title: Extracting signal from noise: kinetic mechanisms from a Michaelis-Menten-like expression for enzymatic fluctuations.
abstract: Enzyme-catalyzed reactions are naturally stochastic, and precision measurements of these fluctuations, made possible by single-molecule methods, promise to provide fundamentally new constraints on the possible mechanisms underlying these reactions. We review some aspects of statistical kinetics: a new field with the goal of extracting mechanistic information from statistical measures of fluctuations in chemical reactions. We focus on a widespread and important statistical measure known as the randomness parameter. This parameter is remarkably simple in that it is the squared coefficient of variation of the cycle completion times, although it places significant limits on the minimal complexity of possible enzymatic mechanisms. Recently, a general expression has been introduced for the substrate dependence of the randomness parameter that is for rate fluctuations what the Michaelis-Menten expression is for the mean rate of product generation. We discuss the information provided by the new kinetic parameters introduced by this expression and demonstrate that this expression can simplify the vast majority of published models.
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Moffitt
authors.forename: Carlos
authors.initials: C
authors.lastname: Bustamante
mesh_terms: D000465:Algorithms; D055162:Biocatalysis; D003627:Data Interpretation, Statistical; D004798:Enzymes / Q000737:chemistry*; D007700:Kinetics; D008390:Markov Chains; D008956:Models, Chemical; D011485:Protein Binding; D013269:Stochastic Processes
chemical_list: D004798:Enzymes
keywords: continuous time Markov models; dwell time distribution; enzyme kinetics; phase-type distribution; queuing theory; randomness parameter; renewal theory; statistical kinetics