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Title: Finding network modules and motifs regulating plant stress responses : integration and modelling across multiple data sets
Author: Polanski, Krzysztof
ISNI:       0000 0004 5915 3319
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2015
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In spite of constant technological advancements, world hunger remains a major challenge due to exponential population growth, and the loss of e effectiveness of crop treatments such as pesticides. As such, comprehending the plant response to stress is of great importance in breeding more resilient crops. Whilst different stresses elicit distinct responses from the plant, a core set of regulatory interactions are conserved across multiple responses and operate as networks. In this thesis, computational approaches were used to elucidate such regulatory interactions from time course expression datasets, predominantly through identification of genes co-expressed across multiple stimuli responses as a footprint of shared network co-regulation. The identification of such network footprints was tackled through Wigwams, a data mining algorithm capable of detecting groups of genes co-regulated across multiple datasets. In contrast to other algorithms, Wigwams assesses whether the co-expression it detects is likely to reflect co-regulation. The modules it found were significantly enriched in functionality and cis-regulatory elements, indicating actual co-regulation. Wigwams and other computational approaches were applied to time course expression data capturing Arabidopsis thaliana response to Pseudomonas syringae pv. tomato DC3000. The presence of a virulent and avirulent strain in the experiment allowed for the temporal deconstruction of the regulatory events underlying the virulent strain's attempts to overcome plant defence through effector action. This analysis led to the detection of a number of effector-specific transcription changes stifling the defence response and manipulating the host's gene and protein expression. A transcription factor-only regulatory network model was proposed to explain the detected network footprints. The inference of causal regulatory networks from expression data is a daunting task, and transcription factor-only models are a good computational compromise by capturing the key regulatory events taking place. However, they are lacking in target genes that carry out the functionality induced by the signalling, making functional assessment di cult. Wigwams was used to introduce the network footprint components into the corresponding transcription factor-only models, resulting in enhanced network models carrying information about downstream regulated genes. This allows for functional assessment to be used to identify nodes of interest within the network, and propose concise follow-up experiments.
Supervisor: Not available Sponsor: University of Warwick
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QK Botany