Regulatory network analysis acceleration with reconfigurable hardware

In medical research it is of great importance to be able to quickly obtain answers to inquiries about system response to different stimuli. Modeling the dynamics of biological regulatory networks is a promising approach to achieve this goal, but existing modeling approaches suffer from complexity issues and become inefficient with large networks. In order to improve the efficiency, we propose the implementation of models of regulatory networks in hardware, which allows for highly parallel simulation of these networks. We find that our FPGA implementation of an example model of peripheral naïve T cell differentiation provides five orders of magnitude speedup when compared to software simulation.