Over the past few decades, the emergence of new classes of treatments, including protein therapeutics, gene therapies, and cell therapies, has ushered in a new era of medicine. Unlike small molecule therapeutics, these treatments are produced in or consist of cells, typically mammalian in origin. Processes have been developed to produce many of these drugs at large scale, often in stirred tank bioreactors. Significant effort has driven staggering increases in the productivity of these processes, enabling economical manufacturing, and the potential to drive down costs and make drugs more widely available. However, the bioreactor is not a natural environment for cells isolated from a multicellular mammalian organism. Many biological regulations are carried over from the cells’ origin and can result in numerous undesirable behaviors manifesting in the dense, highly productive reactor environment. In certain culture stages, or in the case of excess nutrient supply, cells will secrete undesirable metabolites including lactate, ammonia, and many byproducts of amino acid metabolism. These compounds can retard cell growth, or otherwise alter the potency or productivity of the cultures. Traditional biologics process development employs the use of statistical design of experiments, often encompassing several reactors run in parallel for multiple rounds of experiments over a few months. There is thus substantial room for improvement for both the outcome of the development process, such as an increase in titer, and the time it takes to complete the development stage. Given that cell culture processes share intrinsic similarities in their underlying mechanistic behavior, there exists significant opportunity to reduce the overall number of experiments needed for process development, scaling, and diagnostics using models rather than treating cell culture processes as a black box. 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. This framework is first applied to the Warburg effect, a century-old unsolved problem of rapid lactate production in proliferating cells to identify which enzymes may be altered to mitigate the lactate production. This framework in then applied to the problem of hepatic gluconeogenesis to study metabolic disease. As the expression of the enzymes specific to gluconeogenesis is not sufficient for glucose production, we explore what other requirements exist for the synthesis of glucose from different substrates. The next portion discusses the construction and optimization of a bioprocess model which includes metabolism, signaling, cell growth, and the reactor environment. This model is fit to a manufacturing-scale dataset to explore the origins of process variability and potential mitigation strategies. In the final segment of this thesis, we explore another aspect of protein therapeutics: product quality. A model of N-glycosylation is optimized in conjunction with successive rounds of experimentation with the goal of improving the galactose content on an antibody. This work highlights the benefits of feeding back experimental data to refine model parameters for better design and prediction of subsequent experiments.