Ghodba, Ali2026-01-202026-01-202026-01-202026-01-19https://hdl.handle.net/10012/22853Monoclonal antibodies (mAbs) are widely produced in mammalian cell cultures, with Chinese Hamster Ovary (CHO) cells being the predominant host cell used in the pharmaceutical industry. The growing global demand for mAbs has driven significant advances in biomanufacturing and motivated the pharmaceutical sector to develop strategies that enhance productivity. Among the key factors influencing mAb yield are the operating conditions of CHO cell cultures, such as pH and temperature. Optimizing these parameters is therefore essential for improving process performance and product quality. Model-based optimization offers a powerful and systematic approach for improving complex bioprocesses, including mAb production. Combining mechanistic understanding with mathematical modeling, it enables quantitative prediction of process behavior and identification of optimal operating strategies without excessive experimentation. In essence, model-based optimization relies on two critical components: (1) a dynamic model capable of accurately describing process behavior, and (2) an optimization algorithm that determines the best operating conditions based on model predictions. The effectiveness of a Model-based optimization depends on both components working reliably to ensure convergence toward realistic and true optima. The repetitive nature of batch processes makes them particularly suitable for batch-to-batch optimization, where information from previous runs is used to improve future ones. In this iterative framework, process measurements from a completed batch are used to update the model and compute the optimal input profile for the next experiment. However, optimization of mammalian cell cultures is challenging because of the strong nonlinearities and interactions among growth, metabolism, and product formation under varying environmental conditions. These complexities often lead to model–plant mismatches, so parameters estimated through model identification may not accurately reproduce the true gradients of the cost function or constraints, which are quantities that are essential for optimization. To address this, a modified batch-to-batch optimization, so-called the simultaneous identification and optimization method, is employed. This approach forces the model-predicted gradients to match experimentally measured gradients by adjusting model parameters, while an output correction term ensures that previously achieved fitting accuracy is retained. Consequently, the resulting parameter set satisfies both identification and optimization objectives even when structural model errors are present. Despite its potential, several challenges must be overcome before applying this framework to complex biological systems. Previous studies have computed and corrected gradients only at the end of each batch; however, incorporating transient, within-batch measurements could provide richer information and improve the characterization of model discrepancies. Additionally, integrating optimal experimental design can enhance parameter identifiability and accelerate convergence, and the framework can be further extended to continuous operation modes. Most importantly, the methodology has not yet been thoroughly tested in a real experimental system to demonstrate its performance and robustness. A reliable mechanistic model capable of describing CHO cell metabolism under varying process conditions is also essential but remains insufficiently explored. Two major modeling paradigms exist for bioprocesses: kinetic models and dynamic flux balance analysis (dFBA). Kinetic models employ ordinary differential equations to relate measurable process variables, such as viable cell density, substrate and by-product concentrations, and product titer, to underlying rates of growth, uptake, and synthesis. In contrast, dFBA models optimize a biological objective (e.g., growth rate) with respect to intracellular fluxes subject to stoichiometric and steady-state constraints. Compared to purely kinetic models, dFBA frameworks can offer deeper physiological insight and require fewer parameters, making them particularly attractive for model-based optimization and control. Building upon these concepts, this thesis first presents a novel dFBA model integrated with kinetic constraints to predict the dynamic metabolism of CHO cells under varying pH and temperature conditions in fed-batch cultures. The model captures the main metabolic behaviors across different operating conditions. In the subsequent chapters, the batch-to-batch optimization framework is extended and modified for both batch and continuous bioprocesses, incorporating gradient correction and optimal experiment design to ensure robustness and faster convergence. Finally, the developed methodology is implemented and experimentally validated using an AMBR-15 mini-bioreactor system, where it is applied to determine the optimal pH profile that maximizes monoclonal antibody production in CHO cultures.enCHOoptimizationmodel-plant mismatchdynamic flux balance analysiseMPCDoctoral Thesis