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Title: Analysing how the Arabidopsis circadian network responds to temperature
Author: Wareham, Benjamin
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2013
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The circadian clock is an endogenous 24 hours oscillator found within many organisms. It is involved in controlling gene regulation so that different processes are activated at specific times of the day. The clock maintains regular cycles with a period of approximately 24 hours in a range of conditions such as changing environmental temperature. In the past decade, great steps forward have been made in understanding the network of genes that control the circadian clock, and how these are able to maintain their rhythm in a range of conditions. However, existing models are still limited by which conditions they can accurately simulate. Here it was shown that the topology of the circadian clock in Arabidopsis thaliana changes with changing environmental temperature. This was initially investigated using transcriptomic data from ATH1 arrays. This analysis showed that plant buffering to a changing environmental temperature is controlled at a systems level and are not just controlled by a few genes. These changes occur broadly across different biological functions. However, an in-depth analysis suggests temperature responses are primarily regulated by a balancing act between transcription, translation and protein degradation. Further analysis also identified 13 genes important for temperature compensation. This was confirmed using a delayed fluorescence screen to analysis the circadian rhythm it mutants where these genes had their expression knocked out. Using a large luciferase data set, it was demonstrated how circadian genes were expressed relative to each other. Initial cluster results suggested that whilst several genes were repeatedly clustered together at different temperatures, the clustering of many genes changed with temperature. This data was then used to create networks using network inference software, which mathematically predicts gene relationships. Network inference successfully recreated networks similar to existing models. However the networks produced for each temperature had significant differences.
Supervisor: Hall, Anthony Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available