Abstract
The complex regulatory dynamics of biological networks can be succinctly captured using discrete logic models. Previous work has shown that global optimization schemes are well suited for finding state transition logic and kinetic model parameters, in what are typically large and irregular solution spaces where even relatively small networks represent search spaces exceeding 1040 possible solutions. Powerful computing strategies are required to make this modeling practical for in silico pharmaceutical research. Here, we present a benchmark study quantifying the speedup achieved using a GPU framework in the regulatory logic modeling of two biological networks spanning an order of magnitude increase in complexity and several orders of magnitude in search space size. GPU implementation resulted in a 33%–57% reduction in wall time over multi-thread CPU and a 33%–1866% increase over serial CPU while also delivering better quality solutions in many cases. Evaluation on an even larger pathogen-host immune regulatory network suggests that migration to GPU may support convergence in less than 12 h for established models of immune signaling and less than 6 days for networks describing neural transmission in animals. These results offer a first scalability benchmark for regulatory logic modeling of biological networks, making the latter a potentially attractive approach for practical in silico hypothesis generation.