Designing Experiments to Understand the Variability in Biochemical Reaction Networks

TL;DR

This paper develops a method to quantify information from distribution measurements in stochastic biochemical models, enabling optimal experimental design to distinguish noise sources in gene expression.

Contribution

It introduces formulas based on the first four moments for approximating information content in complex stochastic models, facilitating experimental planning.

Findings

Derived formulas valid for models with intrinsic and extrinsic noise

Proposed an optimal experimental design framework for cell heterogeneity

Showed that specific gene induction patterns improve system feature identification

Abstract

Exploiting the information provided by the molecular noise of a biological process has proven to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single cell measurements. However, quantifying this additional information a priori, to decide whether a single cell experiment might be beneficial, is currently only possibly in very simple systems where either the chemical master equation is computationally tractable or a Gaussian approximation is appropriate. Here we show how the information provided by distribution measurements can be approximated from the first four moments of the underlying process. The derived formulas are generally valid for any stochastic kinetic model including models that comprise both intrinsic and extrinsic noise. This allows us to propose an optimal experimental design framework for heterogeneous cell populations which we employ to compare the utility of dual reporter and perturbation experiments for separating extrinsic and intrinsic noise in a simple model of gene expression. Subsequently, we compare the information content of different experiments which have been performed in an engineered light-switch gene expression system in yeast and show that well chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.