Pub Date : 2025-11-21DOI: 10.1016/j.copbio.2025.103384
Amogh K Baranwal, Sebastian J Maerkl
Cell-free systems have emerged as a powerful platform for protein production, characterization, and bottom-up construction of artificial cells, offering direct control over biochemical environments. However, achieving high-throughput and iterative design–build–test cycles requires advanced strategies beyond conventional methods. Microfluidic technologies address these challenges by enabling miniaturization, automation, and exceptional control over reaction conditions. The integration of cell-free systems with microfluidics has unlocked new capabilities by enabling high-throughput assays, long-lived reactions in continuous-flow systems, and the generation of liposome-based artificial cells. This review highlights recent advances at this interface, focusing on microfluidic strategies for protein characterization, gene regulatory studies, and the bottom-up construction of artificial cells exhibiting life-like functions.
{"title":"Microfluidics meets cell-free systems: from molecular engineering to synthetic cells","authors":"Amogh K Baranwal, Sebastian J Maerkl","doi":"10.1016/j.copbio.2025.103384","DOIUrl":"10.1016/j.copbio.2025.103384","url":null,"abstract":"<div><div>Cell-free systems have emerged as a powerful platform for protein production, characterization, and bottom-up construction of artificial cells, offering direct control over biochemical environments. However, achieving high-throughput and iterative design–build–test cycles requires advanced strategies beyond conventional methods. Microfluidic technologies address these challenges by enabling miniaturization, automation, and exceptional control over reaction conditions. The integration of cell-free systems with microfluidics has unlocked new capabilities by enabling high-throughput assays, long-lived reactions in continuous-flow systems, and the generation of liposome-based artificial cells. This review highlights recent advances at this interface, focusing on microfluidic strategies for protein characterization, gene regulatory studies, and the bottom-up construction of artificial cells exhibiting life-like functions.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103384"},"PeriodicalIF":7.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.copbio.2025.103388
Lena M Hümmler , Stefan Hristov , Lucas Hille , Elad Noor , Steffen N Lindner
Growth-coupled bioproduction (GCBP) is a metabolic engineering approach that introduces an obligatory dependency between metabolic activity and production, ensuring a high minimal yield and genetic stability. Here, product yield is primarily limited by pathway constraints, which can be overcome by further metabolic engineering. However, misconceptions persist in understanding GCBP among metabolic and process engineers. Contrary to misperceptions, yield is not stoichiometrically constrained by the growth-restoring enzymatic step, cell division is nonessential for production, and GCBP is compatible with multistage fermentation processes. Finally, GCBP addresses reduced production arising from genetic drift caused by population heterogeneity. Still, challenges remain: the lack of metabolic engineering tools for nonmodel organisms, limited in silico design capabilities, and the existence of uncharacterized metabolism. Nevertheless, by setting a high minimal stoichiometric yield, GCBP facilitates continuous bioproduction. Overall, integrating GCBP with metabolic engineering and improved computational design has the potential to reshape industrial biotechnology toward robust and efficient bioproduction.
