GPU-accelerated Modeling of Biological Regulatory Networks

TL;DR

This paper demonstrates how GPU computing significantly accelerates the process of identifying biological regulatory models, enabling more practical in silico research.

Contribution

Implementing global optimization algorithms on GPUs greatly improves efficiency in modeling complex biological regulatory networks.

Findings

GPU implementation yields 33-43% speedup over multi-thread CPU.

GPU implementation yields 33-1866% speedup over serial CPU.

Enhanced performance makes in silico hypothesis testing more feasible.

Abstract

The complex regulatory dynamics of a biological network can be succinctly captured using discrete logic models. Given even sparse time-course data from the system of interest, previous work has shown that global optimization schemes are suitable for proposing logic models that explain the data and make predictions about how the system will behave under varying conditions. Considering the large scale of the parameter search spaces associated with these regulatory systems, performance optimizations on the level of both hardware and software are necessary for making this a practical tool for in silico pharmaceutical research. We show here how the implementation of these global optimization algorithms in a GPU-computing environment can accelerate the solution of these parameter search problems considerably. We carry out parameter searches on two model biological regulatory systems that represent almost an order of magnitude scale-up in complexity, and we find the gains in efficiency from GPU to be a 33%-43% improvement compared to multi-thread CPU implementations and a 33%-1866% increase compared to CPU in serial. These improvements make global optimization of logic model identification a far more attractive and feasible method for in silico hypothesis generation and design of experiments.