A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

An alternative to traditional kinetic modeling by using machine learning. Our goal is to use time series proteomics data to predict time-series metabolomics data (Fig. (Fig.2).2). The traditional approach involves using ordinary differential equations where the change in metabolites over time is given by Michaelis–Menten kinetics (Figs. (Figs.3 and3 and and4).4). The alternative approach proposed here uses time series of proteomics and metabolomics data to feed machine learning algorithms in order to predict pathway dynamics (Eq. (1) and Supplementary Fig. S1). While the machine learning approach necessitates more data, it can be automatically applied to any pathway or host, leverages systematically new data sets to improve accuracy, and captures dynamic relationships which are unknown by the literature or have a different dynamic form than Michaelis–Menten kinetic