ArticleACS synthetic biology2023
Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.
Article in ACS synthetic biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- Nanofluidics for Pioneering Synthetic Biology of Bottom-Up Cell-Free Molecular Systems.ACS synthetic biology · 2026Review
- Synthesis of Amines for Active Pharmaceutical Ingredients Using the Whole-Cell Factory Saccharomyces Cerevisae.Chembiochem : a European journal of chemical biology · 2026Review
- Unlocking Grass Stress Resistance: Fungal Endophyte-Mediated Pathogen Recognition and RNA Regulation.International journal of molecular sciences · 2026Review
- SUPER: Upcycling Genetic Parts for Precise Gene Expression Control, Leakage Minimization, and Genetic Circuit Stability.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.Frontiers in bioengineering and biotechnology · 2026Article
- Achieving bottom-up ethical, legal, and societal implications and responsible research and innovation in the synthetic biology research community from the Japanese context.Frontiers in genetics · 2026Article
- Machine learning-assisted rational design and evolution of novel signal peptides inSynthetic and systems biotechnology · 2025Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli.Microbial cell factories · 2025Article
- Understanding, inhibiting, and engineering membrane transporters with high-throughput mutational screens.Cell chemical biology · 2025Review
- Recent trends in the elucidation of complex triterpene biosynthetic pathways in horticultural trees.Horticulture research · 2025Article
- Cell-free biosensor with automated acoustic liquid handling for rapid and scalable characterization of cellobiohydrolases on microcrystalline cellulose.Synthetic biology (Oxford, England) · 2025Article
- Article
- Synthetic biology toolkit of Ralstonia eutropha (Cupriavidus necator).Applied microbiology and biotechnology · 2024Review
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.
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