Mathematical Models and Control of Biological Systems

Dear Colleagues,

Biological systems have a variable nature and complex behaviors, making formalizing their performance through mathematics tools an open problem for researchers. In addition, biological systems form networks for their survival, which makes these microcosms intelligent systems. In fact, there is no theoretical basis for any of the reaction mechanisms involved in biological systems proposed in the literature currently available, due to the complexity of the bio-reactions that occur in biological systems and to the exact stoichiometric relationship that allows for theoretically limiting the reaction rates. Therefore, models must be developed on an environmental conditions basis to depict the approximate phenomena under the expected biological systems conditions. Naturally, a complex mathematical model attempting to encompass so many conditions should result in great predictive capability (by increasing time and computational effort), but in real systems, this is usually not practical. As a phenomenological system, biological systems models rely entirely on experimental data to both fit and validate a proposed kinetic mechanism. If the modeling is aimed at the control, for example, an efficiency increase in the output of a biotechnological process is desired, then it is often sufficient to consider individual blocks as components and examine the stationary states of a system. By solving inverse problems, they allow the estimation of kinetic and physical parameters of a holistic system, which is impossible in experimentation without fractionating a system. Biological systems play a vital role in green industries, producing important chemical and biochemical compounds. In this system, living organisms, also known as microbes (plant, animal, and bacterial cells), are converted into marketable products. Finally, the application of biological systems control based on mathematical models is reflected in higher productivity, embracing new technologies in intelligent production and industrial growth.

Prof. Dr. Pablo Antonio López-Pérez
Guest Editor

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