Fast growing cells, including stem cells, cancer cells, and cell lines, have a high glycolysis rate and convert most of the glucose they consume to lactate. In cell culture based biomanufacturing, excessive lactate production has been associated with reduced productivity. Prior efforts to genetically engineer metabolism to eliminate or drastically reduce the conversion of glucose to lactate while maintaining a fast growth rate have not been successful. Given the complex roles of energy metabolism in anabolism and redox homeostasis, successful suppression of lactate production in proliferating cells will likely require simultaneous alteration of multiple metabolic genes. Considering the complexity and number of reactions, we have taken a systems approach to search the parameter space intelligently for key changes to alter cellular metabolic behavior.<br/><br/>Using a nonlinear kinetic model of cell metabolism, we implemented an optimization algorithm to identify combinations of changes to genes of glucose metabolism that will maintain rapid growth with reduced lactate production. The model encompasses glycolysis, the pentose phosphate pathway, and the citric acid cycle, and includes the known allosteric regulations. The formulated multi-objective optimization problem was solved using a local nonlinear optimizer in GAMS. Constraints were chosen to maintain cellular requirements for growth. A penalty term was added to the objective function to reduce the number of genetic alterations to ease experimental design. The resulting solutions contain small sets of changes to enzyme expression that are being evaluated experimentally.<br/><br/>We have demonstrated a method using kinetic model optimization to rationally guide metabolic engineering. The optimization of kinetic models can identify sets of parameter changes that have desirable outcomes and thus greatly reduce the effort required to engineer cell metabolism, providing multiple metabolic engineering strategies and deep insight into the reaction networks and their behavior.

Advancing mammalian metabolic engineering through kinetic model optimization

255th National Meeting (2018)

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