Broad-Host-Range Synthetic Biology: Rethinking Microbial Chassis as a Design Variable.

IF 3.9 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Dennis Tin Chat Chan, Johan Bjerg, Hans C Bernstein
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引用次数: 0

Abstract

Broad-host-range synthetic microbiology is redefining the role of microbial hosts in genetic design by moving beyond the traditional organisms. Historically, synthetic biology has focused on optimizing engineered genetic constructs within a limited set of well-characterized chassis, often treating host-context dependency as an obstacle. However, emerging research demonstrates that host selection is a crucial design parameter that influences the behavior of engineered genetic devices through resource allocation, metabolic interactions, and regulatory crosstalk. By leveraging microbial diversity, broad-host-range synthetic biology enhances the functional versatility of engineered biological systems, enabling a larger design space for biotechnology applications in biomanufacturing, environmental remediation, and therapeutics. The continued development of broad-host-range tools─including modular vectors and host-agnostic genetic devices─facilitates the expansion of chassis selection, improving system predictability and stability. This perspective highlights the advantages of incorporating host selection into synthetic biology design principles, positioning microbial chassis as tunable components rather than passive platforms.

宽宿主范围合成生物学:重新思考微生物底盘作为设计变量。
广泛宿主范围的合成微生物学通过超越传统的生物体,重新定义了微生物宿主在遗传设计中的作用。从历史上看,合成生物学一直专注于在有限的一组特征良好的底盘内优化工程遗传结构,通常将宿主-上下文依赖性视为障碍。然而,新兴的研究表明,宿主选择是一个关键的设计参数,它通过资源分配、代谢相互作用和调控串扰影响工程遗传装置的行为。通过利用微生物多样性,广泛宿主合成生物学增强了工程生物系统的功能多功能性,为生物制造、环境修复和治疗等生物技术应用提供了更大的设计空间。广泛宿主范围工具(包括模块化载体和与宿主无关的遗传设备)的持续发展有助于扩展底盘选择,提高系统的可预测性和稳定性。这一观点强调了将宿主选择纳入合成生物学设计原则的优势,将微生物底盘定位为可调组件而不是被动平台。
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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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