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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., Comment: 36 pages. In: Dehmer, M., Varmuza, K., Bonchev, D, ed. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2012, pp.327-348

This paper presents a novel method for the system identification of the fed-batch fermentation process defined in the problem statement of the Biotechnological Control Forum Modelling and Control Competition. The identification methodology involves a hybrid of mechanistic modelling and Genetic Programming techniques. It provides an accurate model of the system which should be extremely useful in both the optimisation and control of this process.