Integration of Kinetic and Whole-Cell Stoichiometric Model For Hybrid Simulations of Bacterial Central Metabolism.

The bacterial cell is a dynamical system by nature, composed of a number of components whose complexity of interactions yields the diversity in cell physiology. A central principle of the paradigm of system biology is to iteratively construct models to elucidate systemic properties, furthering knowledge for the potential emergence of cellular behaviours. Concentrating on modelling dynamics and regulatory effects of the reaction kinetics of the metabolic state of model organism E. coli K-12, the ultimate objective is to present the development of a novel algorithm which bridges communication between a stoichiometric genome-scale model and a kinetic model of bacterial central carbon metabolism. To enable a consistent integration and communication between the two model types, I parameterize the models with the same multi-omics steady state datasets from the Keio Multi-omics database, ensuring that both models represent the same bacterial strain. Along the way, I show how a re-parameterization of the genome-scale model alone can be powerful enough to make it as predictive as carbon-13 metabolic flux analysis techniques, with regards to predicting metabolic flux distribution amongst reactions of the central carbon metabolism. I also show that, from a highly detailed reconstruction of a kinetic model, regulatory effects of metabolites on kinetics of cellular reactions is sufficient to create the potential for expression of alternative phenotypes, and even for the co-existence of metabolically distinct phenotypes. I describe the development and novelty of our integration algorithm, discussing how certain steps of the algorithm help to overcome known issues, allows for the ease of incorporation of future expansions on the kinetic and/or genome-scale models, as well as discussing how the two model types pass information of flux between one another. Simulations of the integrated model demonstrate its flexibility and power to incorporate growth feasibility, but also uncovers some issues, which are discussed as potential future works to a key methodology