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~(13)C标记实验的代谢通量分析(~(13)CMFA)是探索代谢网络的重要途径。~(13)C通量估计是以碳富集度平衡为条件的全局优化问题,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~(13)C MFA中的难点,也是实现通量精确估计的关键。量子粒子群优化算法显著特点是控制参数少,设置简单,具有较好的全局搜索能力,适应于通量估计。本文提出量子粒子群优化算法结合最小二乘计算求解噪音环境下的环磷酸戊糖代谢网络的通量,以带约束的最小化问题为目标优化函数,仿真实验验证了量子粒子群优化算法是1种有效的通量估计分析算法。
Metabolic Flux Analysis of ~ (13) C Labeled Experiments (~ (13) CMFA) is an Important Approach to Explore Metabolic Networks. ~ (13) C flux estimation is a global optimization problem based on the equilibrium of carbon enrichment, with many constraints and multiple local minima. How to efficiently solve the problem is ~ (13) C MFA The difficulty, but also the key to accurate estimation of flux. Quantum particle swarm optimization algorithm is characterized by fewer control parameters, simple setup, better global search capability and adaptability to flux estimation. In this paper, quantum particle swarm optimization algorithm combined with least-squares calculation to solve the flux of pentose ring metabolic networks in the noise environment, the constrained minimization problem as the objective optimization function, the simulation results show that the quantum particle swarm optimization algorithm is 1 An Effective Flux Estimation Analysis Algorithm.