一个基于同位素标记实验的代谢通量估计高效率算法

基于碳同位素标记实验的代谢通量分析(13C MFA)是代谢工程中的重要方法,它可以对微生物中心代谢网络中的所有代谢通量进行精确量化分析.13C MFA通过逐步拟合同位素标记状态测量数据对胞内的代谢通量进行估计,这对应于一个测量结果和计算结果之间误差平方和极小化的优化问题,该优化问题具有非线性、带有众多约束条件和存在多个局部极小点等特点,如何高效地求解是13C MFA中的难点,也是实现通量精确估计的关键.论文在对目标问题解空间的特性分析基础上,将空间知识引入到搜索过程中,针对通量估计问题提出了一个进化优化算法.实验结果和分析表明,该算法较普通进化算法具有更快的收敛速度和更好的全局寻优能力.

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.