TY - JOUR
T1 - Perspectives for Self-Driving Labs in Synthetic Biology
T2 - Article No. 102881
AU - Martin, Hector
AU - Radivojevic, Tijana
AU - Zucker, Jeremy
AU - Bouchard, Kristofer
AU - Sustarich, Jess
AU - Peisert, Sean
AU - Arnold, Dan
AU - Hillson, Nathan
AU - Babnigg, Gyorgy
AU - Marti, Jose
AU - Mungall, Christopher
AU - Beckham, Gregg
AU - Waldburger, Lucas
AU - Carothers, James
AU - Sundaram, ShivShankar
AU - Agarwal, Deb
AU - Simmons, Blake
AU - Backman, Tyler
AU - Banerjee, Deepanwita
AU - Tanjore, Deepti
AU - Ramakrishnan, Lavanya
AU - Singh, Anup
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - automation
KW - biomanufacturing
KW - machine learning
KW - synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=85145976987&partnerID=8YFLogxK
U2 - 10.1016/j.copbio.2022.102881
DO - 10.1016/j.copbio.2022.102881
M3 - Article
SN - 0958-1669
VL - 79
JO - Current Opinion in Biotechnology
JF - Current Opinion in Biotechnology
ER -