Using genetic algorithms to optimize functions of microbial ecosystems


Vandecasteele, Frederik P. J.. (2006). Using genetic algorithms to optimize functions of microbial ecosystems. Theses and Dissertations Collection, University of Idaho Library Digital Collections.

Using genetic algorithms to optimize functions of microbial ecosystems
Vandecasteele, Frederik P. J.
Microbial ecology--Computer simulation Genetic algorithms
Biological and Agricultural Engineering
Microbial ecology is the study of the interrelationships between microorganisms and their environment. Understanding and controlling microbial processes is of great practical and scientific importance. In this dissertation, two methods are distinguished by which microbial systems can be engineered. First, artificial microbial systems can be assembled by combining- isolated strains. Second, existing natural microbial systems can be driven in desirable directions by manipulating environmental conditions. Designing efficient microbial systems in either of these two fashions requires a sound methodology. Genetic algorithms are search and optimization methods inspired by the biological principle of evolution through natural selection. These optimizers can operate on large numbers of interacting variables, can cope with considerable levels of noise, and do not require a detailed understanding of a system that is to be optimized. These three properties make them an interesting approach to apply in the field of experimental microbial ecology.;In this dissertation, the use of genetic algorithms for the optimization of functions of microbial ecosystems is proposed, and applied in two distinct manners. First, genetic algorithms were applied for the design of efficient mixed microbial cultures from sets of isolated strains. Second, they were used to drive samples of natural, undefined microbial ecosystems in desirable directions by optimizing the presence or absence of sets of chemical supplements. Each of these two proposed approaches was verified in two ways. First, the suitability of a genetic algorithm for each of the design tasks was verified in principle by comparing its performance at searching through multiple exhaustive empirical microbial datasets to that of three competing intuitive optimization methods. Second, the performance of a genetic algorithm for each of the design tasks was evaluated in multiple full-scale experiments. The results of this work showed that genetic algorithms can be successfully used to optimize functions of microbial ecosystems. Genetic algorithms can become a tool in experimental microbial ecology for better managing engineered microbial processes and for performing more flexible and realistic scientific investigations.
Thesis (Ph. D., Biological and Agricultural Engineering)--University of Idaho, July 2006.
Major Professor:
Thomas F. Hess.
Defense Date:
July 2006.
Format Original:
ix, 127 leaves :ill. ;29 cm.

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