Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Abstract

AbstractGenerating large omics datasets has become routine practice to gain insights into cellular processes, yet deciphering such massive datasets and determining intracellular metabolic states remains challenging. Kinetic models of metabolism play a critical role in integrating omics data, as they provide explicit connections between metabolite concentrations, metabolic fluxes, and enzyme levels. However, the difficulties in determining kinetic parameters that govern cellular physiology hinder the broader adoption of these models by the research community. Here we present RENAISSANCE (REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies), a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration and consolidation of diverse omics data and other relevant information, like extracellular medium composition, physicochemical data, and expertise of domain specialists, we show that the proposed framework accurately characterizes unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations, inE. coli’s metabolic network. Moreover, we show that RENAISSANCE successfully estimates missing kinetic parameters and reconciles them with sparse and noisy experimental data, resulting in a substantial reduction in parameter uncertainty and a notable improvement in the accuracy and reliability of the parameter estimates. The proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.