Department of Inorganic and Analytical Chemistry, University of Debrecen, Debrecen, Hungary.
J Chem Phys. 2012 Feb 7;136(5):054111. doi: 10.1063/1.3681942.
The Michaelis-Menten mechanism is an extremely important tool for understanding enzyme-catalyzed transformation of substrates into final products. In this work, a computationally viable, full stochastic description of the Michaelis-Menten kinetic scheme is introduced based on a stochastic equivalent of the steady-state assumption. The full solution derived is free of restrictions on amounts of substance or parameter values and is used to create stochastic maps of the Michaelis-Menten mechanism, which show the regions in the parameter space of the scheme where the use of the stochastic kinetic approach is inevitable. The stochastic aspects of recently published examples of single-enzyme kinetic studies are analyzed using these maps.
米氏动力学机制是理解酶促底物转化为终产物的极其重要的工具。在这项工作中，基于稳态假设的随机等效方法，引入了一种可行的、完整的随机描述米氏动力学方案的方法。所得到的完整解不受物质数量或参数值的限制，并用于创建米氏动力学机制的随机图谱，该图谱显示了方案参数空间中必须使用随机动力学方法的区域。使用这些图谱分析了最近发表的单酶动力学研究示例的随机方面。
