On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis.
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.
Zheng
Ying
Y
Chemical Engineering Department, National Tsing-Hua University, Hsinchu 300, Taiwan.
Yeh
Chen-Wei
CW
Yang
Chi-Da
CD
Jang
Shi-Shang
SS
Chu
I-Ming
IM
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D005240:Feasibility Studies; D005786:Gene Expression Regulation; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008954:Models, Biological; D008957:Models, Genetic