (BN) are probabilistic graphical models which are widely utilized in modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and existing exact and heuristic solutions do not scale to large enough domains to allow for meaningful modeling of many biological processes. In this work, we present efficient parallel algorithms which push the scale of both exact and heuristic BN structure learning. We demonstrate the applicability of our methods by implementations on an IBM Blue Gene/L and an AMD Opteron cluster, and discuss their significance for future applications to systems biology.