RT Journal Article
SR Electronic
T1 Large scale active-learning-guided exploration to maximize cell-free production
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 751669
DO 10.1101/751669
A1 Olivier Borkowski
A1 Mathilde Koch
A1 Agnès Zettor
A1 Amir Pandi
A1 Angelo Cardoso Batista
A1 Paul Soudier
A1 Jean-Loup Faulon
YR 2019
UL http://biorxiv.org/content/early/2019/08/30/751669.abstract
AB Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.