Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting; Erickson, Keesha E.; Feng, Song; Hlavacek, William Scott

doi:10.1007/s11538-018-0418-2

Title: Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

Journal Article · 27 March 2018 · Bulletin of Mathematical Biology

DOI: https://doi.org/10.1007/s11538-018-0418-2 · OSTI ID:1435537

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1435537

Report Number(s):: LA-UR--18-20656

Journal Information:: Bulletin of Mathematical Biology, Journal Name: Bulletin of Mathematical Biology Journal Issue: 8 Vol. 814; ISSN 0092-8240

Publisher:: Society for Mathematical Biology - SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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