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.
@article{arxiv.2210.09085,
title = {Perspectives for self-driving labs in synthetic biology},
author = {Hector Garcia Martin and Tijana Radivojevic and Jeremy Zucker and Kristofer Bouchard and Jess Sustarich and Sean Peisert and Dan Arnold and Nathan Hillson and Gyorgy Babnigg and Jose Manuel Marti and Christopher J. Mungall and Gregg T. Beckham and Lucas Waldburger and James Carothers and ShivShankar Sundaram and Deb Agarwal and Blake A. Simmons and Tyler Backman and Deepanwita Banerjee and Deepti Tanjore and Lavanya Ramakrishnan and Anup Singh},
journal= {arXiv preprint arXiv:2210.09085},
year = {2022}
}
Comments
17 pages, 3 figures. Submitted for publication in Current Opinion in Biotechnology. Updated figure 3 in this version