Suppr超能文献

使用分解法求解米氏方程

Solution of the Michaelis-Menten equation using the decomposition method.

作者信息

Sonnad Jagadeesh R, Goudar Chetan T

机构信息

Department of Radiological Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73190, United States.

出版信息

Math Biosci Eng. 2009 Jan;6(1):173-88. doi: 10.3934/mbe.2009.6.173.

Abstract

We present a low-order recursive solution to the Michaelis-Menten equation using the decomposition method. This solution is algebraic in nature and provides a simpler alternative to numerical approaches such as differential equation evaluation and root-solving techniques that are currently used to compute substrate concentration in the Michaelis-Menten equation. A detailed characterization of the errors in substrate concentrations computed from decomposition, Runge-Kutta, and bisection methods over a wide range of s(0) : K(m) values was made by comparing them with highly accurate solutions obtained using the Lambert W function. Our results indicated that solutions obtained from the decomposition method were usually more accurate than those from the corresponding classical Runge-Kutta methods. Moreover, these solutions required significantly fewer computations than the root-solving method. Specifically, when the stepsize was 0.1% of the total time interval, the computed substrate concentrations using the decomposition method were characterized by accuracies on the order of 10(-8) or better. The algebraic nature of the decomposition solution and its relatively high accuracy make this approach an attractive candidate for computing substrate concentration in the Michaelis-Menten equation.

摘要

我们使用分解方法给出了米氏方程的低阶递归解。该解本质上是代数形式的,为目前用于计算米氏方程中底物浓度的数值方法(如微分方程求值和求根技术)提供了一种更简单的替代方法。通过将分解法、龙格 - 库塔法和二分法在广泛的(s(0):K(m))值范围内计算得到的底物浓度误差与使用兰伯特W函数获得的高精度解进行比较,对这些误差进行了详细表征。我们的结果表明,分解法得到的解通常比相应的经典龙格 - 库塔法得到的解更准确。此外,这些解所需的计算量比求根法少得多。具体而言,当步长为总时间间隔的0.1%时,使用分解法计算得到的底物浓度的精度约为(10^{-8})或更高。分解解的代数性质及其相对较高的精度使得该方法成为计算米氏方程中底物浓度的一个有吸引力的候选方法。

相似文献

1
Solution of the Michaelis-Menten equation using the decomposition method.
Math Biosci Eng. 2009 Jan;6(1):173-88. doi: 10.3934/mbe.2009.6.173.
3
Parameter estimation using a direct solution of the integrated Michaelis-Menten equation.
Biochim Biophys Acta. 1999 Jan 11;1429(2):377-83. doi: 10.1016/s0167-4838(98)00247-7.
5
Explicit analytic approximations for time-dependent solutions of the generalized integrated Michaelis-Menten equation.
Anal Biochem. 2011 Apr 15;411(2):303-5. doi: 10.1016/j.ab.2011.01.016. Epub 2011 Jan 15.
6
Explicit reformulations of time-dependent solution for a Michaelis-Menten enzyme reaction model.
Anal Biochem. 2010 Nov 1;406(1):94-6. doi: 10.1016/j.ab.2010.06.041. Epub 2010 Jul 1.
8
Relationship between enzyme concentration and Michaelis constant in enzyme assays.
Biochimie. 2020 Sep;176:12-20. doi: 10.1016/j.biochi.2020.06.002. Epub 2020 Jun 23.
9
Two-level time-domain decomposition based distributed method for numerical solutions of pharmacokinetic models.
Comput Biol Med. 2011 Apr;41(4):221-7. doi: 10.1016/j.compbiomed.2011.02.003.
10

引用本文的文献

1
Closed form solutions and dominant elimination pathways of simultaneous first-order and Michaelis-Menten kinetics.
J Pharmacokinet Pharmacodyn. 2015 Apr;42(2):151-61. doi: 10.1007/s10928-015-9407-3. Epub 2015 Feb 13.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验