Various reasons can be listed for making and using mathematical models of bioprocesses, each important in some venue of biotechnology :
• To organize disparate information into a coherent whole: Molecular biology and biotechnology have generated and will continue to generate vast amounts of information about components involved in various biological processes and their properties. Models will enable integrated consideration of many interacting components that would shed light on many questions about the genes and their integrated roles.
• To think logically about components and interactions that are important in a complex system and calculate them: As more experimental data become available on protein-nucleic acid interactions and theoretical possibilities to predict effects of sequence changes on these parameters improve, genetically structured models can provide a unique resource for predicting the relationship between nucleotide sequence and complex functions of the organism.
• To discover new strategies in process operation: Cell metabolism can be controlled by manipulation of environmental parameters such as pH, temperature, dissolved oxygen, aeration rate and other operating conditions. Cell metabolism is reflected in measurable quantities of the culture (measured variables). Models are the crucial link between these two groups of variables, enabling implementation of an algorithm for operating the process effectively based on accessible on-line measurements of the process.
• To test and modify conventional wisdom: Many situations encountered in biochemical engineering, and biological science research are extremely complex. It is easy to make erroneous hypotheses or assumptions about a bioprocess. Mathematical modeling and analysis of the resulting model, can aid substantially in avoiding such mistakes or in identifying errors or omissions in earlier thinking and interpretations.
• To understand the essential, qualitative features: When analyzing a complex system, it is often sufficient to have certain qualitative results without the need for particular numerical value. Qualitative analysis becomes increasingly important as the system under investigation becomes complex.
• To build model-based monitoring, control and diagnosis techniques: Multivariable first principles and data-based models are critical for developing powerful techniques for monitoring and controlling process operations and for diagnosing source causes of faulty operation.
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