Michaelis-Menten kinetics are commonly used to represent enzyme-catalysed reactions in biochemical models. The Michaelis-Menten approximation has been thoroughly studied in the context of traditional differential equa...
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Michaelis-Menten kinetics are commonly used to represent enzyme-catalysed reactions in biochemical models. The Michaelis-Menten approximation has been thoroughly studied in the context of traditional differential equation models. The presence of small concentrations in biochemical systems, however, encourages the conversion to a discrete stochastic representation. It is shown that the Michaelis-Menten approximation is applicable in discrete stochastic models and that the validity conditions are the same as in the deterministic regime. The authors then compare the Michaelis-Menten approximation to a procedure called the slow-scale stochastic simulation algorithm (ssSSA). The theory underlying the ssSSA implies a formula that seems in some cases to be different from the well-known Michaelis-Menten formula. Here those differences are examined, and some special cases of the stochastic formulas are confirmed using a first-passage time analysis. This exercise serves to place the conventional Michaelis-Menten formula in a broader rigorous theoretical framework.
This chapter reviews the theory of stochastic chemical kinetics and several simulation methods that are based on that theory. An effort is made to delineate the logical connections among the major elements of the theo...
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ISBN:
(纸本)9783540688921
This chapter reviews the theory of stochastic chemical kinetics and several simulation methods that are based on that theory. An effort is made to delineate the logical connections among the major elements of the theory, such as the chemical master equation, the stochasticsimulationalgorithm, tau-leaping, the chemical Langevin equation, the chemical Fokker-Planck equation, and the deterministic reaction rate equation. Focused presentations are given of two approximate simulation strategies that aim to improve simulation efficiency for systems with "multiscale" complications of the kind that are often encountered in cellular systems: The first, explicit tau-leaping, deals with systems that have a wide range of molecular populations. The second, the slow-scale stochastic simulation algorithm, is designed for systems that have a wide range of reaction rates. The latter procedure is shown to provide a stochastic generalization of the Michaelis-Menten analysis of the enzyme-substrate reaction set.
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