FPGA Implementation of Reaction Systems

Reaction system (RS) belongs to a type of qualitative computing model inspired by biochemical reactions taking place inside biological cells. It concerns more the interactions and causality among reactions rather than concrete concentrations of chemical entities. Many biochemical processes and models can be represented in the form of reaction systems so that complex relations and ultimate products of a variety of reactions can be revealed qualitatively. The reaction system works in parallel mode. Software simulation of this kind of model may suffer from the penalty of inefficient parallelism for the limited performance of CPU/GPU, especially for the simulation of large-scale models. Considering potential applications of reaction systems in disease diagnoses and in drug developments, hardware implementation of reaction systems provides a better way to accelerate computations involved. In this paper, an FPGA implementation method of a reaction system called RSFIM is proposed. Two small-scale models, i.e., the reaction system of intermediate filaments self-assembly and heat shock response, are implemented on FPGA, achieving a computing speed of 2×108 steps per second. For large-scale models, the ErbB reaction system is implemented, obtaining a speedup of 7.649×104 compared with its highest performance GPU simulation so far. The reaction system binary counter, which is a quantitative model, is also implemented by the Boolean explanation of the qualitative character of the reaction system. FPGA implementation of reaction systems opens a novel research line to speed up the simulations of reaction systems and other biological models in the perspective of parallel digital circuits.

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Publication Details

Journal
Electronics
Published
2024-12-13
DOI
https://doi.org/10.3390/electronics13244929
Citations
1
Primary Topic
DNA and Biological Computing
Type
article
Field-Weighted Citation Impact
0.23

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article

FPGA Implementation of Reaction Systems

Jing Lu, Zeyi Shang, Sergey Verlan, Zhe Wei et al.
1 citations
Electronics
DNA and Biological Computing
0.23
article

FPGA Implementation of Reaction Systems

Jing Lu, Zeyi Shang, Sergey Verlan, Zhe Wei, Min Zhou
article en
1 citations

Abstract

Reaction system (RS) belongs to a type of qualitative computing model inspired by biochemical reactions taking place inside biological cells. It concerns more the interactions and causality among reactions rather than concrete concentrations of chemical entities. Many biochemical processes and models can be represented in the form of reaction systems so that complex relations and ultimate products of a variety of reactions can be revealed qualitatively. The reaction system works in parallel mode. Software simulation of this kind of model may suffer from the penalty of inefficient parallelism for the limited performance of CPU/GPU, especially for the simulation of large-scale models. Considering potential applications of reaction systems in disease diagnoses and in drug developments, hardware implementation of reaction systems provides a better way to accelerate computations involved. In this paper, an FPGA implementation method of a reaction system called RSFIM is proposed. Two small-scale models, i.e., the reaction system of intermediate filaments self-assembly and heat shock response, are implemented on FPGA, achieving a computing speed of 2×108 steps per second. For large-scale models, the ErbB reaction system is implemented, obtaining a speedup of 7.649×104 compared with its highest performance GPU simulation so far. The reaction system binary counter, which is a quantitative model, is also implemented by the Boolean explanation of the qualitative character of the reaction system. FPGA implementation of reaction systems opens a novel research line to speed up the simulations of reaction systems and other biological models in the perspective of parallel digital circuits.

ElectronicsVol. 13(24)
Université Paris-Est Créteil (FR), Civil Aviation Flight University of China (CN)
Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 40%
DNA and Biological Computing
0.23
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