461252 Optimizing Ensemble Modeling Framework to Generate Kinetic Models of Metabolism
In this work we build on the previous developments in EM by optimizing parameter screening techniques and introducing methods to reduce structural model complexity. We have investigated the trade-offs between model fitness and computation time associated with additional parameter sample space constraints, alternative model screening methods, and reduced kinetic rate law forms. We have also implemented a conservation analysis step to eliminate linear dependency in our models and reduce the stiffness and screening time of our kinetic parameter sets. Most importantly, our work toward optimizing the existing EM method can directly plug into and benefit concurrent EM efforts to develop genome-scale kinetic models across our field. Specifically, through reducing computation time and optimizing parameter sampling and model screening, we are better equipped to more extensively explore the larger parameter sample spaces associated with larger metabolic networks. Lastly, we hope our combined findings will serve as a useful starting point for future EM users to select the best sampling and screening method options to use in their own, unique applications.
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