合成生物元件与线路的智能设计

Machine learning-aided design of synthetic biological parts and circuits

摘要:

合成生物学是生物学、工程学和计算机科学等多学科交叉融合的新兴前沿领域，旨在通过“自下而上”的工程化设计理念，逐级构建元件、器件和线路，以创造自然界中不存在的人工生物系统，或对已有的生物系统进行目标性改造。随着合成生物产业的飞速发展，对基因线路规模和复杂度的需求也在不断提升。然而，传统依赖经验和试错的方法在元件与线路构建中具有较低的效率和成功率，已无法满足合成生物科技创新转化的需求。这促使元件与线路的开发范式逐渐从人力型、经验型的试错模式向标准化、智能化的工程模式转变。机器学习能够揭示生物数据中隐含的结构和关联，为合成生物元件和线路的智能设计提供强大支持。本文综述了生物元件与线路设计中常用的机器学习算法，以及它们在合成启动子、RNA调控元件、转录因子等生物元件和简单基因线路智能设计中的典型应用，探讨了当前面临的主要挑战及潜在的解决方案。最后，本文展望了机器学习与合成生物系统设计未来的融合趋势，并强调了跨学科合作的重要性。

关键词:合成生物学;生物元件;基因线路;生物设计;机器学习;深度学习

Abstract:

Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up approach to progressively design biological parts, devices, and circuits, aiming to create artificial biological systems not found in nature or to redesign existing biological systems for specific purposes. With the rapid development of the synthetic biology industry, there is an increasing demand for large complex genetic circuits. However, the traditional trial-and-error methods, heavily reliant on empirical knowledge, have limited efficiency and success rates of parts/circuits construction, thereby impeding the innovation and technology translation for synthetic biology. These limitations have prompted a paradigm shift from labor-intensive, experience-driven trial-and-error models towards standardized, intelligent engineering approaches. Machine learning, capable of uncovering hidden structures and relationships within biological data, offers robust support for the intelligent design of synthetic biological parts and genetic circuits. Here, we review commonly used machine learning algorithms and analyze their typical applications in designing biological parts (e.g., synthetic promoters, RNA regulatory elements, and transcription factors) and simple genetic circuits. Additionally, we discuss the primary challenges in machine learning-aided design and propose potential solutions. Lastly, we envision the future trend of integrating machine learning with synthetic biological system design, highlighting the importance of interdisciplinary collaboration.

Key words:synthetic biology;biological parts;genetic circuits;biodesign;machine learning;deep learning