Pub Date : 2025-12-09DOI: 10.1016/j.copbio.2025.103398
Ryan Palaganas , Dimitrios N Sidiropoulos , Meaghan Morris , Genevieve L Stein-O’Brien
Neurodegenerative diseases (NDDs) are a global health crisis affecting 15% of the population, with projections indicating this burden will double within two decades. Despite having shared molecular hallmarks, co-occurring proteinopathies, predictable progression patterns, and overlapping genetic risk variants, what causes specific NDDs to manifest in a particular individual or even a particular cell type is unknown. Thus, systems biology approaches are necessary to decipher the molecular, cellular, and environmental interactions driving pathogenesis. Multi-omics spatial profiling preserves tissue architecture while mapping cellular phenotypes and molecular interactions at subcellular resolution. Systems biology integration of these modalities will facilitate the identification of underlying causes of neuronal vulnerability, protein aggregation mechanisms, and disease progression patterns, accelerating targeted therapeutic development across NDDs.
{"title":"Mapping multipathology via spatial omic integration","authors":"Ryan Palaganas , Dimitrios N Sidiropoulos , Meaghan Morris , Genevieve L Stein-O’Brien","doi":"10.1016/j.copbio.2025.103398","DOIUrl":"10.1016/j.copbio.2025.103398","url":null,"abstract":"<div><div>Neurodegenerative diseases (NDDs) are a global health crisis affecting 15% of the population, with projections indicating this burden will double within two decades. Despite having shared molecular hallmarks, co-occurring proteinopathies, predictable progression patterns, and overlapping genetic risk variants, what causes specific NDDs to manifest in a particular individual or even a particular cell type is unknown. Thus, systems biology approaches are necessary to decipher the molecular, cellular, and environmental interactions driving pathogenesis. Multi-omics spatial profiling preserves tissue architecture while mapping cellular phenotypes and molecular interactions at subcellular resolution. Systems biology integration of these modalities will facilitate the identification of underlying causes of neuronal vulnerability, protein aggregation mechanisms, and disease progression patterns, accelerating targeted therapeutic development across NDDs.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103398"},"PeriodicalIF":7.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145721573","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 : 2025-12-09DOI: 10.1016/j.copbio.2025.103400
Bin Wang , Yuan Lu
Molecular machines convert physical and chemical energy into effective mechanical work, and have made significant progress as tools capable of micro- and nano-scale smart manipulation in fields such as medicine, catalysis, and materials. However, functional limitations hinder the rapid iteration of molecular machines toward practical applications. This requires not only the individual design of molecular machines to achieve more efficient performance in diverse systems but also the cooperation of molecular machines through a swarm to integrate spatial and temporal aspects, thereby scaling up molecular-scale deformations to macroscopic work. In this review, we discuss the latest achievements in individual and swarm design of molecular machines, address existing challenges, and provide insights into the future development of these machines.
{"title":"Molecular machines from individuals to swarms","authors":"Bin Wang , Yuan Lu","doi":"10.1016/j.copbio.2025.103400","DOIUrl":"10.1016/j.copbio.2025.103400","url":null,"abstract":"<div><div>Molecular machines convert physical and chemical energy into effective mechanical work, and have made significant progress as tools capable of micro- and nano-scale smart manipulation in fields such as medicine, catalysis, and materials. However, functional limitations hinder the rapid iteration of molecular machines toward practical applications. This requires not only the individual design of molecular machines to achieve more efficient performance in diverse systems but also the cooperation of molecular machines through a swarm to integrate spatial and temporal aspects, thereby scaling up molecular-scale deformations to macroscopic work. In this review, we discuss the latest achievements in individual and swarm design of molecular machines, address existing challenges, and provide insights into the future development of these machines.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103400"},"PeriodicalIF":7.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145721515","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 : 2025-12-04DOI: 10.1016/j.copbio.2025.103393
Steffen Docter , Benoit David , Holger Gohlke
Efficient enzymes and microbial factories are essential to promote the transition toward a sustainable bioeconomy. This review focuses on the progress of artificial intelligence (AI) methods in accelerating the development of optimized biocatalysts and genetic networks in cells. Recent advances in AI in the field of enzyme discovery, engineering, and de novo design are discussed. Additionally, we highlight examples of successful applications of AI in optimizing different components in cells, from gene expression regulation to metabolic pathway optimization and design. Finally, this review emphasizes the challenges limiting the reliability and generalizability of current AI methods.
