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自动驾驶实验室在合成生物学中的展望。

Perspectives for self-driving labs in synthetic biology.

机构信息

Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States; BCAM, Basque Center for Applied Mathematics, Bilbao, Spain.

Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States.

出版信息

Curr Opin Biotechnol. 2023 Feb;79:102881. doi: 10.1016/j.copbio.2022.102881. Epub 2023 Jan 3.

Abstract

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

摘要

自动驾驶实验室 (SDL) 将全自动实验与人工智能 (AI) 相结合,由 AI 决定下一组实验。从其最终表现形式来看,SDL 可以开创科学研究的新模式,即通过机器为人类利益来探索、解释和说明世界。虽然化学和材料科学领域已经有功能齐全的 SDL,但我们认为合成生物学提供了一个独特的机会,因为基因组为影响生物细胞行为的难以置信的广泛范围提供了一个单一目标。然而,只有在针对解决困难和推动生物学问题的情况下,创建生物 SDL 所需的投资水平才是合理的。在这里,我们讨论了为合成生物学创建 SDL 所面临的挑战和机遇。

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