Bioreactor Monitoring, Modeling, and Simulation

M athematical model–based simulations of actual bioreactor runs suggest how process variables such as substrate and product concentrations change and how nutrient feeding should be “tuned” with respect to time, pattern, concentration, and composition to elicit a desired response. Insights gained from modeling can guide us in the adjustment of a process, reducing the number of characterization rounds required. Furthermore, comparing actual experimental results with model predictions helps improve the models themselves. It is important to note that outputs can vary in unpredictable ways if processes are simulated outside boundaries set by the models that describe them, especially if the true operating ranges of actual processes are inadequately captured (58). Figure 3 lists bioreactor operational modes used in bioproduction, which are prerequisite to appreciating the differences in modeling approaches. The modeling of cellular productivity in bioreactors presents a formidable challenge because of many inherent high-degree nonlinearities (especially in batch and fed-batch culture modes), which are ultimately related to the complexities of living cells and the dynamics of in vitro culture. In response to changes in their culture environment, and driven by their genetic information, living cells alter the rate of their biochemical reactions (or the nature of those reactions) by, e.g., inducing new enzymes while repressing existing ones in an effort to find a renewed cellular homeostasis. The overall effect of the underlying mechanisms of cellular regulation dictates a nonlinear behavior in cell culture processes. The prevalence of fed-batch or repeated fed-batch operation in most commercial cell culture manufacturing processes means that most producers are operating not only in an absence of the true steady state, but also under conditions in which many individual processes follow multiple, divergent trajectories through the operational cycle. This adds to the complexity of modeling such operations. Early modeling experiences from industrial microbial culture processes (22) are of rather limited and merely conceptual modeling value for our fed-batch processes. They are predominantly operated in the “steady state” continuous mode (23–30) and rely on the optimization of steady-state culture conditions. That comes from the difference in control objectives between the two types of culture. In continuous mode, the objective is to maximize the amount of desired product per unit time, whereas in batch or fed-batch modes the goal is to maximize product at the end of each batch, leading to control challenges of a different nature. Note that another culture mode is sometimes used in industrial settings: perfusion cell culture. Although continuous by design, perfusion requires cell retention devices (31–33) that present yet another category of challenges for dynamic modeling (34). Judging from the limited publication of results so far, this technique is still in its infancy (35). Some unstructured models have been developed for fed-batch microbial (36–39) and mammalian cell culture (40–43) processes that model cell growth, substrate consumption, and product formation. Some describe the use of (artificial) neural networks (NN) and fuzzy logic (FL). Those latter so-called “expert” systems (44– 56) are introducing intriguing possibilities. Furthermore, reliable sensor technologies are seldom found that can provide real-time assessment of many intraand extracellular activities. Those currently available tend to suffer from high complexity, insufficient accuracy, risk of contamination, or insufficient robustness altogether, which makes inherently dynamic process states very difficult to characterize. NOVARTIS AG (WWW.NOVARTIS.COM)