Reverse Engineering Chemical Reaction Networks from Time Series Data

TL;DR

This paper presents a method to automatically infer chemical reaction networks and their rate coefficients from time series data using an evolutionary algorithm, enabling accurate reconstruction without prior chemical knowledge.

Contribution

It introduces a novel approach combining differential evolution and stoichiometric matrix properties to accurately recover reaction network topology and parameters from experimental data.

Findings

Successfully recovers reaction networks from simulated data.

No prior chemical characterization needed for inference.

Biases search towards physically feasible solutions.

Abstract

The automated inference of physically interpretable (bio)chemical reaction network models from measured experimental data is a challenging problem whose solution has significant commercial and academic ramifications. It is demonstrated, using simulations, how sets of elementary reactions comprising chemical reaction networks, as well as their rate coefficients, may be accurately recovered from non-equilibrium time series concentration data, such as that obtained from laboratory scale reactors. A variant of an evolutionary algorithm called differential evolution in conjunction with least squares techniques is used to search the space of reaction networks in order to infer both the reaction network topology and its rate parameters. Properties of the stoichiometric matrices of trial networks are used to bias the search towards physically realisable solutions. No other information, such as chemical characterisation of the reactive species is required, although where available it may be used to improve the search process.