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<p>Synthetic biology enables the bottom-up synthesis of microbial genomes through the assembly of synthetic DNA fragments. Such de novo genome synthesis could enable the generation of synthetic cells, with applications in fundamental biology, biotechnology and biomedicine. In this Review, we explore the rational design of synthetic microbial genomes, including expression unit optimization, codon usage, transcriptional and translational control, and RNA and protein turnover. We then examine genome-level design considerations, highlighting the roles of chromosome architecture, gene orientation and positioning, and 3D gene arrangement, outlining strategies for the assembly and testing of synthetic genomes. Finally, we propose a path towards a fully realized synthetic cell, emphasizing the importance of method integration, including evolution-based strategies and machine learning.</p>

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De novo design of synthetic microbial genomes

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Charlotte C. Koster,
  • Henrique da Costa Oliveira,
  • Fabian van Beveren,
  • Joep Houkes,
  • Jolien J. E. van Hooff,
  • Thijs J. G. Ettema,
  • John van der Oost,
  • Nico J. Claassens

摘要

Synthetic biology enables the bottom-up synthesis of microbial genomes through the assembly of synthetic DNA fragments. Such de novo genome synthesis could enable the generation of synthetic cells, with applications in fundamental biology, biotechnology and biomedicine. In this Review, we explore the rational design of synthetic microbial genomes, including expression unit optimization, codon usage, transcriptional and translational control, and RNA and protein turnover. We then examine genome-level design considerations, highlighting the roles of chromosome architecture, gene orientation and positioning, and 3D gene arrangement, outlining strategies for the assembly and testing of synthetic genomes. Finally, we propose a path towards a fully realized synthetic cell, emphasizing the importance of method integration, including evolution-based strategies and machine learning.