Large scale active-learning-guided exploration for in vitro protein production optimization

Olivier Borkowski, Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, Paul Soudier and Jean-Loup Faulon ()
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Olivier Borkowski: Univ Evry, Université Paris-Saclay
Mathilde Koch: Université Paris-Saclay
Agnès Zettor: Center for Technological Resources and Research (C2RT)
Amir Pandi: Université Paris-Saclay
Angelo Cardoso Batista: Université Paris-Saclay
Paul Soudier: Université Paris-Saclay
Jean-Loup Faulon: Univ Evry, Université Paris-Saclay

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract 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 buffer 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15798-5

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DOI: 10.1038/s41467-020-15798-5

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