期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-15798-5
关键词
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资金
- Genopole Allocation Recherche 2017
- DGA (French Ministry of Defense)
- Ecole Polytechnique
- ANR SynBioDiag [ANR-18-CE33-0015]
- ANR SINAPUV [ANR-17CE07-0046]
- INRAE (National Institute for Agricultural, Alimentation, and Environmental Research)
- University of Paris-Saclay
- BBSRC/EPSRC [BB/M017702/1]
- Life Science Department of the University of Paris Saclay
- Global Care initiative
- Institut Carnot Pasteur MS
- BBSRC [BB/M017702/1] Funding Source: UKRI
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 similar to 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.
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