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Poster De Conférence Année : 2019

A large-scale exploration of cell-free compositions to maximize protein production using active learning

Résumé

Lysate-based cell-free systems have become a major platform to study gene expression 1-3 but batch-to-batch variation makes protein production difficult to predict 4. Here we describe an active learning approach 5 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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Dates et versions

hal-04292156 , version 1 (21-11-2023)

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  • HAL Id : hal-04292156 , version 1

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Olivier Borkowski, Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, et al.. A large-scale exploration of cell-free compositions to maximize protein production using active learning. Structural Biology and Chemistry Department days, Dec 2019, paris, France. ⟨hal-04292156⟩
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