Stochastic Control Analysis for Biochemical Reaction Systems

TL;DR

This paper extends metabolic control analysis to stochastic biochemical systems, introducing sensitivities for means and variances, and explores how measurement time windows influence flux fluctuations and control strategies.

Contribution

It develops a stochastic MCA framework with summation theorems and introduces a new measure for time scale separation affecting flux variance analysis.

Findings

Flux variances depend on measurement time window size.

Orthogonal control of fluctuations minimizes mean concentration changes.

Flux fluctuation control strategies vary with measurement time window.

Abstract

In this paper, we investigate how stochastic reaction processes are affected by external perturbations. We describe an extension of the deterministic metabolic control analysis (MCA) to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes and show that there exist MCA-like summation theorems among these sensitivities. The summation theorems for flux variances are shown to depend on the size of the measurement time window ($\epsilon$), within which reaction events are counted for measuring a single flux. The degree of the $\epsilon$-dependency can become significant for processes involving multi-time-scale dynamics and is estimated by introducing a new measure of time scale separation. This $\epsilon$-dependency is shown to be closely related to the power-law scaling observed in flux fluctuations in various complex networks. We propose a systematic way to control fluctuations of reactant concentrations while minimizing changes in mean concentration levels. Such orthogonal control is obtained by introducing a control vector indicating the strength and direction of parameter perturbations leading to a sensitive control. We also propose a possible implication in the control of flux fluctuation: The control distribution for flux fluctuations changes with the measurement time window size, $\epsilon$. When a control engineer applies a specific control operation on a reaction system, the system can respond contrary to what is expected, depending on the time window size $\epsilon$.