title: Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches.
abstract: This contribution moves in the direction of answering some general questions about the most effective and useful ways of modelling bioprocesses. We investigate the characteristics of models that are good at extrapolating. We trained three fully predictive models with different representational structures (differential equations, differential equations with inheritance of rates and a network of reactions) on Saccharopolyspora erythraea shake flask fermentation data using genetic programming. The models were then tested on unseen data outside the range of the training data and the resulting performances were compared. It was found that constrained models with mathematical forms analogous to internal mass balancing and stoichiometric relations were superior to flexible unconstrained models, even though no a priori knowledge of this fermentation was used.
authors.affiliation: The Advanced Centre for Biochemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Hodgson
authors.forename: Christopher N
authors.initials: CN
authors.lastname: Taylor
authors.forename: Misti
authors.initials: M
authors.lastname: Ushio
authors.forename: J R
authors.initials: JR
authors.lastname: Leigh
authors.forename: Tatiana
authors.initials: T
authors.lastname: Kalganova
authors.forename: Frank
authors.initials: F
authors.lastname: Baganz
mesh_terms: D001185:Artificial Intelligence; D049109:Cell Proliferation; D003198:Computer Simulation; D005285:Fermentation / Q000502:physiology; D005947:Glucose / Q000378:metabolism*; D008954:Models, Biological; D009566:Nitrates / Q000378:metabolism*; D016930:Saccharopolyspora / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods
chemical_list: D009566:Nitrates; D005947:Glucose