Legitimacy of the stochastic Michaelis-Menten approximation

• Published in: IET systems biology Vol. 5; no. 1; p. 58
• Main Authors: Sanft, K R, Gillespie, D T, Petzold, L R
• Format: Journal Article
• Language: English
• Published: England 01.01.2011
• Subjects: Algorithms; Enzymes - metabolism; Kinetics; Models, Chemical; Models, Theoretical; Stochastic Processes
• ISSN: 1751-8849
• DOI: 10.1049/iet-syb.2009.0057

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.