Palacios Sebastian, Collins James J, Del Vecchio Domitilla
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02215, USA.
Curr Opin Biotechnol. 2025 Apr;92:103263. doi: 10.1016/j.copbio.2025.103263. Epub 2025 Jan 27.
Synthetic biology leverages engineering principles to program biology with new functions for applications in medicine, energy, food, and the environment. A central aspect of synthetic biology is the creation of synthetic gene circuits - engineered biological circuits capable of performing operations, detecting signals, and regulating cellular functions. Their development involves large design spaces with intricate interactions among circuit components and the host cellular machinery. Here, we discuss the emerging role of machine learning in addressing these challenges. We articulate how machine learning may enhance synthetic gene circuit engineering, from individual components to circuit-level aspects, while highlighting associated challenges. We discuss potential hybrid approaches that combine machine learning with mechanistic modeling to leverage the advantages of data-driven models with the prescriptive ability of mechanism-based models. Machine learning and its integration with mechanistic modeling are poised to advance synthetic biology, but challenges need to be overcome for such efforts to realize their potential.
合成生物学利用工程原理对生物学进行编程，赋予其新功能，以应用于医学、能源、食品和环境领域。合成生物学的一个核心方面是合成基因回路的创建——能够执行操作、检测信号和调节细胞功能的工程化生物回路。它们的开发涉及大型设计空间，回路组件与宿主细胞机制之间存在复杂的相互作用。在此，我们讨论机器学习在应对这些挑战中日益凸显的作用。我们阐述了机器学习如何从单个组件到回路层面增强合成基因回路工程，同时强调相关挑战。我们讨论了将机器学习与机理建模相结合的潜在混合方法，以利用数据驱动模型的优势和基于机制模型的规范性能力。机器学习及其与机理建模的整合有望推动合成生物学的发展，但要实现其潜力，还需要克服诸多挑战。
