Perspectives for Self-Driving Labs in Synthetic Biology

Hector Martin, Tijana Radivojevic, Jeremy Zucker, Kristofer Bouchard, Jess Sustarich, Sean Peisert, Dan Arnold, Nathan Hillson, Gyorgy Babnigg, Jose Marti, Christopher Mungall, Gregg Beckham, Lucas Waldburger, James Carothers, ShivShankar Sundaram, Deb Agarwal, Blake Simmons, Tyler Backman, Deepanwita Banerjee, Deepti TanjoreLavanya Ramakrishnan, Anup Singh

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations


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.

Original languageAmerican English
Article number102881
Number of pages8
JournalCurrent Opinion in Biotechnology
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

NREL Publication Number

  • NREL/JA-2A00-84830


  • artificial intelligence
  • automation
  • biomanufacturing
  • machine learning
  • synthetic biology


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