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Title: Computational approaches to the study of T cell migration and the T cell receptor repertoire
Author: Thomas, N. C.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2014
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Two pertinent questions in T cell immunology are addressed by using techniques from machine learning to describe T cell migration dynamics and characteristics of the T cell receptor repertoire. Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. The time T cells may spend in an individual lymph node is estimated by analysing data from long term cannulation of blood and efferent lymphatics of a single lymph node in sheep. The distribution of transit times of migrating T cells is determined empirically by applying the Least Absolute Shrinkage & Selection Operator to experimental data. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes. High throughput sequencing provides an opportunity to analyse the repertoire of antigen specific receptors with an unprecedented breadth and depth. However, the quantity of raw data produced by this technology requires efficient ways to categorise and store the output for subsequent analysis. A novel application of a finite state automaton is implemented to characterise T cell receptor sequences for the purpose of downstream analysis. Finally, the clonal theory of adaptive immunity proposes that immunological responses are encoded by increases in the frequency of lymphocytes carrying antigen-specific receptors. Both unsupervised (hierarchical clustering) and supervised (support vector machine) learning techniques are successfully used to track changes in the T cell receptor repertoire induced by immunization, using contiguous stretches of amino acids within the T cell receptor complementarity determining region 3 repertoire of different mice.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available