AMB Volume 32, Issue 1, March 2016Pages 10-25
Review-Development of Models of Ethanol Synthesis and Production by Using Principles of the Theory of System Analysis. Personal Experience
Kroumov, A. D., Módenes, A. N.
The objective of this review article was to highlight the authors’ more than 30 years’ experience in the field of model development procedure of ethanol biosynthesis. During all these years a powerful system analysis theory and its decomposition principles were applied not only for studying and description of microbial kinetics but for the needs of bioprocesses and bioreactors/photobioreactors optimization and scale-up. Special attention was given to the description of phenomena and techniques involved in ethanol synthesis on single and two substrates utilization processes by wild and genetically modified strains. The method of simultaneous saccharification and fermentation of starch to ethanol (SSFSE) by genetically modified microbial Saccharomyces cerevisiae YPB-G strain is presented to fully illustrate the strength of the applied theory. Another modern method for extractive fermentation and ethanol production is presented as well. Diauxic growth phenomena in ethanol synthesis is the example showing a modeling approach by connecting knowledge from molecular and population hierarchic levels. This modeling work helped tremendously to find new insights on the control of internal metabolic mechanisms of the cells and how the processes can be guided to an optimal trajectory. A parameter identification procedure was performed by using the latest achievements in global search methods. The applied strategy of model development was extremely successful in obtaining new knowledge about the microbial systems’ behavior as well as for fast and robust bioprocesses development. The review article can be very useful for modelers and young scientists working in the fields of biotechnology, bioengineering, metabolic and chemical engineering.
Keywords: modeling, system analysis, ethanol, kinetics, parameters identification, genetic algorithmView Full Article