NOTE: THIS IS MY NEW EMAIL ADDRESS

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Where a=steven.kiddle and b=kcl.ac.uk

In 2004 I started an undergraduate maths degree at the University of Warwick, my main interests were using calculus to model natural phenomena and scientific programming. I pursued these interests by taking modules in ODEs, mathematical biology and in computing. I decided to take these interests further by studying an MSc in Systems Biology, a field in which data from quantitative molecular biology is modelled and used to generate experimental hypotheses. Systems Biology appealed to me as a way to apply my Mathematical training to research which could directly impact societal problems. Within the MSc I learnt basic molecular biology, how to perform some simple laboratory experiments and how to apply statistics to interpret biological data. I particularly enjoyed the two three-month miniprojects, during which I learnt how to fit Bayesian Networks to biological data using Metropolis-Hasting Markov Chain Monte Carlo and how to perform western blots, reverse genetic screens and microarrays.

Supervised by Sach Mukherjee and Katherine Denby.

I research the interaction of the model plant Arabidopsis thaliana (Arabidopsis) with Botrytis cinerea, a fungal plant pathogen. The plant defence response to pathogens is mediated by an innate system of molecular interactions, with pathogen perception activating receptors and signalling pathways that cause a massive transcriptional reprogramming. The result of this is a tightly co-ordinated defence response that leads to partial resistance against the pathogen. I have been studying the dynamics of these molecular interactions, in the hope that such knowledge will help researchers to increase crop yield in important food crops by decreasing susceptibility to disease.

Breeze, Harrison, McHattie, Hughes, Hickman, Hill, Kiddle, Kim, Penfold, Jenkins, Zhang, Morris, Jenner, Jackson, Thomas, Tabrett, Legaie, Moore, Wild, Ott, Rand, Beynon, Denby, Mead, Buchanan-Wollaston. High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation. Plant Cell, 23(3):873-894, (2011).

Kiddle, Windram, McHattie, Mead, Beynon, Buchanan-Wollaston, Denby and Mukherjee. Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana. Bioinformatics, 26(3):355-362, (2010).

I have developed a novel clustering algorithm for time series of gene expression that predicts transcriptional regulation better than existing approaches, for the paper click here. I also apply graphical model approaches, such as Bayesisan Networks and State Space Models, and ODE models, based around the model of Barenco et al., to predict the structure of the gene regulatory networks that control the partial resistance of Arabidopsis to B. cinerea. I work with other groups to study promoter sequences, to find sequences that can be bound by regulatory proteins called Transcription Factors.

I have developed several approaches to integrate heterogeneous datasets, such as:

- Yeast-1-Hybrid and gene expression

- Gene expression and degradation rate

- Promoter sequences and gene expression

I work within experimental groups that perform microrray time-series at high temporal resolution, for example see the paper here. We also use reverse genetic screens, mutant vs. wildtype microarray experiments, Yeast-1-hybrid screens, biolistic transformation/promoter-reporter fusion experiments and ChIP methods to study the proteins that regulate the plant defence response.

I am very grateful to EPSRC and BBSRC for funding to do my research.