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

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.
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Submitted on : Thursday, April 23, 2020 - 11:54:29 AM
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Olivier Borkowski, Mathilde Koch, Agnes Zettor, Amir Pandi, Angelo Cardoso Batista, et al.. Large scale active-learning-guided exploration for in vitro protein production optimization. Nature Communications, Nature Publishing Group, 2020, 11 (1), pp.1872. ⟨10.1038/s41467-020-15798-5⟩. ⟨pasteur-02552065⟩

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