ArticleNature communications2023
A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Niche Construction: A Rational Engineering Strategy for Regulating Microbial Assembly to Enhance Flavor and Bioactivity of Fermented Foods.Comprehensive reviews in food science and food safety · 2026Review
- Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis.Infectious Disease Modelling · 2026Review
- dAMN: a genome-scale neural-mechanistic hybrid model to predict bacterial growth dynamics.Bioinformatics (Oxford, England) · 2026Article
- Keystone concept revisited: insights into microbial community dynamics and control.Nature reviews. Microbiology · 2026Review
- Yeast Stress Response to Synthetic Constructs.ACS synthetic biology · 2026Review
- Engineering synthetic microbial communities for soil restoration: from rational design to multi-scale biogeochemical applications.Frontiers in microbiology · 2026Review
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- A guide to network analysis, multi-omics integration, and applications in livestock microbiome research.World journal of microbiology & biotechnology · 2025Review
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Genome-scale metabolic model-guided systematic framework for designing customized live biotherapeutic products.NPJ systems biology and applications · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Engineering microorganisms for enhanced tolerance to toxic end-products and intermediates.FEMS microbiology reviews · 2025Review
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Natural color biofactories: advancing the spectrum of pigment production in phototrophic microbes.FEMS microbes · 2025Review
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.