ArticleNature machine intelligence2022
Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Kinetic parameter prediction using neural networks identifies limitations to CThe New phytologist · 2026Article
- Organ-on-a-Chip and Microfluidic Plant Cell Culture Systems: The Next Frontier for Controlled Secondary Metabolite Production and Real-Time Metabolomic Monitoring.Plants (Basel, Switzerland) · 2026Review
- Metabolism as the biochemical language for mechanistic biomedical AI.Nature metabolism · 2026Article
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- A logistic model for glucose‑driven asymmetry in multi‑phase E. coli growth kinetics.Scientific reports · 2026Article
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- A standardized workflow for kinetic metabolic model curation and dissemination.PLoS computational biology · 2026Article
- Understanding Iron and Oxidative Stress Response inbioRxiv : the preprint server for biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Interpretable-AI-Based Model Structural Transfer Learning to Accelerate Bioprocess Model Construction.Biotechnology and bioengineering · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Mitochondrial Metabolomics in Cancer: Mass Spectrometry-Based Approaches for Metabolic Rewiring Analysis and Therapeutic Discovery.Metabolites · 2025Review
- Spatiotemporal Heterogeneity of Tumor Glucose Metabolism Reprogramming: From Single-Cell Mechanisms to Precision Interventions.International journal of molecular sciences · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.Nature communications · 2025Article
- Robust Prediction of Enzyme Variant Kinetics with RealKcat.bioRxiv : the preprint server for biology · 2025Article
- Enhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs inBioinformatics advances · 2025Article
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
- Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli.NPJ systems biology and applications · 2024Article
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6 authors.
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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 significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.
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