The autorepressor: A case study of the importance of model selection

Harris, Andreas W. K., Kelly, Ciarán, Steel, Harrison and Papachristodoulou, Antonis (2018) The autorepressor: A case study of the importance of model selection. In: CDC 2017 - IEEE 56th Annual Conference on Decision and Control, 12th - 15th December 2017, Melbourne, Australia.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/CDC.2017.8263882

Abstract

Major challenges exist in the design of gene regulatory networks. Some of these can be addressed by the in silico modelling and design of systems prior to implementation. However, reliable modelling of a given system is predicated upon a range of simplifying assumptions which may only be valid for a limited range of architectures and experimental conditions. In this paper we study the autorepressor, also referred to as the negative autoregulator, a genetic motif common both in natural and synthetic circuits. A number of approaches to modelling the autorepressor are presented, and one of these is extended to include the impact of inducer consumption, a phenomenon frequently observed in experiments. We implement this system using the tet-repressor (TetR), and compare the in vivo data with the results of simulations using parameters taken from the literature. We demonstrate that a modelling approach that considers inducer sequestration due its binding with a transcription factor may be required to qualitatively replicate experimental results. We conclude by drawing comparisons between experimental and simulated results, and discuss approaches by which modelling could be extended to better represent observed behaviours.

Item Type: Conference or Workshop Item (Paper)
Subjects: C400 Genetics
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Paul Burns
Date Deposited: 26 Sep 2019 09:12
Last Modified: 10 Oct 2019 14:48
URI: http://nrl.northumbria.ac.uk/id/eprint/40863

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics