Literature DB >> 28747400

Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate.

Ankit Gupta1, Andreas Milias-Argeitis1,2, Mustafa Khammash3.   

Abstract

A growing amount of evidence over the last two decades points to the fact that many enzymes exhibit fluctuations in their catalytic activity, which are associated with conformational changes on a broad range of timescales. The experimental study of this phenomenon, termed dynamic disorder, has become possible thanks to advances in single-molecule enzymology measurement techniques, through which the catalytic activity of individual enzyme molecules can be tracked in time. The biological role and importance of these fluctuations in a system with a small number of enzymes, such as a living cell, have only recently started being explored. In this work, we examine a simple stochastic reaction system consisting of an inflowing substrate and an enzyme with a randomly fluctuating catalytic reaction rate that converts the substrate into an outflowing product. To describe analytically the effect of rate fluctuations on the average substrate abundance at steady state, we derive an explicit formula that connects the relative speed of enzymatic fluctuations with the mean substrate level. Under fairly general modelling assumptions, we demonstrate that the relative speed of rate fluctuations can have a dramatic effect on the mean substrate, and lead to large positive deviations from predictions based on the assumption of deterministic enzyme activity. Our results also establish an interesting connection between the amplification effect and the mixing properties of the Markov process describing the enzymatic activity fluctuations, which can be used to easily predict the fluctuation speed above which such deviations become negligible. As the techniques of single-molecule enzymology continuously evolve, it may soon be possible to study the stochastic phenomena due to enzymatic activity fluctuations within living cells. Our work can be used to formulate experimentally testable hypotheses regarding the nature and magnitude of these fluctuations, as well as their phenotypic consequences.
© 2017 The Author(s).

Keywords:  continuous-time Markov chains; dynamic disorder; stochastic chemical kinetics

Mesh:

Substances:

Year:  2017        PMID: 28747400      PMCID: PMC5550977          DOI: 10.1098/rsif.2017.0311

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  21 in total

1.  Stochastic gene expression in a single cell.

Authors:  Michael B Elowitz; Arnold J Levine; Eric D Siggia; Peter S Swain
Journal:  Science       Date:  2002-08-16       Impact factor: 47.728

2.  Quantum dynamics with non-Markovian fluctuating parameters.

Authors:  Igor Goychuk
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2004-07-07

3.  Rate processes with non-Markovian dynamical disorder.

Authors:  Igor Goychuk
Journal:  J Chem Phys       Date:  2005-04-22       Impact factor: 3.488

Review 4.  Fluctuating enzymes: lessons from single-molecule studies.

Authors:  Wei Min; Brian P English; Guobin Luo; Binny J Cherayil; S C Kou; X Sunney Xie
Journal:  Acc Chem Res       Date:  2005-12       Impact factor: 22.384

5.  Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited.

Authors:  Brian P English; Wei Min; Antoine M van Oijen; Kang Taek Lee; Guobin Luo; Hongye Sun; Binny J Cherayil; S C Kou; X Sunney Xie
Journal:  Nat Chem Biol       Date:  2005-12-25       Impact factor: 15.040

6.  On stochastic models of dynamic disorder.

Authors:  David R Reichman
Journal:  J Phys Chem B       Date:  2006-09-28       Impact factor: 2.991

7.  Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.

Authors:  J Hasenauer; V Wolf; A Kazeroonian; F J Theis
Journal:  J Math Biol       Date:  2013-08-06       Impact factor: 2.259

Review 8.  Sizing up single-molecule enzymatic conformational dynamics.

Authors:  H Peter Lu
Journal:  Chem Soc Rev       Date:  2014-02-21       Impact factor: 54.564

9.  Single-molecule enzymatic dynamics.

Authors:  H P Lu; L Xun; X S Xie
Journal:  Science       Date:  1998-12-04       Impact factor: 47.728

10.  Allostery: An Overview of Its History, Concepts, Methods, and Applications.

Authors:  Jin Liu; Ruth Nussinov
Journal:  PLoS Comput Biol       Date:  2016-06-02       Impact factor: 4.475

View more
  3 in total

1.  Stochastic modelling reveals mechanisms of metabolic heterogeneity.

Authors:  Mona K Tonn; Philipp Thomas; Mauricio Barahona; Diego A Oyarzún
Journal:  Commun Biol       Date:  2019-03-21

2.  Computation of Single-Cell Metabolite Distributions Using Mixture Models.

Authors:  Mona K Tonn; Philipp Thomas; Mauricio Barahona; Diego A Oyarzún
Journal:  Front Cell Dev Biol       Date:  2020-12-22

3.  Metabolite Sequestration Enables Rapid Recovery from Fatty Acid Depletion in Escherichia coli.

Authors:  Christopher J Hartline; Ahmad A Mannan; Di Liu; Fuzhong Zhang; Diego A Oyarzún
Journal:  mBio       Date:  2020-03-17       Impact factor: 7.867

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.