Mathematical model library for recombinant e.coli cultivation process

Biotechnological processes are among the most complicated control objects that require deep knowledge about the process. These systems have nonlinear relationships between process variables and properties that vary over time. Usually such processes are hard to model and require exceptional knowledge and experience in this field. In this review article studies conducted within the last five years in the biotechnology field, that used various model types (mechanistic models, neural networks, fuzzy models) to model cultivation processes were analyzed. Recommendations on what type of models should be used taking into account available process knowledge and experimental data were provided. Mechanistic models are best suited if there is a lack in experience in this field, advanced models like neural networks, fuzzy logic or hybrid models should be used if there is enough experimental data and process knowledge since these models tend to model the process more precisely and take in to account parameters or phenomena that cannot be described by mechanistic models. Keywords—biotechnological processes, neural networks, fuzzy logic, cell growth modeling.