In this thesis, we present the case for the use of mathematical optimization of mechanistic models to accurately describe cell culture processes and augment their behavior. We first outline recent advances in understanding of metabolic regulation and homeostasis. Cell signaling and metabolic networks interact over multiple time-scales and through multiple means, resulting in cell metabolism with nonlinear behavior that is consequently context-dependent. In the following sections of this work, we then develop an optimization framework which can efficiently be used for the design of experiments to rewire cellular metabolism through metabolic engineering, or to otherwise understand the biological requirements of different metabolic phenomena.
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