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.
Subject(s)
Artificial Intelligence , Glucose/metabolism , Models, Biological , Nitrates/metabolism , Saccharopolyspora/physiology , Signal Transduction/physiology , Cell Proliferation , Computer Simulation , Fermentation/physiology , Systems Biology/methodsABSTRACT
A risk sharing agreement between a managed care organization and an employer can be used by employers to either guarantee a managed care plan's short-term success or mitigate its failure. Under such arrangements, the managed care organization financially shares the employer's risk of unfavorable claims experience.
