Fundamental design principles for transcription-factor-based metabolite biosensors

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Title: Fundamental design principles for transcription-factor-based metabolite biosensors
Author(s): Mannan, AA
Liu, D
Zhang, F
Oyarzun, DA
Item Type: Journal Article
Abstract: Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose–response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.
Publication Date: 1-Aug-2017
Date of Acceptance: 1-Aug-2017
URI: http://hdl.handle.net/10044/1/50318
DOI: https://dx.doi.org/10.1021/acssynbio.7b00172
ISSN: 2161-5063
Publisher: American Chemical Society
Journal / Book Title: ACS Synthetic Biology
Copyright Statement: This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Synthetic Biology, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://dx.doi.org/10.1021/acssynbio.7b00172
Sponsor/Funder: Human Frontier Science Program
Funder's Grant Number: RGY-0076/2015
Keywords: dynamic pathway regulation
metabolic engineering
metabolite biosensor
model-based design
pathway optimization
transcriptional regulator
Publication Status: Published online
Embargo Date: 2018-08-01
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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