{"title":"Deep learning and generative artificial intelligence methods in enzyme and cell engineering","authors":"Steffen Docter , Benoit David , Holger Gohlke","doi":"10.1016/j.copbio.2025.103393","DOIUrl":"10.1016/j.copbio.2025.103393","url":null,"abstract":"<div><div>Efficient enzymes and microbial factories are essential to promote the transition toward a sustainable bioeconomy. This review focuses on the progress of artificial intelligence (AI) methods in accelerating the development of optimized biocatalysts and genetic networks in cells. Recent advances in AI in the field of enzyme discovery, engineering, and <em>de novo</em> design are discussed. Additionally, we highlight examples of successful applications of AI in optimizing different components in cells, from gene expression regulation to metabolic pathway optimization and design. Finally, this review emphasizes the challenges limiting the reliability and generalizability of current AI methods.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103393"},"PeriodicalIF":7.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676701","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 : 2025-12-04DOI: 10.1016/j.copbio.2025.103399
Bastian Nießing , Rebekka Wagner , Laura Herbst , Robert H Schmitt
Automated genome editing of stem cells represents a great advancement in the fields of disease modeling and regenerative medicine. This review evaluates the current tools and methodologies for implementing automated genome editing for induced pluripotent stem cells. The increasing demand for precise genome editing technologies, driven by the growing gene editing market, necessitates efficient and scalable solutions to overcome the complexities and costs associated with traditional editing methods. A comprehensive overview of various automation strategies is provided, focusing on key workflows that encompass stem cell expansion, genetic material delivery through viral vectors, lipid nanoparticles, and electroporation, as well as monoclonal expansion techniques. Advances in automation not only enhance editing efficiency and reduce labor intensity but also ensure quality and reproducibility in stem cell research. As the field progresses, the integration of artificial intelligence and the shift toward closed, good manufacturing practice-compliant systems are anticipated to further streamline automated genome editing processes.
{"title":"Tools for automated genome editing of stem cells","authors":"Bastian Nießing , Rebekka Wagner , Laura Herbst , Robert H Schmitt","doi":"10.1016/j.copbio.2025.103399","DOIUrl":"10.1016/j.copbio.2025.103399","url":null,"abstract":"<div><div>Automated genome editing of stem cells represents a great advancement in the fields of disease modeling and regenerative medicine. This review evaluates the current tools and methodologies for implementing automated genome editing for induced pluripotent stem cells. The increasing demand for precise genome editing technologies, driven by the growing gene editing market, necessitates efficient and scalable solutions to overcome the complexities and costs associated with traditional editing methods. A comprehensive overview of various automation strategies is provided, focusing on key workflows that encompass stem cell expansion, genetic material delivery through viral vectors, lipid nanoparticles, and electroporation, as well as monoclonal expansion techniques. Advances in automation not only enhance editing efficiency and reduce labor intensity but also ensure quality and reproducibility in stem cell research. As the field progresses, the integration of artificial intelligence and the shift toward closed, good manufacturing practice-compliant systems are anticipated to further streamline automated genome editing processes.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103399"},"PeriodicalIF":7.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682220","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 : 2025-12-04DOI: 10.1016/j.copbio.2025.103396
Križan Jurinović, Merry Mitra, Rakesh Mukherjee, Thomas E Ouldridge
DNA strand displacement (SD) reactions are central to the operation of many synthetic nucleic acid systems, including molecular circuits, sensors, and machines. Over the years, a broad set of design frameworks has emerged to accommodate various functional goals, initial configurations, and environmental conditions. Nevertheless, key challenges persist, particularly in reliably predicting reaction kinetics. In contrast to reviews centred on network-level architectures, this article focuses on the design and analysis of individual SD reactions, highlighting kinetic mechanisms, structural determinants, and the current limits of predictive modelling. We identify promising innovations while analysing the factors that continue to hinder predictive accuracy. We conclude by outlining future directions for achieving more robust and programmable behaviour in DNA-based systems.
