The global bio-manufacturing industry requires improved process efficiency to satisfy the increasing demands for biochemicals, biofuels, and biologics. The use of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce labor and operating costs by identifying the most informative experiments and providing strategies to optimize the bioprocess at hand. Herein, we investigate the potential of a research methodology that combines model development, parameter estimation, global sensitivity analysis, and selection of optimal feeding policies via dynamic optimization methods to improve the efficiency of an industrially relevant bioprocess. Data from a set of batch experiments was used to estimate values for the parameters of an unstructured model describing monoclonal antibody (mAb) production in GS-NS0 cell cultures. Global Sensitivity Analysis (GSA) highlighted parameters with a strong effect on the model output and data from a fed-batch experiment were used to refine their estimated values. Model-based optimization was used to identify a feeding regime that maximized final mAb titer. An independent fed-batch experiment was conducted to validate both the results of the optimization and the predictive capabilities of the developed model. The successful integration of wet-lab experimentation and mathematical model development, analysis, and optimization represents a unique, novel, and interdisciplinary approach that addresses the complicated research and industrial problem of model-based optimization of cell based processes. Biotechnol. Bioeng. 2015;112: 536-548. (c) 2014 Wiley Periodicals, Inc.