Modeling of metabolic systems using fuzzy logic and supervisory simplex genetic algorithm /
Modeling of metabolic, pathway dynamics is a complex task due to the complexity of the system and limited knowledge about the model. Several attempts have been reported to simulate or predict system behavior based on individual component models such as enzyme kinetic equations. Mathematical equati...
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
1996.
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| Online Access: | http://proxy.library.tamu.edu/login?url=http://search.proquest.com/docview/304364723?accountid=7082 |
| Summary: | Modeling of metabolic, pathway dynamics is a complex task due to the complexity of the system and limited knowledge about the model. Several attempts have been reported to simulate or predict system behavior based on individual component models such as enzyme kinetic equations. Mathematical equations have been used to capture the structure of the component level model and the conventional optimization techniques have been used to identify the parameters. Unfortunately, most enzyme kinetic laws available in the literature do not consider all the effectors simultaneously. and much kinetic information exists in a qualitative or semi-quantitative form. Moreover, there are difficulties identifying system level parameters when the enzyme level models are combined to form the system level model. Even with a very efficient optimizer, the identification of system level parameters often fails due to its huge search space and various sensitivity of the parameters. In this research, we solve the parametric uncertainty by developing a robust and efficient optimization method termed simplex-GA hybrid. It has been successfully applied to various problems. We also present a strategy to incorporate qualitative information into kinetic equations. This strategy uses fuzzy logic-based models to modify mechanistic models that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of the simplex-GA hybrid. The resulting model provides a flexible form that can simulate various kinetic behavior. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Finally, to solve the difficulty in optimizing system level parameters, the supervisory simplex genetic algorithm has been developed, which is a hierarchical reasoning system. The supervisory simplex genetic algorithm iteratively performs the parameter optimization and supervisory level reasoning until a satisfactory model is found. Qualitative reasoning approach has been proposed to generate hypotheses for mending the discrepancy between the model output and the target data. The metabolic system under consideration in this research is the central metabolism in Escherichia coli, which includes glycolysis and the tricarboxylic acid (TCA) cycle. The obtained result demonstrated the feasibility of our approach. |
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| Item Description: | Vita. "Major Subject: Computer Science". |
| Physical Description: | xvi, 149 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references: pages 137-146. |