Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate

A growing amount of evidence 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 due 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. 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 magnitude of these fluctuations, as well as their phenotypic consequences.Comment: 7 Figure

Derivation of moment equations for a nonlinear gene expression model with initial condition and parameter uncertainty

In this note, we present the derivation of moment equations for a deterministic gene expression model with rational right-hand side, which is subject to initial condition and parameter uncertainty. Our derivation is based on the use moment-generating functions and the application of the transport theorem [1].[1] M. Ehrendorfer,The Liouville equation and atmospheric predictability. Cambridge University Press, 2006, p. 59–9

Analytical calculation of Sobol sensitivity indices for Gaussian Processes with a squared exponential covariance function

In this note, we present a complete analytic calculation of variance-based sensitivity indices using GP regression