Perspectives for self-driving labs in synthetic biology

TL;DR

Self-driving labs in synthetic biology could revolutionize research by automating experiments and AI-driven decision-making, but require significant investment to address complex biological questions.

Contribution

This paper discusses the potential, challenges, and opportunities of developing self-driving labs specifically for synthetic biology applications.

Findings

Synthetic biology offers a unique target for SDLs due to genome influence.

Creating biological SDLs requires addressing significant technical and biological challenges.

Investments in SDLs are justified when aimed at solving complex biological problems.

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 towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.