Stochastic theory of large-scale enzyme-reaction networks: Finite copy number corrections to rate equation models

Details Stochastic theory of large-scale enzyme-reaction networks: Finite copy number corrections to rate equation models Stochastic theory of large-scale enzyme-reaction networks: Finite copy number corrections to rate equation models

2011-07-25

arxiv.org/abs/1107.4963v1

J. Chem. Phys. 133, 195101 (2010)

Physics

Condensed Matter

Statistical Mechanics

13 pages, 4 figures; published in The Journal of Chemical Physics

Scientific paper

10.1063/1.3505552

Chemical reactions inside cells occur in compartment volumes in the range of atto- to femtolitres. Physiological concentrations realized in such small volumes imply low copy numbers of interacting molecules with the consequence of considerable fluctuations in the concentrations. In contrast, rate equation models are based on the implicit assumption of infinitely large numbers of interacting molecules, or equivalently, that reactions occur in infinite volumes at constant macroscopic concentrations. In this article we compute the finite-volume corrections (or equivalently the finite copy number corrections) to the solutions of the rate equations for chemical reaction networks composed of arbitrarily large numbers of enzyme-catalyzed reactions which are confined inside a small sub-cellular compartment. This is achieved by applying a mesoscopic version of the quasi-steady state assumption to the exact Fokker-Planck equation associated with the Poisson Representation of the chemical master equation. The procedure yields impressively simple and compact expressions for the finite-volume corrections. We prove that the predictions of the rate equations will always underestimate the actual steady-state substrate concentrations for an enzyme-reaction network confined in a small volume. In particular we show that the finite-volume corrections increase with decreasing sub-cellular volume, decreasing Michaelis-Menten constants and increasing enzyme saturation. The magnitude of the corrections depends sensitively on the topology of the network. The predictions of the theory are shown to be in excellent agreement with stochastic simulations for two types of networks typically associated with protein methylation and metabolism.

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