Using fuzzy logic and a hybrid genetic algorithm for metabolic modeling

John Yen, Bogju Lee, James C. Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The identification of metabolic systems such as metabolic pathways, enzyme actions, and gene regulations is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE's have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm (GA) to identify uncertain parameters in the model.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE
Pages220-225
Number of pages6
Volume1
StatePublished - 1996
EventProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3) - New Orleans, LA, USA
Duration: Sep 8 1996Sep 11 1996

Other

OtherProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3)
CityNew Orleans, LA, USA
Period9/8/969/11/96

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Yen, J., Lee, B., & Liao, J. C. (1996). Using fuzzy logic and a hybrid genetic algorithm for metabolic modeling. In IEEE International Conference on Fuzzy Systems (Vol. 1, pp. 220-225). IEEE.