Industrial CHO cell fed-batch processes have progressed significantly over the past decade, with recombinant protein titer consistently reaching the gram per liter level. Such improvements have largely resulted from separate advances in process and cell line development. Model-based selection of targets for metabolic engineering in CHO cells is confounded by the dynamic nature of the fed-batch process. In this work, we use a dynamic model of CHO cell metabolism to simultaneously identify both process and cell modifications that improve antibody production. Model simulations explored ca. 9200 combinations of process variables (shift temperature, shift day, seed density, and harvest day) and knockdowns (8 metabolic enzymes). A comprehensive examination of a simulated solution space showed that optimal gene knockdown clearly depends on the process parameters such as temperature shift day, shift temperature, and seed density. Knockdown of enzymes related to lactate production were the most beneficial; however, depending on the process conditions, modulating such enzymes yielded varying productivities, ranging from a reduction in final titer to greater than 2-fold improvement.