Rigorous elimination of fast stochastic variables from the linear noise approximation using projection operators
arXiv:1209.5236 · doi:10.1103/PhysRevE.86.041110
Abstract
The linear noise approximation (LNA) offers a simple means by which one can study intrinsic noise in monostable biochemical networks. Using simple physical arguments, we have recently introduced the slow-scale LNA (ssLNA) which is a reduced version of the LNA under conditions of timescale separation. In this paper, we present the first rigorous derivation of the ssLNA using the projection operator technique and show that the ssLNA follows uniquely from the standard LNA under the same conditions of timescale separation as those required for the deterministic quasi-steady state approximation. We also show that the large molecule number limit of several common stochastic model reduction techniques under timescale separation conditions constitutes a special case of the ssLNA.
10 pages, 1 figure, submitted to Physical Review E; see also BMC Systems Biology 6, 39 (2012)
References in corpus (2)
Cited by in corpus (17)
- Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
- Thermodynamically Consistent Coarse Graining of Biocatalysts beyond Michaelis--Menten
- Metastable behavior in Markov processes with internal states
- Stochastic dynamics on slow manifolds
- Model reduction for stochastic chemical systems with abundant species
- Model reduction methods for classical stochastic systems with fast-switching environments: reduced master equations, stochastic differential equations, and applications
- Marginal process framework: A model reduction tool for Markov jump processes
- Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours
- Statistical physics approaches to subnetwork dynamics in biochemical systems
- The effects of intrinsic noise on the behaviour of bistable cell regulatory systems under quasi-steady state conditions
- Context in Synthetic Biology: Memory Effects of Environments with Mono-molecular Reactions
- Learning Moment Closure in Reaction-Diffusion Systems with Spatial Dynamic Boltzmann Distributions
- Stochastic enzyme kinetics and the quasi-steady-state reductions: Application of the slow scale linear noise approximation à la Fenichel
- Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks
- Elimination of fast variables in stochastic nonlinear kinetics
- Model order reduction for Linear Noise Approximation using time-scale separation (Extended Version)
- On the validity of the stochastic quasi-steady-state approximation in open enzyme catalyzed reactions: Timescale separation or singular perturbation?