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bioRxiv · 10.1101/751669

Large scale active-learning-guided exploration to maximize cell-free production

Borkowski, O.·Koch, M.·Zettor, A.·Pandi, A.·Cardoso Batista, A.·Soudier, P.·Faulon, J.-L.
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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 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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Borkowski, O., Koch, M., Zettor, A., Pandi, A., Cardoso Batista, A., Soudier, P., Faulon, J.-L.. 2019-08-30. Large scale active-learning-guided exploration to maximize cell-free production. https://doi.org/10.1101/751669

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