Identifiability of Chemical Reaction Networks with Intrinsic and Extrinsic Noise from Stationary Distributions

TL;DR

This paper develops a framework to determine if reaction rate parameters in biological chemical networks can be uniquely identified from stationary distribution data, using linear noise approximation and algebraic methods.

Contribution

It introduces a novel computational approach for global identifiability analysis of reaction networks from stationary data, applicable to systems biology.

Findings

Method successfully certifies identifiability in example networks.

Applicable to biochemical mechanism discrimination.

Uses Hilbert's Nullstellensatz for algebraic certification.

Abstract

Many biological systems can be modeled as a chemical reaction network with unknown parameters. Data available to identify these parameters are often in the form of a stationary distribution, such as that obtained from measurements of a cell population. In this work, we introduce a framework for analyzing the identifiability of the reaction rate coefficients of chemical reaction networks from stationary distribution data. Working with the linear noise approximation, which is a diffusive approximation to the chemical master equation, we give a computational procedure to certify global identifiability based on Hilbert's Nullstellensatz. We present a variety of examples that show the applicability of our method to chemical reaction networks of interest in systems and synthetic biology, including discrimination between possible molecular mechanisms for the interaction between biochemical species.