{"title":"Design of DNA strand displacement reactions","authors":"Križan Jurinović, Merry Mitra, Rakesh Mukherjee, Thomas E Ouldridge","doi":"10.1016/j.copbio.2025.103396","DOIUrl":"10.1016/j.copbio.2025.103396","url":null,"abstract":"<div><div>DNA strand displacement (SD) reactions are central to the operation of many synthetic nucleic acid systems, including molecular circuits, sensors, and machines. Over the years, a broad set of design frameworks has emerged to accommodate various functional goals, initial configurations, and environmental conditions. Nevertheless, key challenges persist, particularly in reliably predicting reaction kinetics. In contrast to reviews centred on network-level architectures, this article focuses on the design and analysis of individual SD reactions, highlighting kinetic mechanisms, structural determinants, and the current limits of predictive modelling. We identify promising innovations while analysing the factors that continue to hinder predictive accuracy. We conclude by outlining future directions for achieving more robust and programmable behaviour in DNA-based systems.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103396"},"PeriodicalIF":7.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682219","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 : 2025-12-03DOI: 10.1016/j.copbio.2025.103397
Yifan Cui , Mark W Tibbitt , Timothy K Lu , Tzu-Chieh Tang
Engineered living materials (ELMs) combine living cells, typically microorganisms, such as bacteria, yeasts, or filamentous fungi, with structural carrier matrices to form systems capable of sensing, growth, and self-repair. Most current designs emphasize programming the microbes to render otherwise static materials functional. A less explored dimension is leveraging reciprocal microbial–material interactions themselves to engineer adaptive and evolving living materials as integrated systems. Achieving such dynamic behavior requires understanding how support matrices influence microbial behavior and how cells, in turn, reshape material properties over time. This review outlines key modes of cell–material interactions as a framework for expanding the functional toolbox of ELMs and for creating sustainable and programmable materials that respond to their environments and evolve.
{"title":"Design principles for adaptive and evolving engineered living materials","authors":"Yifan Cui , Mark W Tibbitt , Timothy K Lu , Tzu-Chieh Tang","doi":"10.1016/j.copbio.2025.103397","DOIUrl":"10.1016/j.copbio.2025.103397","url":null,"abstract":"<div><div>Engineered living materials (ELMs) combine living cells, typically microorganisms, such as bacteria, yeasts, or filamentous fungi, with structural carrier matrices to form systems capable of sensing, growth, and self-repair. Most current designs emphasize programming the microbes to render otherwise static materials functional. A less explored dimension is leveraging reciprocal microbial–material interactions themselves to engineer adaptive and evolving living materials as integrated systems. Achieving such dynamic behavior requires understanding how support matrices influence microbial behavior and how cells, in turn, reshape material properties over time. This review outlines key modes of cell–material interactions as a framework for expanding the functional toolbox of ELMs and for creating sustainable and programmable materials that respond to their environments and evolve.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103397"},"PeriodicalIF":7.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676720","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 : 2025-11-29DOI: 10.1016/j.copbio.2025.103394
Ryogo Higashi , Izumi Shibayama , Jiajue Ji , Tao Ren , Iiro Kiiski , Nanami Takeuchi , Ryuji Kawano
Glass capillary nanopores have been widely used for resistive pulse sensing and ionic current rectification-based nanopore sensing due to their excellent mechanical properties, simple fabrication, easy surface modification, and low production cost. These nanopores have demonstrated great potential in detecting diverse biomarkers, including nucleic acids, proteins, cell vesicles, viruses, and bacteria. Here, we highlight recent advances in molecular diagnostics using glass capillary nanopores, especially focusing on the detection of infectious disease pathogens and cancer-associated microRNAs. By integrating precisely sized glass nanopipette-based nanopores with nucleic acid amplification techniques and well-designed probes, this sensing platform has emerged as a promising method for liquid biopsy from patient body fluids. In consideration of recent research on molecular diagnostics and the difficulties with glass capillary nanopipettes, we also discuss the future direction of molecular diagnostics using this tool.
{"title":"Molecular diagnostics for infectious disease and cancer based on glass capillary nanopore sensing","authors":"Ryogo Higashi , Izumi Shibayama , Jiajue Ji , Tao Ren , Iiro Kiiski , Nanami Takeuchi , Ryuji Kawano","doi":"10.1016/j.copbio.2025.103394","DOIUrl":"10.1016/j.copbio.2025.103394","url":null,"abstract":"<div><div>Glass capillary nanopores have been widely used for resistive pulse sensing and ionic current rectification-based nanopore sensing due to their excellent mechanical properties, simple fabrication, easy surface modification, and low production cost. These nanopores have demonstrated great potential in detecting diverse biomarkers, including nucleic acids, proteins, cell vesicles, viruses, and bacteria. Here, we highlight recent advances in molecular diagnostics using glass capillary nanopores, especially focusing on the detection of infectious disease pathogens and cancer-associated microRNAs. By integrating precisely sized glass nanopipette-based nanopores with nucleic acid amplification techniques and well-designed probes, this sensing platform has emerged as a promising method for liquid biopsy from patient body fluids. In consideration of recent research on molecular diagnostics and the difficulties with glass capillary nanopipettes, we also discuss the future direction of molecular diagnostics using this tool.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103394"},"PeriodicalIF":7.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145647698","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 : 2025-11-28DOI: 10.1016/j.copbio.2025.103395
Konstantin FG Weigmann, Uwe T Bornscheuer, Mark Doerr
Protein engineering aims to enhance enzymatic properties such as activity, selectivity, stability, and solvent tolerance by restructuring protein frameworks beyond natural performance limits. This process relies on the iterative Design-Build-Test-Learn cycle, where experimental feedback guides progressive improvements. Advancements in artificial intelligence have transformed both the Design and Learn phases, with zero-shot protein language models predicting beneficial mutations directly from sequence data and supervised models integrating assay results to refine subsequent variant designs. These approaches reduce the dependence on structural insights while enabling the discovery of synergistic effects across mutations. Automation technologies, including robotic liquid handlers and integrated platforms, have become central to modern protein engineering by reducing errors, ensuring reproducibility, and enabling large-scale variant screening. Emerging autonomous platforms demonstrate closed-loop optimization that couples protein library design, automated plasmid transformation/protein expression and corresponding assays, and machine learning–driven decision-making. These systems achieve significant accelerations in the research process, reducing multi-round engineering cycles from months to days while successfully improving diverse proteins/enzymes to a targeted objective. Beyond single-lab platforms, orchestration frameworks adhering to FAIR data principles and leveraging knowledge graphs promise distributed ‘self-driving’ laboratories capable of coordinating workflows across facilities. While high setup costs and proprietary systems remain challenging, open-source and modular alternatives highlight a path toward transparent, flexible automation. Collectively, these innovations are redefining protein engineering as an increasingly autonomous, data-driven discipline.
{"title":"Advances and critical evaluation of autonomous protein engineering: towards transparent, accessible, and reproducible platforms","authors":"Konstantin FG Weigmann, Uwe T Bornscheuer, Mark Doerr","doi":"10.1016/j.copbio.2025.103395","DOIUrl":"10.1016/j.copbio.2025.103395","url":null,"abstract":"<div><div>Protein engineering aims to enhance enzymatic properties such as activity, selectivity, stability, and solvent tolerance by restructuring protein frameworks beyond natural performance limits. This process relies on the iterative Design-Build-Test-Learn cycle, where experimental feedback guides progressive improvements. Advancements in artificial intelligence have transformed both the Design and Learn phases, with zero-shot protein language models predicting beneficial mutations directly from sequence data and supervised models integrating assay results to refine subsequent variant designs. These approaches reduce the dependence on structural insights while enabling the discovery of synergistic effects across mutations. Automation technologies, including robotic liquid handlers and integrated platforms, have become central to modern protein engineering by reducing errors, ensuring reproducibility, and enabling large-scale variant screening. Emerging autonomous platforms demonstrate closed-loop optimization that couples protein library design, automated plasmid transformation/protein expression and corresponding assays, and machine learning–driven decision-making. These systems achieve significant accelerations in the research process, reducing multi-round engineering cycles from months to days while successfully improving diverse proteins/enzymes to a targeted objective. Beyond single-lab platforms, orchestration frameworks adhering to FAIR data principles and leveraging knowledge graphs promise distributed ‘self-driving’ laboratories capable of coordinating workflows across facilities. While high setup costs and proprietary systems remain challenging, open-source and modular alternatives highlight a path toward transparent, flexible automation. Collectively, these innovations are redefining protein engineering as an increasingly autonomous, data-driven discipline.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103395"},"PeriodicalIF":7.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616654","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 : 2025-11-28DOI: 10.1016/j.copbio.2025.103392
Laura Marie Helleckes , Sebastian Putz , Kshitiz Gupta , Matthias Franzreb , Hector Garcia Martin
Recent advances in artificial intelligence (AI) have rapidly changed the lab automation landscape, promoting self-driving laboratories (SDLs) that enable autonomous scientific discovery. These trends are increasingly applied in bioprocess development, yet bioprocessing faces unique challenges — biological complexity, regulatory and safety requirements, and multiscale experimentation — that distinguish it from other automation domains. Rather than pursuing full autonomy, we foresee that hybrid SDLs, combining AI-driven decision-making with sustained human oversight, represent the most practical near-term trajectory. This review examines three interconnected perspectives: (i) hybrid human–machine decision-making for bioprocessing; (ii) laboratory design considerations in the era of AI; and (iii) scale-up challenges when transitioning from screening to manufacturing. We highlight critical gaps in data standardization and the required community efforts necessary to realize autonomous bioprocess innovation.
{"title":"Perspectives for artificial intelligence in bioprocess automation","authors":"Laura Marie Helleckes , Sebastian Putz , Kshitiz Gupta , Matthias Franzreb , Hector Garcia Martin","doi":"10.1016/j.copbio.2025.103392","DOIUrl":"10.1016/j.copbio.2025.103392","url":null,"abstract":"<div><div>Recent advances in artificial intelligence (AI) have rapidly changed the lab automation landscape, promoting self-driving laboratories (SDLs) that enable autonomous scientific discovery. These trends are increasingly applied in bioprocess development, yet bioprocessing faces unique challenges — biological complexity, regulatory and safety requirements, and multiscale experimentation — that distinguish it from other automation domains. Rather than pursuing full autonomy, we foresee that hybrid SDLs, combining AI-driven decision-making with sustained human oversight, represent the most practical near-term trajectory. This review examines three interconnected perspectives: (i) hybrid human–machine decision-making for bioprocessing; (ii) laboratory design considerations in the era of AI; and (iii) scale-up challenges when transitioning from screening to manufacturing. We highlight critical gaps in data standardization and the required community efforts necessary to realize autonomous bioprocess innovation.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103392"},"PeriodicalIF":7.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616655","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 : 2025-11-24DOI: 10.1016/j.copbio.2025.103391
Bas Teusink , Pranas Grigaitis , Maaike Remeijer , Frank Bruggeman , Ralf Steuer
Wild-type cells allocate their limited resources to express proteins that support growth and survival in their natural environments. In contrast, biotechnology aims to maximize key performance indicators such as yield, productivity, or titer. Maximizing performance indicators, however, inevitably encounters physical, biochemical, genetic, and evolutionary constraints that create trade-offs between competing objectives. A central challenge in microbial biotechnology is therefore to align cellular behavior with production goals, which can be achieved by manipulating cultivation conditions and intracellular resource allocation strategies through targeted metabolic engineering or adaptive laboratory evolution. Resource allocation models provide a theoretical framework to understand and guide such optimization efforts. Here, we review the current state of resource allocation modeling, including tools, methods, and theoretical foundations, and discuss their current applications in microbial biotechnology.
{"title":"Resource allocation models: theory and applications in microbial biotechnology","authors":"Bas Teusink , Pranas Grigaitis , Maaike Remeijer , Frank Bruggeman , Ralf Steuer","doi":"10.1016/j.copbio.2025.103391","DOIUrl":"10.1016/j.copbio.2025.103391","url":null,"abstract":"<div><div>Wild-type cells allocate their limited resources to express proteins that support growth and survival in their natural environments. In contrast, biotechnology aims to maximize key performance indicators such as yield, productivity, or titer. Maximizing performance indicators, however, inevitably encounters physical, biochemical, genetic, and evolutionary constraints that create trade-offs between competing objectives. A central challenge in microbial biotechnology is therefore to align cellular behavior with production goals, which can be achieved by manipulating cultivation conditions and intracellular resource allocation strategies through targeted metabolic engineering or adaptive laboratory evolution. Resource allocation models provide a theoretical framework to understand and guide such optimization efforts. Here, we review the current state of resource allocation modeling, including tools, methods, and theoretical foundations, and discuss their current applications in microbial biotechnology.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"97 ","pages":"Article 103391"},"PeriodicalIF":7.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584151","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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