A compiler for biological networks on silicon chips
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file (3).pdf
Description
Published version
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3.07 MB
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a16a73866b94d9c10a293cb3be19d137
Author(s)
Medley, J Kyle
Teo, Jonathan
Woo, Sung Sik
Hellerstein, Joseph
Sarpeshkar, Rahul
Sauro, Herbert M
Date Issued
September 23, 2020
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Medley JK, Teo J, Woo SS, Hellerstein J, Sarpeshkar R, et al. (2020) A compiler for biological networks on silicon chips. PLOS Computational Biology 16(9): e1008063.
Version
Final published version
Abstract
The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit–level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass–action kinetics, classic enzyme kinetics (Michaelis–Menten, Briggs–Haldane, and Botts–Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule–based models are particularly well–suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software.
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1008063