{"title":"Resolving misconceptions and constraints in growth-coupled bioproduction","authors":"Lena M Hümmler , Stefan Hristov , Lucas Hille , Elad Noor , Steffen N Lindner","doi":"10.1016/j.copbio.2025.103388","DOIUrl":"10.1016/j.copbio.2025.103388","url":null,"abstract":"<div><div>Growth-coupled bioproduction (GCBP) is a metabolic engineering approach that introduces an obligatory dependency between metabolic activity and production, ensuring a high minimal yield and genetic stability. Here, product yield is primarily limited by pathway constraints, which can be overcome by further metabolic engineering. However, misconceptions persist in understanding GCBP among metabolic and process engineers. Contrary to misperceptions, yield is not stoichiometrically constrained by the growth-restoring enzymatic step, cell division is nonessential for production, and GCBP is compatible with multistage fermentation processes. Finally, GCBP addresses reduced production arising from genetic drift caused by population heterogeneity. Still, challenges remain: the lack of metabolic engineering tools for nonmodel organisms, limited <em>in silico</em> design capabilities, and the existence of uncharacterized metabolism. Nevertheless, by setting a high minimal stoichiometric yield, GCBP facilitates continuous bioproduction. Overall, integrating GCBP with metabolic engineering and improved computational design has the potential to reshape industrial biotechnology toward robust and efficient bioproduction.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103388"},"PeriodicalIF":7.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1016/j.copbio.2025.103385
Jiho Seok, Mark P Styczynski
Metabolic engineering employs microbial cell factories to produce high-value products from low-cost feedstocks. Designing, optimizing, and evaluating biosynthetic pathways in microbial cell factories is essential, yet these processes remain time- and labor-intensive. Biosensors help metabolic engineers address this challenge by converting target metabolite concentrations into observable outputs, enabling efficient assessment of microbial production. Whole-cell biosensors, which operate within living microorganisms, and cell-free biosensors, which function independently of cell growth using transcription–translation machinery, have contributed to microbial biosynthesis optimization through distinct approaches. This review summarizes recent advances in biosensor-driven metabolic engineering facilitated by whole-cell and cell-free biosensors.
{"title":"Whole-cell and cell-free biosensor-driven metabolic engineering","authors":"Jiho Seok, Mark P Styczynski","doi":"10.1016/j.copbio.2025.103385","DOIUrl":"10.1016/j.copbio.2025.103385","url":null,"abstract":"<div><div>Metabolic engineering employs microbial cell factories to produce high-value products from low-cost feedstocks. Designing, optimizing, and evaluating biosynthetic pathways in microbial cell factories is essential, yet these processes remain time- and labor-intensive. Biosensors help metabolic engineers address this challenge by converting target metabolite concentrations into observable outputs, enabling efficient assessment of microbial production. Whole-cell biosensors, which operate within living microorganisms, and cell-free biosensors, which function independently of cell growth using transcription–translation machinery, have contributed to microbial biosynthesis optimization through distinct approaches. This review summarizes recent advances in biosensor-driven metabolic engineering facilitated by whole-cell and cell-free biosensors<strong>.</strong></div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103385"},"PeriodicalIF":7.0,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145556437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1016/j.copbio.2025.103387
Yi Yu , Xiaoying Fu , Jinmiao Hu , Jens Nielsen , Shuobo Shi
The sustainable production of chemicals from renewable, nonedible biomass has become crucial to address environmental challenges like climate change and resource depletion caused by fossil resource dependence. Saccharomyces cerevisiae has emerged as a versatile microbial chassis for industrial bioproduction of chemicals, with engineered breakthroughs in central carbon metabolism, lipid metabolism, and terpenoid metabolism. This review examines three transformative paradigms: (1) optimizing metabolic flux and redirecting yeast pathways for chemical biosynthesis (e.g. farnesene), (2) enhancing yeast robustness to improve biomass and biochemical production under fermentation stresses (e.g. succinic acid), and (3) expanding feedstock flexibility through engineered substrate assimilation (e.g. ethanol). These examples pave the way for producing sustainable chemicals. We also discuss future challenges and propose AI (Artificial Intelligence)-driven design tools, CRISPR-based genome editing, and integrated biological-chemical hybrid processes as next-generation solutions to advance a yeast-mediated circular bioeconomy.
