Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks.

ABSTRACT: Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significa

SUBMITTER: Choudhury S 

PROVIDER: S-EPMC10543203 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature