ArticleNature communications2024
Rational strain design with minimal phenotype perturbation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- A logistic model for glucose‑driven asymmetry in multi‑phase E. coli growth kinetics.Scientific reports · 2026Article
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- COBRA-k: A powerful framework bridging constraint-based and kinetic metabolic modeling.Science advances · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli.Microbial cell factories · 2025Article
- Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.mSystems · 2024Article
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.
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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.