背景:动力学建模是了解生化系统动态行为的强大工具。对于动力学建模,确定一些动力学参数,例如米氏常数(Km),是必要的,和全局优化算法长期以来一直用于参数估计。然而,传统的全局优化方法有三个问题:(I)计算要求很高。(ii)它通常会产生不切实际的参数值,因为它只是寻求适合实验观察到的行为的更好的模型。(iii)由于多个参数集可以使动力学模型同样很好地拟合实验数据(不可识别性问题),因此难以识别唯一的解决方案。
结果:为了解决这些问题,我们提出了机器学习辅助全局优化(MLAGO)方法,用于动力学建模的Km估计。首先,我们使用基于机器学习的Km预测器,仅基于三个因素:EC数,KEGG化合物ID,和生物体ID,然后使用机器学习预测的Km值作为参考值进行基于约束全局优化的参数估计。机器学习模型取得了相对较好的预测分数:RMSE=0.795,R2=0.536,使得后续的全局优化变得简单实用。MLAGO方法减少了模拟和实验数据之间的误差,同时保持Km值接近机器学习预测值。因此,MLAGO方法成功地估计了Km值,其计算成本比传统方法低。此外,MLAGO方法唯一估计的Km值,接近测量值。
结论:MLAGO克服了参数估计中的主要问题,加速动力学建模,从而最终导致对复杂细胞系统的更好理解。我们基于机器学习的Km预测器的Web应用程序可在https://站点访问。google.com/view/kazuhiro-maeda/software-tools-web-apps,这有助于建模者对自己的参数估计任务执行MLAGO。
BACKGROUND: Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).
RESULTS: To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values.
CONCLUSIONS: MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.