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Before MOAC and the MSc year

Before MOAC

I graduated from the BSc MORSE program at Warwick in June 2008, with upper second class honours. MORSE is a unique degree and covers Mathematics, Operational Research, Statistics and Economics. The course gave me the opportunity to study topics from each of these fields, and I found interests that ranged from the geometry of fractals and topology to the theory and analysis of decisions.

Part of what I enjoyed about MORSE was using ideas from one subject to help me understand material in another. As an example the economics courses I took in my first and second years meant I found it easier to understand the concept of an individual’s utility function in decision theory (Statistics). Another example would be using the methods of linear analysis to form the theory of Markov Chains.

The MOAC DTC appealed to me as a way that I could continue learning a wide range of topics and using ideas from each to solve an equally wide range of problems.

MSc year

The taught portion of the MSc was varied, giving me a basic understanding of topics in mathematical modelling, cellular biology and physical chemistry techniques. I was also able to develop my undergraduate background, studying the BLAST algorithm for DNA sequence alignment and how to model CpG islands using Hidden Markov models. It was a fast re-introduction to some areas of biology and chemistry, neither of which I had studied past GCSE level. However I was able to move from appearing as a 'statistician wearing a lab coat' to being used to running experiments myself.

Students study three mini research projects during the second half of the MSc year. My choices are described below:

Theoretical: 'Predicting T cell receptor ligands using peptide libraries and Bayesian networks', with Dr. Hugo van den Berg (Mathematics)

I chose to take my theoretical project first as I had an interest in bioinformatical methods. We looked at the interaction between T-cell receptors and models of presented peptides, trying to find a model to explain observed experimental data. The model we used had a large number of parameters, and so to optimise these as best as possible I wrote a genetic algorithm using Matlab. This was an early case of applying lessons from the taught course as the use of a genetic algorithm was my suggestion, having first learnt about such methods only weeks before as part of CH926.

Physical science: 'Peptidoglycan structure and reactions', with Prof. Alison Rodger (Chemistry)

My second mini-project tasked me with investigating the bacterial cell wall using physical chemistry techniques. Peptidoglycan, a network of sugar chains crosslinked by short amino acid peptides, is the main stress-bearing component of most bacteria. We used linear dichroism, a comparison of the absorbance of a sample of light polarised in different directions, to see if it could provide information on structural and kinetic properties. As part of this project I travelled to Newcastle to work with the Vollmer group in producing a fresh sample of peptidoglycan. This gave me a useful experience in working in a lab and with multiple groups of people.

Wet biology: 'Structure-function analysis of the Carbohydrate Recognition Domain of the Human C-type lectin DC-SIGNR receptor via solution NMR', with Dr. Daniel Mitchell (Clinical Sciences Research Institute) and Dr. Ann Dixon (Chemistry)

Finally I looked at the structure of a domain from the human protein DC-SIGNR using solution state NMR. DC-SIGNR is a C-type lectin, a protein that binds to sugars only in the presence of calcium. One reason it is of interest is because it can be targetted by viruses such as HIV in vivo as part of the infection process of a person. Through this project I had the chance to see the expression of the domain of interest through to the purification and use in NMR experiments. In the time I had I was also able to make initial progress on the assignment of peaks on the recorded spectra. This mini-project was continued as a PhD project by Fay Probert.