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.
Models of biological networks have been studied through simulations using a number of software tools. However, the intrinsic disparity between the sequential nature of microprocessor architecture used in software-based simulations and the highly parallel nature of biological systems may result in prohibitively long simulation times. In this work, we adopt an alternative approach to simulation of biological systems using hardware-based emulation. Our results on Boolean network models show that such an approach can provide speedup of 17,000X when compared to existing software simulation approaches.
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