Reverse Engineering Chemical Reaction Networks from Time Series Data
This addresses a challenging problem in chemistry and biology with potential commercial and academic impacts, though it appears incremental as it builds on existing evolutionary algorithms and least squares techniques.
The paper tackles the problem of automatically inferring chemical reaction networks and their rate coefficients from time series concentration data, achieving accurate recovery of both network topology and parameters using simulations.
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.