Fast reactions with non-interacting species in stochastic reaction networks
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Fast reactions with non-interacting species in stochastic reaction networks. / Hoessly, Linard; Wiuf, Carsten.
I: Mathematical Biosciences and Engineering, Bind 19, Nr. 3, 2022, s. 2720-2749.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Fast reactions with non-interacting species in stochastic reaction networks
AU - Hoessly, Linard
AU - Wiuf, Carsten
N1 - Funding Information: LH acknowledges funding from the Swiss National Science Foundations Early Postdoc.Mobility grant (P2FRP2 188023). The work presented in this article is supported by Novo Nordisk Foundation, grant NNF19OC0058354. Publisher Copyright: © 2022 the Author(s), licensee AIMS Press.
PY - 2022
Y1 - 2022
N2 - We consider stochastic reaction networks modeled by continuous-time Markov chains. Such reaction networks often contain many reactions, potentially occurring at different time scales, and have unknown parameters (kinetic rates, total amounts). This makes their analysis complex. We examine stochastic reaction networks with non-interacting species that often appear in examples of interest (e.g. in the two-substrate Michaelis Menten mechanism). Non-interacting species typically appear as intermediate (or transient) chemical complexes that are depleted at a fast rate. We embed the Markov process of the reaction network into a one-parameter family under a two time-scale approach, such that molecules of non-interacting species are degraded fast. We derive simplified reaction networks where the non-interacting species are eliminated and that approximate the scaled Markov process in the limit as the parameter becomes small. Then, we derive sufficient conditions for such reductions based on the reaction network structure for both homogeneous and time-varying stochastic settings, and study examples and properties of the reduction.
AB - We consider stochastic reaction networks modeled by continuous-time Markov chains. Such reaction networks often contain many reactions, potentially occurring at different time scales, and have unknown parameters (kinetic rates, total amounts). This makes their analysis complex. We examine stochastic reaction networks with non-interacting species that often appear in examples of interest (e.g. in the two-substrate Michaelis Menten mechanism). Non-interacting species typically appear as intermediate (or transient) chemical complexes that are depleted at a fast rate. We embed the Markov process of the reaction network into a one-parameter family under a two time-scale approach, such that molecules of non-interacting species are degraded fast. We derive simplified reaction networks where the non-interacting species are eliminated and that approximate the scaled Markov process in the limit as the parameter becomes small. Then, we derive sufficient conditions for such reductions based on the reaction network structure for both homogeneous and time-varying stochastic settings, and study examples and properties of the reduction.
KW - Continuous-time Markov process
KW - Markov process
KW - Mass-action system
KW - Reduction
KW - Singular perturbation
KW - Stochastic reaction networks
UR - http://www.scopus.com/inward/record.url?scp=85123455370&partnerID=8YFLogxK
U2 - 10.3934/MBE.2022124
DO - 10.3934/MBE.2022124
M3 - Journal article
AN - SCOPUS:85123455370
VL - 19
SP - 2720
EP - 2749
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
SN - 1547-1063
IS - 3
ER -
ID: 291433667