Recently, modelling and simulation have been used and applied to understand biological systems better. Therefore, the development of precise computational models of a biological system is essential. This model is a mathematical expression derived from a series of parameters of the system. The measurement of parameter values through experimentation is often expensive and time-consuming. However, if a simulation is used, the manipulation of computational parameters is easy, and thus the behaviour of a biological system model can be altered for a better understanding. The complexity and nonlinearity of a biological system make parameter estimation the most challenging task in modelling. Therefore, this paper proposes a hybrid of Particle Swarm Optimization (PSO) and Harmony Search (HS), also known as PSOHS, designated to determine the kinetic parameter values of essential amino acids, mainly aspartate metabolism, in Arabidopsis thaliana. Three performance measurements are used in this paper to evaluate the proposed PSOHS: the standard deviation, nonlinear least squared error, and computational time. The proposed algorithm outperformed the other two methods, namely Simulated Annealing and the downhill simplex method, and proved that PSOHS is a more suitable algorithm for estimating kinetic parameter values.

中文翻译：

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粒子群优化与和谐搜索相结合的拟南芥动力学参数估计

最近，已经使用建模和仿真来更好地理解生物系统。因此，开发生物系统的精确计算模型至关重要。该模型是从系统的一系列参数得出的数学表达式。通过实验来测量参数值通常是昂贵且费时的。但是，如果使用模拟，则计算参数的操作很容易，因此可以更改生物系统模型的行为以更好地理解。生物系统的复杂性和非线性使参数估计成为建模中最具挑战性的任务。因此，本文提出了粒子群优化（PSO）和和声搜索（HS）的混合体，也称为PSOHS，拟南芥。本文使用三种性能度量来评估所提出的PSOHS：标准偏差，非线性最小二乘误差和计算时间。该算法优于其他两种方法，即“模拟退火”和“下坡单纯形法”，并证明了PSOHS是一种更合适的估计动力学参数值的算法。