Mathematical Reviews, American Mathematical Society, 416 4th Street, Ann Arbor, MI, 48103, USA.
Department of Biological Sciences and Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.
Bull Math Biol. 2024 May 4;86(6):68. doi: 10.1007/s11538-024-01295-z.
We demonstrate that the Michaelis-Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke's comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is , where is the Van Slyke-Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.
我们证明了当初始底物浓度较低时,米氏反应机制可以通过线性系统进行精确逼近。这导致了准一级动力学,简化了数学计算和实验分析。我们的证明利用了系统的单调性和 Kamke 的比较定理。这种线性近似给出了封闭形式的解,即使没有时间尺度分离,也能够准确地建模和估计反应速率常数。在先前工作的基础上,我们确定了这个近似的有效性的充分条件是 ,其中 是 Van Slyke-Cullen 常数。这个条件与初始酶浓度无关。此外,我们研究了线性系统中的时间尺度分离,确定了必要和充分的条件,并推导出了相应的简化一维方程。