ReviewBiochemical Society transactions2023
Applications of artificial intelligence and machine learning in dynamic pathway engineering.
Review in Biochemical Society transactions, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.Clinical and experimental medicine · 2025Pooled it
- Metabolic engineering ofApplied and environmental microbiology · 2026Review
- Mining the Hidden Pharmacopeia: Fungal Endophytes, Natural Products, and the Rise of AI-Driven Drug Discovery.International journal of molecular sciences · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Probiotic-Based Materials as Living Therapeutics.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Development and application of synthetic biology tools for improved production of human breast milk components.Synthetic and systems biotechnology · 2025Review
- Applications of Large Language Models and Prompt Optimization for Knowledge Extraction from Biological Pathway Figures.IEEE journal of biomedical and health informatics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Recent advances of muconic acid production using microbial synthetic biology.Archives of microbiology · 2025Review
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- The switch-liker's guide to plant synthetic gene circuits.The Plant journal : for cell and molecular biology · 2025Review
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Feed-forward loop improves the transient dynamics of an antithetic biological controller.Journal of the Royal Society, Interface · 2025Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?PLoS biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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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.