[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124447-en":3,"doc-seo-124447-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124447,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Applications of artificial intelligence and machine learning in dynamic pathway engineering - Review article","Dynamic pathway engineering builds metabolic production systems with intracellular feedback control to adjust the temporal activity of heterologous enzymes when perturbations occur, typically through biosensors and feedback circuits. Pathway design is limited by the breadth of biological parts, circuit architectures, and calibration requirements, making experimental exploration expensive. This review summarizes how artificial intelligence and machine learning can accelerate design by extracting hidden patterns from complex data, enabling rapid screening of candidate pathways and components. It highlights recent advances and discusses future directions for integrating AI into metabolic engineering pipelines to improve production of high-value chemicals.","Edinburgh Research Explorer  \nApplications of artificial intelligence and machine learning in dynamic pathway engineering  \nCitation for published version:  \nMerzbacher, C & Oyarzún, DA 2023, 'Applications of artificial intelligence and machine learning in dynamic pathway engineering', Biochemical Society Transactions, vol. 51, no. 5, pp. 1871-1879.  \n[https://doi.org/10.1042/BST20221542](https://doi.org/10.1042/BST20221542)  \nDigital Object Identifier (DOI):  \n10.1042/BST20221542  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nBiochemical Society Transactions  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 03. Aug. 2026  \nBiochemical Society Transactions (2023) 51 1871–1879 [https://doi.org/10.1042/BST20221542](https://doi.org/10.1042/BST20221542)  \nReview Article  \nApplications of artificial intelligence and machine learning in dynamic pathway engineering  \nCharlotte Merzbacher1 and  Diego A. Oyarzún1,2,3  \n1School of Informatics, University of Edinburgh, Edinburgh, U. K. ; 2The Alan Turing Institute, London, U. K. ; 3School of Biological Sciences, University of Edinburgh, Edinburgh, U. K. Correspondence: Diego A. Oyarzún ([d.oyarzun@ed.ac.uk](d.oyarzun@ed.ac.uk))  \nReceived: 24 May 2023  \nRevised: 7 August 2023  \nAccepted: 21 August 2023  \nVersion of Record published:  \n1 September 2023  \nDynamic 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.  \nIntroduction  \nA key aim in metabolic engineering is the production of high-value chemicals using the metabolic machinery of microorganisms [1,2] . In a typical metabolic engineering pipeline, microbial strains are transformed with enzymatic genes that convert native precursors of the host into target products. However, production is typically limited by multiple factors such as pathway sensitivity to fermentation conditions, accumulation of toxic intermediates, and difficulties in scaling up production. To overcome these challenges, last decade has witnessed the b","cbCaitLVWkMFMClx","https://ap.wps.com/l/cbCaitLVWkMFMClx","pdf",1515716,1,10,"English","en",105,"# Introduction\n## Dynamic pathway engineering and feedback control\n## Design challenges and computational approaches\n# AI and machine learning for dynamic pathway design\n## Applications to pathway and component design","[{\"question\":\"What is dynamic pathway engineering and what problem does it address?\",\"answer\":\"Dynamic pathway engineering equips production strains with built-in feedback control systems so pathway enzyme expression adapts over time to cellular or bioreactor changes. This improves robustness and reduces issues such as toxic intermediate accumulation and gene expression burden.\"},{\"question\":\"Why is designing dynamic pathways costly using experimentation alone?\",\"answer\":\"Dynamic pathways require assembling many molecular components into appropriate circuit architectures and then calibrating each component’s function. This creates a large design space that is expensive and slow to explore solely through experiments.\"},{\"question\":\"How can AI and machine learning help in the dynamic pathway design cycle?\",\"answer\":\"AI/ML methods can learn hidden patterns in complex datasets and rapidly screen through large collections of designs. This can streamline design by supporting modeling, simulation, and component/pathway selection within metabolic engineering pipelines.\"}]","Applications of artificial intelligence and machine learning in dynamic pathway engineering - Review article | PDF",1785822336,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"applications-of-artificial-intelligence-and-machine-learning-in-dynamic-pathway-engineering-review-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applications-of-artificial-intelligence-and-machine-learning-in-dynamic-pathway-engineering-review-article/124447/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is dynamic pathway engineering and what problem does it address?","Question",{"text":75,"@type":76},"Dynamic pathway engineering equips production strains with built-in feedback control systems so pathway enzyme expression adapts over time to cellular or bioreactor changes. This improves robustness and reduces issues such as toxic intermediate accumulation and gene expression burden.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is designing dynamic pathways costly using experimentation alone?",{"text":80,"@type":76},"Dynamic pathways require assembling many molecular components into appropriate circuit architectures and then calibrating each component’s function. This creates a large design space that is expensive and slow to explore solely through experiments.",{"name":82,"@type":73,"acceptedAnswer":83},"How can AI and machine learning help in the dynamic pathway design cycle?",{"text":84,"@type":76},"AI/ML methods can learn hidden patterns in complex datasets and rapidly screen through large collections of designs. 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