An efficient approach for carbon labeling experiment-based metabolic flux estimation
Carbon-labeling experiment (CLE) -based metabolic flux analysis (13C MFA) becomes an important tool in metabolic engineering, it allows the accurate and detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. In 13C MFA, the flux distributions of an arbitrary metabolic network are estimated by fitting computational results to measured results iteratively. Actually, it corresponds to an optimization problem which minimizes a weighted distance between measurements and simulation results. Characteristics such as existence of multiple local minima and non-linear optimization make this problem a special difficulty. This paper proposes an evolutionary-based global optimization algorithm which takes advantage of the feature that the problem's feasible region is convex. Experimental and analytical results illustrate this metabolic flux estimation algorithm's efficiency.
国家科技期刊平台
NSTL
万方数据
维普
