Author(s):

Ruess, Jakob

Article Title: 
Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space

Affiliation 
IST Austria 
Abstract: 
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 loworder 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.

Keywords: 
Gene Expression; conservation; Finite state spaces; Infinite numbers; Symbolic algorithms; Probability distributions; Moment equations; Stochastic systems; Chemical analysis; Conditional moments; Infinite state space; Loworder moments; Models of biochemical reactions; Stochastic models

Journal Title:

Journal of Chemical Physics

Volume: 
143

Issue 
24

ISSN:

10897690

Publisher:

American Institute of Physics

Date Published:

20151228

Start Page: 
Article number: 244103

URL: 

DOI: 
10.1063/1.4937937

Notes: 
This work has received funding from the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS, http://www.avacs.org/), by the European Research Council (ERC) under Grant No. 267989 (QUAREM), by the Austrian Science Fund (FWF) under Grant Nos. S11402N23 (RiSE) and Z211N23 (Wittgenstein Award) and by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/20072013) under REA Grant Agreement No. 291734.

Open access: 
yes (repository) 