{"title":"Engineering metabolism of Saccharomyces cerevisiae for production of chemicals","authors":"Yi Yu , Xiaoying Fu , Jinmiao Hu , Jens Nielsen , Shuobo Shi","doi":"10.1016/j.copbio.2025.103387","DOIUrl":"10.1016/j.copbio.2025.103387","url":null,"abstract":"<div><div>The sustainable production of chemicals from renewable, nonedible biomass has become crucial to address environmental challenges like climate change and resource depletion caused by fossil resource dependence. <em>Saccharomyces cerevisiae</em> has emerged as a versatile microbial chassis for industrial bioproduction of chemicals, with engineered breakthroughs in central carbon metabolism, lipid metabolism, and terpenoid metabolism. This review examines three transformative paradigms: (1) optimizing metabolic flux and redirecting yeast pathways for chemical biosynthesis (e.g. farnesene), (2) enhancing yeast robustness to improve biomass and biochemical production under fermentation stresses (e.g. succinic acid), and (3) expanding feedstock flexibility through engineered substrate assimilation (e.g. ethanol). These examples pave the way for producing sustainable chemicals. We also discuss future challenges and propose AI (Artificial Intelligence)-driven design tools, CRISPR-based genome editing, and integrated biological-chemical hybrid processes as next-generation solutions to advance a yeast-mediated circular bioeconomy.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103387"},"PeriodicalIF":7.0,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145556428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-15DOI: 10.1016/j.copbio.2025.103390
Kate Collins , Claire E Stanley , Thomas E Ouldridge
The capacity to pattern biomolecules within microfluidic devices expands the scope of microfluidic technologies. In such patterned systems, surface-bound components remain localized, while the microfluidic network supplies reagents and removes waste products. This approach has enabled continuous protein expression from patterned DNA, chemical synthesis from immobilized enzymes, and cell capture assays. Here, we review methods to pattern surfaces within microfluidic devices. Patterns may be printed before or after the device is assembled; pre-bonding methods are compatible with well-established open-surface patterning protocols but present challenges for device bonding and alignment. Conversely, post-bonding methods are compatible with standard bonding procedures but rely on less established, sequential patterning protocols. Future progress will require consistent reporting of pattern signal and noise relative to controls.
{"title":"Biochemical surface patterning in microfluidic devices","authors":"Kate Collins , Claire E Stanley , Thomas E Ouldridge","doi":"10.1016/j.copbio.2025.103390","DOIUrl":"10.1016/j.copbio.2025.103390","url":null,"abstract":"<div><div>The capacity to pattern biomolecules within microfluidic devices expands the scope of microfluidic technologies. In such patterned systems, surface-bound components remain localized, while the microfluidic network supplies reagents and removes waste products. This approach has enabled continuous protein expression from patterned DNA, chemical synthesis from immobilized enzymes, and cell capture assays. Here, we review methods to pattern surfaces within microfluidic devices. Patterns may be printed before or after the device is assembled; pre-bonding methods are compatible with well-established open-surface patterning protocols but present challenges for device bonding and alignment. Conversely, post-bonding methods are compatible with standard bonding procedures but rely on less established, sequential patterning protocols. Future progress will require consistent reporting of pattern signal and noise relative to controls.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103390"},"PeriodicalIF":7.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.copbio.2025.103389
Aukse Gaizauskaite , Emma E. Crean , Imre Banlaki , Jan L. Kalkowski , Henrike Niederholtmeyer
Performing cell-free expression (CFE) in tailored microfluidic environments is a powerful tool to investigate the organisation of biosystems from molecular to multicellular scales. While cell-free transcription–translation systems simplify and open up cellular biochemistry for manipulation, microfluidics enables miniaturisation and precise control over geometries and reaction conditions. In this review, we highlight the benefits of combining microfluidics with CFE reactions for the study and engineering of molecular functions and the construction of life-like systems from nonliving components. By defining spatial organisation at different scales and sustaining nonequilibrium conditions, microfluidic environments play a key role in the quest to ‘boot up’ the biochemistry of life.
