Maximizing Information Gain for the Characterization of Biomolecular Circuits

TL;DR

This paper introduces an approximate method for optimal experimental design in biomolecular circuit characterization, leveraging mutual information and microfluidic perturbations to improve parameter inference.

Contribution

It proposes a novel approach combining multivariate log-normal approximation and Metropolis-Hastings sampling to optimize perturbation profiles for better data informativeness.

Findings

Optimized perturbation sequences increase information gain.

Method effectively estimates mutual information in complex models.

Demonstrated on synthetic gene expression models.

Abstract

Quantitatively predictive models of biomolecular circuits are important tools for the design of synthetic biology and molecular communication circuits. The information content of typical time-lapse single-cell data for the inference of kinetic parameters is not only limited by measurement uncertainty and intrinsic stochasticity, but also by the employed perturbations. Novel microfluidic devices enable the synthesis of temporal chemical concentration profiles. The informativeness of a perturbation can be quantified based on mutual information. We propose an approximate method to perform optimal experimental design of such perturbation profiles. To estimate the mutual information we perform a multivariate log-normal approximation of the joint distribution over parameters and observations and scan the design space using Metropolis-Hastings sampling. The method is demonstrated by finding optimal perturbation sequences for synthetic case studies on a gene expression model with varying reporter characteristics.