Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches

• Published in: Bioprocess and biosystems engineering 2004-12, Vol.26 (6), p.353-359
• Main Authors: Hodgson, Benjamin J, Taylor, Christopher N, Ushio, Misti, Leigh, J R, Kalganova, Tatiana, Baganz, Frank
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cell Proliferation; Computer Simulation; Differential equations; Fermentation; Fermentation - physiology; Glucose - metabolism; Models, Biological; Nitrates - metabolism; Prediction models; Saccharopolyspora - physiology; Signal Transduction - physiology; Studies; Systems Biology - methods
• ISSN: 1615-7591; 1615-7605
• DOI: 10.1007/s00449-004-0382-0

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.