Application of Cooperative Convolution Optimization for 13C Metabolic Flux Analysis: Simulation of Isotopic Labeling Patterns Based on Tandem Mass Spectrometry Measurements

Abstract

AbstractMetabolic fluxes are the key determinants of cellular metabolism. They are estimated from stable isotope labeling experiments and provide the informative parameters in evaluating cell physiology and causes of disease. Metabolic flux analysis involves in solving a system of non-linear isotopomer balance equations by simulating the isotopic labeling distributions of metabolites measured by tandem mass spectrometry, which is essentially an optimization problem. In this work, we introduce the cooperative coevolution optimization method for solving the set of non-linear equations that decomposes a large problem into a set of subcomponents. We demonstrate that cooperative coevolution can be used for solving the given metabolic flux model. While the proposed approach makes good progress on the use of evolutionary computation techniques for this problem, there exist a number of disadvantages that need to be addressed in the future to meet the expectation of the biologists.KeywordsMetabolic flux analysismetabolic pathwayscooperative coevolutionevolutionary algorithms