Ismail Ahmad Muhaimin, Mohamad Mohd Saberi, Abdul Majid Hairudin, Abas Khairul Hamimah, Deris Safaai, Zaki Nazar, Mohd Hashim Siti Zaiton, Ibrahim Zuwairie, Remli Muhammad Akmal
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Bachok, Kelantan, Malaysia,; Center For Computing and Informatics, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia; Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia.
Biosystems. 2017 Dec;162:81-89. doi: 10.1016/j.biosystems.2017.09.013. Epub 2017 Sep 23.
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
数学建模对于理解生物系统中生化代谢及其途径的动态行为和调节至关重要。途径用于描述涉及许多参数的复杂过程。拥有一套准确完整的参数来描述给定模型的特征非常重要。然而，测量这些参数通常很困难，在某些情况下甚至是不可能的。此外，实验数据往往不完整，还存在实验噪声。这些缺点使得识别能够代表生物系统中实际生物过程的最佳拟合参数具有挑战性。需要计算方法来估计这些参数。参数估计被转化为多模态优化问题，这需要一种能够避免局部解的全局优化算法。这些局部解在与模型进行校准时可能导致拟合不佳。尽管模型本身可能潜在地匹配一组实验数据，但需要高性能的估计算法来提高解的质量。本文描述了一种改进的粒子群优化与引力搜索算法的混合算法（IPSOGSA），以提高全局最优解（最佳动力学参数值集）搜索的效率。研究结果表明，所提出的算法能够通过利用可行解区域来缩小搜索空间。因此，所提出的算法能够以快速收敛速度实现接近最优的参数集。所提出的算法基于从生物模型数据库获得的两条天冬氨酸途径进行了测试和评估。结果表明，所提出的算法在准确性和接近最优的动力学参数估计方面优于其他标准优化算法。然而，预计所提出的算法仅在小规模系统中能良好运行。此外，本研究结果可用于在不同实验条件下的模型选择阶段估计动力学参数值。
