article
Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space
published
yes
Jakob
Ruess
author 4A245D00-F248-11E8-B48F-1D18A9856A870000-0003-1615-3282
ToHe
department
GaTk
department
Quantitative Reactive Modeling
project
Rigorous Systems Engineering
project
The Wittgenstein Prize
project
International IST Postdoc Fellowship Programme
project
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
https://research-explorer.app.ist.ac.at/download/1539/4641/IST-2016-593-v1+1_Minimal_moment_equations.pdf
application/pdfno
American Institute of Physics2015
eng
Journal of Chemical Physics10.1063/1.4937937
14324
J. Ruess, Journal of Chemical Physics 143 (2015).
Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. American Institute of Physics. <a href="https://doi.org/10.1063/1.4937937">https://doi.org/10.1063/1.4937937</a>
J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” <i>Journal of Chemical Physics</i>, vol. 143, no. 24. American Institute of Physics, 2015.
Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103.
Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. 2015;143(24). doi:<a href="https://doi.org/10.1063/1.4937937">10.1063/1.4937937</a>
Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>. American Institute of Physics, 2015. <a href="https://doi.org/10.1063/1.4937937">https://doi.org/10.1063/1.4937937</a>.
Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:<a href="https://doi.org/10.1063/1.4937937">10.1063/1.4937937</a>.
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