The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.

IF 2.2 4区 数学 Q2 BIOLOGY Bulletin of Mathematical Biology Pub Date : 2024-05-04 DOI:10.1007/s11538-024-01295-z
Justin Eilertsen, Santiago Schnell, Sebastian Walcher
{"title":"The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.","authors":"Justin Eilertsen, Santiago Schnell, Sebastian Walcher","doi":"10.1007/s11538-024-01295-z","DOIUrl":null,"url":null,"abstract":"<p><p>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 <math> <mrow><msub><mi>s</mi> <mn>0</mn></msub> <mo>≪</mo> <mi>K</mi></mrow> </math> , where <math><mrow><mi>K</mi> <mo>=</mo> <msub><mi>k</mi> <mn>2</mn></msub> <mo>/</mo> <msub><mi>k</mi> <mn>1</mn></msub> </mrow> </math> 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.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"86 6","pages":"68"},"PeriodicalIF":2.2000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11069484/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Mathematical Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11538-024-01295-z","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

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 s 0 K , where K = k 2 / k 1 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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
低底物浓度下的 Michaelis-Menten 反应:伪一阶动力学和时标分离条件。
我们证明,当初始底物浓度较低时,迈克尔斯-门顿(Michaelis-Menten)反应机制可以用线性系统来精确近似。这导致了伪一阶动力学,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属性和卡姆克比较定理。这种线性近似产生了闭式解,即使没有时标分离,也能对反应速率常数进行精确建模和估算。在先前工作的基础上,我们确定了这一近似的充分条件是 s 0 ≪ K,其中 K = k 2 / k 1 是 Van Slyke-Cullen 常数。这一条件与初始酶浓度无关。此外,我们还研究了线性系统中的时标分离,确定了必要条件和充分条件,并推导出相应的简化一元方程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.90
自引率
8.60%
发文量
123
审稿时长
7.5 months
期刊介绍: The Bulletin of Mathematical Biology, the official journal of the Society for Mathematical Biology, disseminates original research findings and other information relevant to the interface of biology and the mathematical sciences. Contributions should have relevance to both fields. In order to accommodate the broad scope of new developments, the journal accepts a variety of contributions, including: Original research articles focused on new biological insights gained with the help of tools from the mathematical sciences or new mathematical tools and methods with demonstrated applicability to biological investigations Research in mathematical biology education Reviews Commentaries Perspectives, and contributions that discuss issues important to the profession All contributions are peer-reviewed.
期刊最新文献
A Random Differential Equation Approach for Modeling the Growth of Microalgae in Photobioreactors. Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion. Mathematical Modeling of Lesion Pattern Formation in Dendritic Keratitis. Modeling and Simulation of the Role of Mass Testing in Controlling COVID-19. Inference of Genetic Networks from Pseudo Time Series of Single-cell Gene Expression Data using Modified Random Forests.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1