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On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis

Paper ID Volume ID Publish Year Pages File Format Full-Text
25158 43556 2007 9 PDF Available
Title
On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis
Abstract

Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach—information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.

Keywords
Metabolic regulatory network; MINLP; Information theory; Cluster analysis; Genetic algorithm
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On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Biotechnology - Volume 131, Issue 2, 31 August 2007, Pages 159–167
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us