Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

中文翻译：

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大肠埃希氏菌主要代谢模型动力学参数估计的改进分段粒子群算法

建立描述生物系统中细胞行为的生物模型的目的在于了解细胞的生理学，预测酶和代谢产物的产生，并提供对生物产品有效的合适数据。另外，建立动力学模型需要动力学参数的估计，但是由于模型的非线性，动力学建模中的动力学参数估计是困难的任务。结果，动力学参数大部分是在不同条件和时间消耗下从不同实验室报告或估算的。因此，基于上述问题，优化算法方法在解决这些问题中起着重要作用。在这项研究中，提出了一种改进的分段粒子群算法（ESe-PSO）用于动力学参数估计。提出该方法以增加对分段粒子群优化算法（Se-PSO）的探索和利用。的主要代谢模型大肠杆菌用作基准，其中包含分布在五个途径中的172个动力学参数。根据仿真和实验结果之间的距离最小化，很好地估计了七个动力学参数。结果表明，该方法具有处理时间和最小距离方面的动力学参数估计的能力。