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Lookup NU author(s): Ruud Stoof, Dr Alexander Wood, Dr Angel Goni-Moreno
This is the authors' accepted manuscript of an article that has been published in its final definitive form by American Chemical Society, 2019.
For re-use rights please refer to the publisher's terms and conditions.
Mathematical modeling assists the design of synthetic regulatory networks by providing a detailed mechanistic understanding of biological systems. Models that can predict the performance of a design are fundamental for synthetic biology since they minimize iterations along the design-build-test lifecycle. Such predictability depends crucially on what assumptions (i.e., biological simplifications) the model considers. Here, we challenge a common assumption when it comes to the modeling of bacterial-based gene regulation: considering negligible the effects of intracellular physical space. It is commonly assumed that molecules, such as transcription factors (TF), are homogeneously distributed inside a cell, so there is no need to model their diffusion. We describe a mathematical model that accounts for molecular diffusion and show how simulations of network performance are decisively affected by the distance between its components. Specifically, the model focuses on the search by a TF for its target promoter. The combination of local searches, via one-dimensional sliding along the chromosome, and global searches, via three-dimensional diffusion through the cytoplasm, determine TF-promoter interplay. Previous experimental results with engineered bacteria in which the distance between TF source and target was minimized or enlarged were successfully reproduced by the spatially resolved model we introduce here. This suggests that the spatial specification of the circuit alone can be exploited as a design parameter in synthetic biology to select programmable output levels.
Author(s): Stoof R, Wood A, Goni-Moreno A
Publication type: Article
Publication status: Published
Journal: ACS Synthetic Biology
Year: 2019
Volume: 8
Issue: 9
Pages: 2007-2016
Print publication date: 20/09/2019
Online publication date: 20/08/2019
Acceptance date: 18/01/2019
Date deposited: 14/02/2020
ISSN (electronic): 2161-5063
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acssynbio.9b00022
DOI: 10.1021/acssynbio.9b00022
PubMed id: 31429541
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