MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling
Kazuhiro Maeda, Aoi Hatae, Yukie Sakai, Fred C. Boogerd, Hiroyuki Kurata
Abstract
Abstract 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 (K m ), 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 K m estimation of kinetic modeling. First, we use a machine learning-based K m 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 K m values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R 2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping K m values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated K m values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated K m 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 K m 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.