In this paper we study noise-induced bimodality in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis–Menten equations. We study the biological feasibility of this phenomenon, which allows for switch-like behavior in response to graded stimuli, considering a small and discrete number of molecules involved in the circuit, and we characterize the conditions necessary for it. We show that intrinsic noise (due to the stochastic character of the Master Equation approach) of a one-dimensional substrate reaction is not sufficient to achieve bimodality, then we characterize analytically the necessary conditions on enzyme number fluctuations. We implement numerically two model circuits that show bimodality over different parameter windows, that depend critically on system size as predicted by our results, providing hints about how such a phenomenon could be exploited in real biological systems.

Analysis of noise-induced bimodality in a Michaelis–Menten single-step enzymatic cycle

REMONDINI, DANIEL;GIAMPIERI, ENRICO;BAZZANI, ARMANDO;CASTELLANI, GASTONE;
2013

Abstract

In this paper we study noise-induced bimodality in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis–Menten equations. We study the biological feasibility of this phenomenon, which allows for switch-like behavior in response to graded stimuli, considering a small and discrete number of molecules involved in the circuit, and we characterize the conditions necessary for it. We show that intrinsic noise (due to the stochastic character of the Master Equation approach) of a one-dimensional substrate reaction is not sufficient to achieve bimodality, then we characterize analytically the necessary conditions on enzyme number fluctuations. We implement numerically two model circuits that show bimodality over different parameter windows, that depend critically on system size as predicted by our results, providing hints about how such a phenomenon could be exploited in real biological systems.
D. Remondini; E. Giampieri; A. Bazzani; G. Castellani; A. Maritan
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/128577
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