Zhang Daolei, Xu Fan, Wang Fanhua, Le Liang, Pu Li
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; School of Life Sciences, Inner Mongolia University, Hohhot 010021, China.
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China.
Plant Commun. 2025 Feb 10;6(2):101220. doi: 10.1016/j.xplc.2024.101220. Epub 2024 Dec 12.
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved bioproduction based on an input genetic circuit (DNA, RNA, and proteins). Crop synthetic biology is a new tool that uses circular principles to redesign and create innovative biological components, devices, and systems to enhance yields, nutrient absorption, resilience, and nutritional quality. In the digital age, artificial intelligence (AI) has demonstrated great strengths in design and learning. The application of AI has become an irreversible trend, with particularly remarkable potential for use in crop breeding. However, there has not yet been a systematic review of AI-driven synthetic biology pathways for plant engineering. In this review, we explore the fundamental engineering principles used in crop synthetic biology and their applications for crop improvement. We discuss approaches to genetic circuit design, including gene editing, synthetic nucleic acid and protein technologies, multi-omics analysis, genomic selection, directed protein engineering, and AI. We then outline strategies for the development of crops with higher photosynthetic efficiency, reshaped plant architecture, modified metabolic pathways, and improved environmental adaptability and nutrient absorption; the establishment of trait networks; and the construction of crop factories. We propose the development of SMART (self-monitoring, adapted, and responsive technology) crops through AI-empowered synthetic biotechnology. Finally, we address challenges associated with the development of synthetic biology and offer potential solutions for crop improvement.
合成生物学通过运用工程学原理,在改善作物性状和增加生物产量方面发挥着关键作用。这些原理通过“设计、构建、测试和学习”循环有目的地改造植物,最终基于输入的遗传电路(DNA、RNA和蛋白质)提高生物产量。作物合成生物学是一种新工具,它运用循环原理重新设计和创造创新的生物组件、装置和系统,以提高产量、养分吸收能力、抗逆性和营养品质。在数字时代,人工智能(AI)在设计和学习方面展现出巨大优势。人工智能的应用已成为不可逆转的趋势,在作物育种中的应用潜力尤为显著。然而,尚未有对人工智能驱动的植物工程合成生物学途径进行系统综述。在本综述中,我们探讨了作物合成生物学中使用的基本工程原理及其在作物改良中的应用。我们讨论了遗传电路设计方法,包括基因编辑、合成核酸和蛋白质技术、多组学分析、基因组选择、定向蛋白质工程和人工智能。然后,我们概述了培育具有更高光合效率、重塑株型、改良代谢途径、提高环境适应性和养分吸收能力的作物的策略;性状网络的建立;以及作物工厂的构建。我们提议通过人工智能赋能的合成生物技术开发智能(自我监测、自适应和响应式技术)作物。最后,我们阐述了合成生物学发展面临的挑战,并为作物改良提供了潜在解决方案。