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

Author

Listed:
  • 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
    Université Paris-Saclay
    University of Manchester)

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.

Suggested Citation

  • Olivier Borkowski & Mathilde Koch & Agnès Zettor & Amir Pandi & Angelo Cardoso Batista & Paul Soudier & Jean-Loup Faulon, 2020. "Large scale active-learning-guided exploration for in vitro protein production optimization," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15798-5
    DOI: 10.1038/s41467-020-15798-5
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    Cited by:

    1. Amir Pandi & Christoph Diehl & Ali Yazdizadeh Kharrazi & Scott A. Scholz & Elizaveta Bobkova & Léon Faure & Maren Nattermann & David Adam & Nils Chapin & Yeganeh Foroughijabbari & Charles Moritz & Nic, 2022. "A versatile active learning workflow for optimization of genetic and metabolic networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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