Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning.

In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions.

Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses.

The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.

本研究回顾并用于合成生物学和代谢工程的主要学习方法包括监督学习、强化和主动学习，以及体外或体内学习。

在生物合成的背景下，正在利用监督机器学习来预测生物序列活动、预测结构和工程序列以及优化培养条件。

主动和强化学习方法使用通过通常涉及实验测量的迭代过程获得的训练集。它们用于设计、工程和优化代谢途径和生物过程。

体外和体内学习的新生但有希望的发展包括执行简单任务的分子电路，例如模式识别和分类。