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Computational modeling to design and analyze synthetic metabolic circuits

Abstract : The aims of this thesis are two-fold, and centered on synthetic metabolic circuits, which perform sensing and computation using enzymes.The first part consisted in developing reinforcement and active learning tools to improve the design of metabolic circuits and optimize biosensing and bioproduction. In order to do this, a novel algorithm (RetroPath3.0) based on similarity-guided Monte Carlo Tree Search to improve the exploration of the search space is presented. This algorithm, combined with data-derived reaction rules and varying levels of enzyme promiscuity, allows to focus exploration on the most promising compounds and pathways for bio-retrosynthesis. As retrosynthesis-based pathways can be implemented in whole cell or cell-free systems, an active learning method to efficiently explore the combinatorial space of components for rational media optimization was also developed, to design the best media maximizing cell-free productivity.The second part consisted in developing analysis tools, to generate knowledge from biological data and model biosensor response. First, the effect of plasmid copy number on sensitivity of a transcription-factor based biosensor was modeled. Then, using cell-free systems allowing for broader control over the experimental factors such as DNA concentration, resource usage was modeled to ensure our current knowledge of underlying phenomenons is sufficient to account for circuit behavior, using either empirical models or mechanistic models. Coupled with metabolic circuit design, those models allowed us to develop a new biocomputation approach, called metabolic perceptrons.Overall, this thesis presents tools to design and analyse synthetic metabolic circuits, which are a novel way to perform computation in synthetic biology.
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Submitted on : Wednesday, December 18, 2019 - 11:09:08 AM
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  • HAL Id : tel-02417453, version 1


Mathilde Koch. Computational modeling to design and analyze synthetic metabolic circuits. Quantitative Methods [q-bio.QM]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLS467⟩. ⟨tel-02417453⟩



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