ABSTRACT
It is extremely important from a scientific and technological point of view to understand cell behavior under different culture conditions. This knowledge allows control of the processes that generate important commercial products and establishes a rational basis to increase cell productivity and yield. By understanding cell behavior, it is possible to identify factors that
limit and inhibit cell growth and product synthesis. This allows improvements in culture media formulation, defining adequate culture conditions and keeping them at optimum values through process control. The optimization of these conditions allows an increase in reaction rates and their control, resulting in enhanced process productivity, higher final product concentration, and increased substrate-to-product conversion factor. This process optimization, in general, may also allow improvements in product quality, insuring technical and economic viability. The systematization and quantification of this knowledge through
mathematical equations, called mathematical modeling, relate culture conditions to growth, death, nutrient consumption, and product synthesis rates. Solving these equations allows a quantitative prediction of cell behavior, meaning that it is possible to simulate the changes in the major variables (such as component concentrations) with time. Even at the initial stages of model development, before it is capable of representing the totality of the observed phenomena, it is possible to use a model for the simulation of different culture conditions. The capacity to predict the dynamics of the population helps to understand the system under investigation, because it may guide experimental planning, reduce the number of experiments, and allow estimates of un-measured variables, as well as providing rational criteria for process design, optimization, and control of bioreactors. Although animal cells have the ability to produce high value proteins
with post-translational modifications appropriate for clinical application,
there is an urgent need to increase bioprocess efficiency and productivity. The reduction of cost is essential to insure that animal cell culture remains competitive compared to alternative technologies such as ribosome display and transgenic animals. Improvements can be made by a biological approach, as discussed in Chapter 3, or based on the optimization of culture conditions. Mathematical modeling is probably the most efficient technique for the optimization of culture conditions. The construction of a mathematical model involves the following steps,
as developed by several authors (Engasser et al., 1998; Miller and Reddy, 1998; Bonomi and Schmidell, 2001).
