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 3 fully predictive models with different representational structures (diff eqns, inheritance of rates, 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 compared. It was found that constrained models with mathematical forms analogous to internal mass balancing and stoichiometric were superior to flexible unconstrained models even though no A priori knowledge of this fermentation was used.