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Type: Theses
Title: The design, synthesis and quantitative analysis of a bistable mixed feedback loop gene network
Author: Pietsch, Julian Michael Juers
Issue Date: 2015
School/Discipline: School of Biological Sciences
Abstract: Bistability, the capacity for switch-like memory, is a fundamental building block for robust behaviour in the noisy biochemical environment of a cell. Bistability has been observed experimentally in gene networks that exhibit overall positive feedback in some form; particular properties are endowed by variations on the basic network topology. The Mixed Feedback Loop (MFL) is a two-protein network that can be configured for positive feedback, and is notable since it has been observed to arise in nature more often than expected. The MFL includes an intervening protein-protein interaction to close a transcriptional feedback loop. This network architecture has been predicted to support bistable operation even without molecular cooperativity. To investigate the capabilities and features of the MFL, a synthetic bistable MFL was designed for construction in Escherichia coli (E. coli) using genetic components from bacteriophage 186. The design consists of the phage CI repressor protein inhibiting the production of its corresponding Tum antirepressor. This Tum-CI MFL prototype was first validated using a deterministic model expressly formulated for this instance of the MFL. It was then constructed in E. coli with dual LacZ and fluorescent reporters to permit multiple modes of measurement. Hysteresis assays—assays testing for history dependence or ‘memory’ of the system—were chosen as the measure of bistability, both since the bistable MFL naturally lends itself to such an assay, and since the assay simultaneously enables optimisation and setting of the switch. Measured by LacZ assay, the bistable MFL showed limited hysteresis. A detailed experimental characterisation of the network components and strains assisted in refining the data and setting bounds on model parameters. However, whilst this served to increase analytical accuracy, the deterministic model remained a poor fit of the data. When instead measuring activities in single cells by flow cytometry using the fluorescent reporter, two semi-stable sub-populations were discovered. Poor separation of the sub-populations necessitated the development of a system-specific mixture model for accurate identification of their characteristics, but the sub-population dynamics found much better agreement with the deterministic model. By building on this model with a hybrid stochastic/deterministic model, the limited hysteresis seen by LacZ assay can be explained by variation in switch robustness: the steady-state repressor concentration weights each cell’s ‘decision’ for either of the two stable states. These results further an understanding of the core requirements for stable maintenance of epigenetic memory. The simplifications made by isolating the MFL according to the ‘synthetic biology’ approach allowed key features of this network motif to be determined. A deep knowledge of simple circuit structures like the MFL contributes fundamentally towards the way we understand proteins and how they fit into the complex networks that underpin the workings of life.
Advisor: Shearwin, Keith
Dodd, Ian
Dissertation Note: Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Biological Sciences, 2015.
Keywords: quantitative biology
synthetic biology
gene network models
stochastic modelling
flow cytometry
bacteriophage 186
Research by Publication
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
DOI: 10.4225/55/5900266a05dd5
Appears in Collections:Research Theses

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