Data-driven synthetic microbes for sustainable future.

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2025-07-07 DOI:10.1038/s41540-025-00556-4

Iqra Mariam, Ulrika Rova, Paul Christakopoulos, Leonidas Matsakas, Alok Patel

引用次数: 0

Abstract

The escalating global environmental crisis demands transformative biotechnological solutions that are both sustainable and scalable. This perspective advocates Data-Driven Synthetic Microbes (DDSM); engineered microorganisms designed through integrating omics, machine learning, and systems biology to tackle challenges like PFAS degradation, greenhouse gas mitigation, and sustainable biomanufacturing. DDSMs offer a rational framework for developing robust microbial systems, reshaping the future of synthetic biology toward environmental resilience and circular bioeconomy.

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可持续未来的数据驱动合成微生物。

不断升级的全球环境危机需要具有可持续性和可扩展性的变革性生物技术解决方案。这种观点提倡数据驱动的合成微生物（DDSM）；通过整合组学、机器学习和系统生物学来设计工程微生物，以应对PFAS降解、温室气体减排和可持续生物制造等挑战。dddms为开发强大的微生物系统提供了一个合理的框架，重塑了合成生物学的未来，朝着环境弹性和循环生物经济的方向发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.