title: Regulatory network analysis acceleration with reconfigurable hardware.
abstract: 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.
authors.affiliation: Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, USA. nam66@pitt.edu
authors.forename: Natasa
authors.initials: N
authors.lastname: Miskov-Zivanov
authors.forename: Andrew
authors.initials: A
authors.lastname: Bresticker
authors.forename: Deepa
authors.initials: D
authors.lastname: Krishnaswamy
authors.forename: Sreesan
authors.initials: S
authors.lastname: Venkatakrishnan
authors.forename: Prashant
authors.initials: P
authors.lastname: Kashinkunti
authors.forename: Diana
authors.initials: D
authors.lastname: Marculescu
authors.forename: James R
authors.initials: JR
authors.lastname: Faeder
mesh_terms: D000465:Algorithms; D002454:Cell Differentiation; D002478:Cells, Cultured; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*; D013601:T-Lymphocytes / Q000166:cytology* / Q000502:physiology*