ArticleNature communications2020
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
What it found
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Who cites it
108 citing papers in PubMed, 270 citations in OpenAlex.
- Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model.Journal for immunotherapy of cancer · 2022Trial
- Advances and perspectives in genetically encoded biosensors for biomarker detection and theranostic circuits.Synthetic and systems biotechnology · 2027Review
- Microbial Production of Alternative Proteins for Food Applications: Advances, Industrial Challenges, and Regulatory Status.Comprehensive reviews in food science and food safety · 2026Review
- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- A unifying equation for fermentation sustainability across the titer-rate-yield landscape.Nature communications · 2026Article
- Integrated multi-dimensional modeling of non-model bacteria identifies engineering targets for acarbose biosynthesis optimization.Cell reports methods · 2026Article
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Advances in metabolic engineering of Bacillus subtilis towards high-efficiency cytidine biosynthesis.World journal of microbiology & biotechnology · 2026Review
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Engineering yeast for sustainable bioproduction of tryptophan-derived aromatic compounds.FEMS yeast research · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Electrochemical Biosensor for Rapid Detection of Acute Rejection in Kidney Transplants.Advanced healthcare materials · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.Frontiers in bioengineering and biotechnology · 2026Article
- Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida.Nature communications · 2025Article
- Advances in the microbial biosynthesis of ʟ-tryptophan and its derivatives.Biodesign research · 2025Review
- Advances in the microbial biosynthesis of therapeutic terpenoids.Current opinion in biotechnology · 2025Review
- Development and application of synthetic biology tools for improved production of human breast milk components.Synthetic and systems biotechnology · 2025Review
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- Engineering and Application of Biosensors for Aromatic Compounds Production inMicroorganisms · 2025Review
48 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
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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.