{"title":"Controlled protein synthesis and spatial organisation in microfluidic environments","authors":"Aukse Gaizauskaite , Emma E. Crean , Imre Banlaki , Jan L. Kalkowski , Henrike Niederholtmeyer","doi":"10.1016/j.copbio.2025.103389","DOIUrl":"10.1016/j.copbio.2025.103389","url":null,"abstract":"<div><div>Performing cell-free expression (CFE) in tailored microfluidic environments is a powerful tool to investigate the organisation of biosystems from molecular to multicellular scales. While cell-free transcription–translation systems simplify and open up cellular biochemistry for manipulation, microfluidics enables miniaturisation and precise control over geometries and reaction conditions. In this review, we highlight the benefits of combining microfluidics with CFE reactions for the study and engineering of molecular functions and the construction of life-like systems from nonliving components. By defining spatial organisation at different scales and sustaining nonequilibrium conditions, microfluidic environments play a key role in the quest to ‘boot up’ the biochemistry of life.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103389"},"PeriodicalIF":7.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.copbio.2025.103382
Wolfgang Wiechert , Laura M Helleckes , Katharina Nöh
Uncertainty quantification (UQ) is central to data analytics, particularly in the life sciences, where experiments are often affected by significant measurement noise. In emerging automated high-throughput biolabs, such as biofoundries, parallel cultivation systems, and smart analytics platforms, UQ should be a built-in feature rather than an optional add-on. These environments pose a unique challenge: robotic liquid handling must be combined with miniaturized biochemical analytics (including omics), process monitoring, online data analytics, and digital control. Although traditional UQ methods from classical and computational statistics remain valid and applicable, integrating them into highly parallelized experimental and digital workflows presents new challenges. These include data preprocessing, model-based data integration, decision-making, and experimental control. In this review, we examine the emerging demands on UQ in automated experimentation and survey recent frameworks, strategies, and computational tools designed to address them.
{"title":"Building trust in automated experimentation: uncertainty quantification in the era of high-throughput biolabs","authors":"Wolfgang Wiechert , Laura M Helleckes , Katharina Nöh","doi":"10.1016/j.copbio.2025.103382","DOIUrl":"10.1016/j.copbio.2025.103382","url":null,"abstract":"<div><div>Uncertainty quantification (UQ) is central to data analytics, particularly in the life sciences, where experiments are often affected by significant measurement noise. In emerging automated high-throughput biolabs, such as biofoundries, parallel cultivation systems, and smart analytics platforms, UQ should be a built-in feature rather than an optional add-on. These environments pose a unique challenge: robotic liquid handling must be combined with miniaturized biochemical analytics (including omics), process monitoring, online data analytics, and digital control. Although traditional UQ methods from classical and computational statistics remain valid and applicable, integrating them into highly parallelized experimental and digital workflows presents new challenges. These include data preprocessing, model-based data integration, decision-making, and experimental control. In this review, we examine the emerging demands on UQ in automated experimentation and survey recent frameworks, strategies, and computational tools designed to address them.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103382"},"PeriodicalIF":7.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.copbio.2025.103386
Ji Hun Kim , Jae Heon Kim , Seong Do Kim , Haseong Kim , Byung-Kwan Cho
Biofoundries are transforming synthetic biology, with the DNA assembly workflow being critical for successful biofoundry operation. This review examines recent advances in automated DNA assembly strategies for biofoundry, focusing on three key perspectives. First, we discuss emerging platforms ranging from high-throughput and highly efficient systems to affordable and accessible solutions. Second, we explore how standardized design tools enable seamless interoperability across diverse biofoundries, facilitating protocol sharing and reproducibility. Third, we analyze the integration of machine learning into assembly workflows, in which AI-driven systems dynamically optimize protocols, diagnose failures, and close the DBTL loop through real-time learning. These convergent advances are establishing a new paradigm in which experiments continuously improve through iteration, promising to accelerate both fundamental research and industrial applications.
{"title":"Recent advances in AI-enabled automation of DNA assembly in biofoundries","authors":"Ji Hun Kim , Jae Heon Kim , Seong Do Kim , Haseong Kim , Byung-Kwan Cho","doi":"10.1016/j.copbio.2025.103386","DOIUrl":"10.1016/j.copbio.2025.103386","url":null,"abstract":"<div><div>Biofoundries are transforming synthetic biology, with the DNA assembly workflow being critical for successful biofoundry operation. This review examines recent advances in automated DNA assembly strategies for biofoundry, focusing on three key perspectives. First, we discuss emerging platforms ranging from high-throughput and highly efficient systems to affordable and accessible solutions. Second, we explore how standardized design tools enable seamless interoperability across diverse biofoundries, facilitating protocol sharing and reproducibility. Third, we analyze the integration of machine learning into assembly workflows, in which AI-driven systems dynamically optimize protocols, diagnose failures, and close the DBTL loop through real-time learning. These convergent advances are establishing a new paradigm in which experiments continuously improve through iteration, promising to accelerate both fundamental research and industrial applications.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103386"},"PeriodicalIF":7.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.copbio.2025.103381
Dominic Kösters , Jan Marienhagen
Novel approaches to genome engineering are crucial to rapidly advance the capabilities of strain engineering and synthetic biology. With ongoing developments in DNA editing techniques, researchers have begun to engineer organisms at higher throughput and can now perform multiple genome modifications simultaneously. As laboratory automation becomes more accessible, workflows are being transferred to robot-assisted platforms, enabling large-scale and highly parallelized genome editing campaigns. These platforms play a key role in fully utilizing the potential of modern molecular biology tools. Here, we review recent developments in technologies for high-throughput, multiplexed, and automated strain engineering in prokaryotic and eukaryotic organisms.
