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An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
Open accessAbstract
Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.
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Metadata
- Authors
- Gupta, A., & Mendes, P.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Mendes, Pedro
- Venue
- Computation
- Publisher
- MDPI AG
- Published
- 2018-01-31
- Volume
- 6
- Issue
- 1
- Pages
- 9
- ISSN
- 2079-3197 (ISSN-L)
- PMC
- PMC6013266
- OpenAlex
- W2792385328
- Cited by
- 17
- References
- 59
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 4 · 2019: 2 · 2018: 1
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computation (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- PubMed Central (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (GM080219)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- stochastic simulation, biochemical reaction networks, chemical master equation, Gillespie algorithm, network simulation, reaction rules, rule transformation, simulation efficiency
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