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Modelling and control of gene regulatory networks for perturbation mitigation

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Foo, Mathias, Kim, Jongrae and Bates, Declan (2019) Modelling and control of gene regulatory networks for perturbation mitigation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16 (2). pp. 583-595. doi:10.1109/TCBB.2017.2771775

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Official URL: http://doi.org/10.1109/TCBB.2017.2771775

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Abstract

Synthetic Biologists are increasingly interested in the idea of using synthetic feedback control circuits for the mitigation of perturbations to gene regulatory networks that may arise due to disease and/or environmental disturbances. Models employing Michaelis-Menten kinetics with Hill-type nonlinearities are typically used to represent the dynamics of gene regulatory networks. Here, we identify some fundamental problems with such models from the point of view of control system design, and argue that an alternative formalism, based on so-called S-System models, is more suitable. Using tools from system identification, we show how to build S-System models that capture the key dynamics of an example gene regulatory network, and design a genetic feedback controller with the objective of rejecting an external perturbation. Using a sine sweeping method, we show how the S-System model can be approximated by a linear transfer function and, based on this transfer function, we design our controller. Simulation results using the full nonlinear S-System model of the network show that the synthetic control circuit is able to mitigate the effect of external perturbations. Our study is the first to highlight the usefulness of the S-System modelling formalism for the design of synthetic control circuits for gene regulatory networks.

Item Type: Journal Article
Subjects: Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Gene regulatory networks- -- Mathematical models, Synthetic biology
Journal or Publication Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Publisher: IEEE
ISSN: 1545-5963
Official Date: March 2019
Dates:
DateEvent
March 2019Published
11 January 2018Available
3 November 2017Accepted
Volume: 16
Number: 2
Page Range: pp. 583-595
DOI: 10.1109/TCBB.2017.2771775
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), University of Warwick. School of Engineering
Grant number: BB/M017982/1 (BBSRC)
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