System identification of gene regulatory networks for perturbation mitigation via feedback control

Mathias Foo, Declan Bates, Jongrae Kim

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)
8 Downloads (Pure)

Abstract

In Synthetic Biology, the idea of using feedback control for the mitigation of perturbations to gene regulatory networks due to disease and environmental disturbances is gaining popularity. To facilitate the design of such synthetic control circuits, a suitable model that captures the relevant dynamics of the gene regulatory network is essential. Traditionally, Michaelis-Menten models with Hill-type nonlinearities have often been used to model gene regulatory networks. Here, we show that such models are not suitable for the purposes of controller design, and propose an alternative formalism. Using tools from system identification, we show how to build so-called S-System models that capture the key dynamics of the gene regulatory network and are suitable for controller design. Using the identified S-System model, we design a genetic feedback controller for an example gene regulatory network 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 second order 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 designed controller is able to mitigate the effect of external perturbations. Our findings highlight the usefulness of the S-System modelling formalism for the design of synthetic control circuits for gene regulatory networks.
Original languageEnglish
Title of host publicationIEEE International Conference on Networking, Sensing and Control
PublisherIEEE
Pages216-221
Number of pages6
ISBN (Electronic)978-1-5090-4429-0
ISBN (Print)978-1-5090-4430-6
DOIs
Publication statusPublished - 16 May 2017
Event14th International Conference On Networking, Sensing and Control - Calabria, Italy
Duration: 16 May 201718 May 2017

Conference

Conference14th International Conference On Networking, Sensing and Control
Abbreviated titleICNSC
CountryItaly
CityCalabria
Period16/05/1718/05/17

Bibliographical note

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Fingerprint Dive into the research topics of 'System identification of gene regulatory networks for perturbation mitigation via feedback control'. Together they form a unique fingerprint.

  • Cite this

    Foo, M., Bates, D., & Kim, J. (2017). System identification of gene regulatory networks for perturbation mitigation via feedback control. In IEEE International Conference on Networking, Sensing and Control (pp. 216-221). IEEE. https://doi.org/10.1109/ICNSC.2017.8000094