{"title":"Molecular methods for high-throughput, multiplexed, and automated genome editing in prokaryotes and eukaryotes","authors":"Dominic Kösters , Jan Marienhagen","doi":"10.1016/j.copbio.2025.103381","DOIUrl":"10.1016/j.copbio.2025.103381","url":null,"abstract":"<div><div>Novel approaches to genome engineering are crucial to rapidly advance the capabilities of strain engineering and synthetic biology. With ongoing developments in DNA editing techniques, researchers have begun to engineer organisms at higher throughput and can now perform multiple genome modifications simultaneously. As laboratory automation becomes more accessible, workflows are being transferred to robot-assisted platforms, enabling large-scale and highly parallelized genome editing campaigns. These platforms play a key role in fully utilizing the potential of modern molecular biology tools. Here, we review recent developments in technologies for high-throughput, multiplexed, and automated strain engineering in prokaryotic and eukaryotic organisms.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103381"},"PeriodicalIF":7.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.copbio.2025.103378
Felix M Hubert , Anna C Love , Tian Lan , Hannah K Bone , Bradley S Moore
Cannabis sativa and related plants are recognized for their psychoactive and therapeutic effects due to the unique molecules they produce, including phytocannabinoids. Beyond the major phytocannabinoids Δ⁹-tetrahydrocannabinol and cannabidiol, research on ‘minor cannabinoids’ has revealed new biological properties, motivating alternative production methods for these low-abundance molecules. Advances in asymmetric synthetic methods have enabled access to structurally complex cannabinoids and analogs. In tandem, synthetic biology approaches, including heterologous pathway reconstruction and functionally analogous enzyme discovery, have expanded the biosynthetic toolkit, delivering rare and novel cannabinoids at increasing scale. Here, we summarize the latest insights into cannabinoid pharmacology, synthetic chemistry innovations, and engineered production strategies, underscoring how feedback across disciplines is reshaping access to and understanding of minor cannabinoids.
{"title":"Recent innovations in cannabinoid chemistry, biology, and biosynthesis","authors":"Felix M Hubert , Anna C Love , Tian Lan , Hannah K Bone , Bradley S Moore","doi":"10.1016/j.copbio.2025.103378","DOIUrl":"10.1016/j.copbio.2025.103378","url":null,"abstract":"<div><div><em>Cannabis sativa</em> and related plants are recognized for their psychoactive and therapeutic effects due to the unique molecules they produce, including phytocannabinoids. Beyond the major phytocannabinoids Δ⁹-tetrahydrocannabinol and cannabidiol, research on ‘minor cannabinoids’ has revealed new biological properties, motivating alternative production methods for these low-abundance molecules. Advances in asymmetric synthetic methods have enabled access to structurally complex cannabinoids and analogs. In tandem, synthetic biology approaches, including heterologous pathway reconstruction and functionally analogous enzyme discovery, have expanded the biosynthetic toolkit, delivering rare and novel cannabinoids at increasing scale. Here, we summarize the latest insights into cannabinoid pharmacology, synthetic chemistry innovations, and engineered production strategies, underscoring how feedback across disciplines is reshaping access to and understanding of minor cannabinoids.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103378"},"PeriodicalIF":7.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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