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Designer Biosensors as Tools for Optimizing Engineered Biosynthetic Pathways


Designer Biosensors as Tools for Optimizing Engineered Biosynthetic Pathways

Nasr, Mohamed (2022) Designer Biosensors as Tools for Optimizing Engineered Biosynthetic Pathways. PhD thesis, Concordia University.

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Synthetic biology techniques aimed at constructing artificial metabolic pathways in genetically modified microorganisms are important to develop sustainable methods to produce biofuels, pharmaceuticals, and value-added chemicals. To reach industrially relevant scales, challenges related to pathway bottlenecks and system optimization must be addressed. Since these are typically complex multi-enzyme pathways, techniques such as enzyme and genome engineering offer solutions to these limitations. However, screening methods for most products are laborious and inefficient. In this work, we utilize and engineer transcription factor-based biosensors to develop high-throughput molecule detection tools.
Bacterial allosteric transcription factors (aTFs) bind a limited set of effectors, which restricts their utility as biosensors. Our aim is to expand the toolbox of available aTFs, which we achieve by using two methods. First, we use high-throughput in vivo biosensor assays to uncover novel ligands for an aTF that could be used towards detecting new chemistries. As well, we employ protein engineering and directed evolution methodologies to engineer another aTF over several generations towards multiple aromatic molecules of increasing complexity, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. In addition to their response in Escherichia coli, we demonstrate the functionality of our engineered biosensors in the model eukaryote Saccharomyces cerevisiae. Finally, we coupled our engineered biosensors with a genome-wide, multi-functional CRISPR system to identify genetic changes that contribute towards improving the productivity of an engineered cis,cis-muconic acid pathway in Saccharomyces cerevisiae.

Divisions:Concordia University > Faculty of Arts and Science > Biology
Item Type:Thesis (PhD)
Authors:Nasr, Mohamed
Institution:Concordia University
Degree Name:Ph. D.
Date:20 June 2022
Thesis Supervisor(s):Kwan, David and Martin, Vincent
ID Code:991002
Deposited On:27 Oct 2022 14:13
Last Modified:31 Aug 2023 00:00
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