ArticleNature catalysis2024
Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.
Article in Nature catalysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- Enzyme kinetics shapes the growth response of metabolic networks.PLoS computational biology · 2026Article
- 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
- Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.Experimental & molecular medicine · 2026Review
- A logistic model for glucose‑driven asymmetry in multi‑phase E. coli growth kinetics.Scientific reports · 2026Article
- Biophysical Sensing Tools in Drug Discovery: Integrating Kinetics, Thermodynamics, Cellular Target Engagement and Structure.Sensors (Basel, Switzerland) · 2026Review
- Metabolomic analysis for diagnosis of oral squamous cell carcinoma using machine learning.Discover oncology · 2026Article
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- Understanding plant resilience by putting photosynthesis and photorespiration in the metabolic context.Planta · 2026Review
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- From single cells to communities: Mathematical perspectives on bacterial quorum sensing.Computational and structural biotechnology journal · 2025Review
- Biotechnology systems engineering: preparing the next generation of bioengineers.Frontiers in systems 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
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in
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