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

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1435537
Report Number(s):
LA-UR-18-20656
Journal ID: ISSN 0092-8240
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Bulletin of Mathematical Biology
Additional Journal Information:
Journal Volume: 814; Journal Issue: 8; Journal ID: ISSN 0092-8240
Publisher:
Society for Mathematical Biology - Springer
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science; Stochastic simulation; Rule-based modeling; Combinatorial complexity; Kinetic Monte Carlo

Citation Formats

Suderman, Ryan T., Mitra, Eshan David, Lin, Yen Ting, Erickson, Keesha E., Feng, Song, and Hlavacek, William Scott. Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations. United States: N. p., 2018. Web. doi:10.1007/s11538-018-0418-2.
Suderman, Ryan T., Mitra, Eshan David, Lin, Yen Ting, Erickson, Keesha E., Feng, Song, & Hlavacek, William Scott. Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations. United States. doi:10.1007/s11538-018-0418-2.
Suderman, Ryan T., Mitra, Eshan David, Lin, Yen Ting, Erickson, Keesha E., Feng, Song, and Hlavacek, William Scott. Wed . "Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations". United States. doi:10.1007/s11538-018-0418-2. https://www.osti.gov/servlets/purl/1435537.
@article{osti_1435537,
title = {Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations},
author = {Suderman, Ryan T. and Mitra, Eshan David and Lin, Yen Ting and Erickson, Keesha E. and Feng, Song and Hlavacek, William Scott},
abstractNote = {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.},
doi = {10.1007/s11538-018-0418-2},
journal = {Bulletin of Mathematical Biology},
number = 8,
volume = 814,
place = {United States},
year = {2018},
month = {3}
}

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