Year: 2012
Type: other
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 toward physically realizable solutions. No other information, such as chemical characterization of the reactive species is required, although where available it may be used to improve the search process.
Authors: Dominic P. Searson, Mark J. Willis, Allen Wright
Institution: Newcastle University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1485255279