Eliminating fast reactions in stochastic simulations of biochemical networks: a bistable genetic switch

Abstract

In many stochastic simulations of biochemical reaction networks, it is desirable to ``coarse-grain'' the reaction set, removing fast reactions while retaining the correct system dynamics. Various coarse-graining methods have been proposed, but it remains unclear which methods are reliable and which reactions can safely be eliminated. We address these issues for a model gene regulatory network that is particularly sensitive to dynamical fluctuations: a bistable genetic switch. We remove protein-DNA and/or protein-protein association-dissociation reactions from the reaction set, using various coarse-graining strategies. We determine the effects on the steady-state probability distribution function and on the rate of fluctuation-driven switch flipping transitions. We find that protein-protein interactions may be safely eliminated from the reaction set, but protein-DNA interactions may not. We also find that it is important to use the chemical master equation rather than macroscopic rate equations to compute effective propensity functions for the coarse-grained reactions.

46 pages, 5 figures

References in corpus (4)

• Forward Flux Sampling-type schemes for simulating rare events: Efficiency analysis
• Genetic Toggle Switch Without Cooperative Binding
• Computing stationary distributions in equilibrium and non-equilibrium systems with Forward Flux Sampling
• Testing the transition state theory in stochastic dynamics of a genetic switch

Cited by in corpus (3)

• Studying Rare Events using Forward-Flux Sampling: Recent Breakthroughs and Future Outlook
• Exponential sensitivity of noise-driven switching in genetic networks
• Quantifying Stochastic Effects in Biochemical Reaction Networks using Partitioned Leaping