Interval Analysis of Worst-case Stationary Moments for Stochastic Chemical Reactions with Uncertain Parameters

TL;DR

This paper introduces a semidefinite programming framework to compute guaranteed bounds on the stationary moments of stochastic chemical reactions with uncertain parameters, effectively capturing combined intrinsic and extrinsic noise effects.

Contribution

It develops a novel, non-approximate optimization method to determine interval bounds of moments under partial distribution information, advancing uncertainty analysis in stochastic reaction modeling.

Findings

Provides guaranteed bounds on stationary moments for uncertain parameters.

Demonstrates effectiveness with two stochastic chemical reaction examples.

Offers a computational approach that surpasses sampling-based estimation methods.

Abstract

The dynamics of cellular chemical reactions are variable due to stochastic noise from intrinsic and extrinsic sources. The intrinsic noise is the intracellular fluctuations of molecular copy numbers caused by the probabilistic encounter of molecules and is modeled by the chemical master equation. The extrinsic noise, on the other hand, represents the intercellular variation of the kinetic parameters due to the variation of global factors affecting gene expression. The objective of this paper is to propose a theoretical framework to analyze the combined effect of the intrinsic and the extrinsic noise modeled by the chemical master equation with uncertain parameters. More specifically, we formulate a semidefinite program to compute the intervals of the stationary solution of uncertain moment equations whose parameters are given only partially in the form of the statistics of their distributions. The semidefinite program is derived without approximating the governing equation in contrast with many existing approaches. Thus, we can obtain guaranteed intervals of the worst possible values of the moments for all parameter distributions satisfying the given statistics, which are prohibitively hard to estimate from sample-path simulations since sampling from all possible uncertain distributions is difficult. We demonstrate the proposed optimization approach using two examples of stochastic chemical reactions and show that the solution of the optimization problem gives informative upper and lower bounds of the statistics of the stationary copy number distributions.