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Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle

Khan, Imtiaz Ali 2008. Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle. PhD Thesis, Cardiff University.

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Abstract

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell growth and proliferation in tissues - while abnormal control and modulation of the cell cycle are characteristic of cancer cells, particularly in response to therapy. A central theme in cancer biology is to resolve and understand the origin and nature of innate and induced heterogeneity at the cell population level. Cellular heterogeneity - comprising structural, temporal and functional dimensions - is a confounding factor in the analysis of cell population dynamics and has implications at physiological, pathological and therapeutic levels. There is an exceptional advancement in the applications of imaging and cell tracking technologies dedicated to the area of cytometric research, that demand an integrated bioinformatics environment for high-content data extraction and interrogation. Image-derived cell-based analyses, where time is the quality parameter also demand unique solutions with the aim of enabling image encoding of spatiotemporal cellular events within complex cell populations. The perspective for this thesis is the complex yet poorly understood nature of cancer and the opportunities offered by rapidly evolving cytometric technologies. The research addresses the intellectual aspects of a bioinformatics framework for cellular informatics that encompass integrated data encoding, archiving, mining and analysis tools and methods capable of producing in silico cellular fingerprints for the responses of cell populations to perturbing influences. The overall goal is to understand the effects of anti-cancer drugs in complex and potentially heterogeneous neoplastic cellular systems by providing hypothesis testing opportunities. Cell lineage maps encoded from timelapse microscopy image sequences sit at the core of the proposed bioinformatics infrastructure developed in the current work. Through a number of data mining, analysis and visualisation tools the interactions and relationships within and between lineages have provided dynamic patterns for the modulation of the cell cycle in disease and under stress. The lineage data, accessible through databases implemented during the current study, has provided a rich repository for pharmacodynamic (PD) modelling and validation and has thus laid the foundation for fabricating a comprehensive knowledge base for linking both cellular and molecular behaviour patterns. These provide the foundation for meeting the aspirations of systems biology and drug discovery.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Medicine
Neuroscience and Mental Health Research Institute (NMHRI)
Subjects: Q Science > QH Natural history > QH426 Genetics
R Medicine > R Medicine (General)
ISBN: 9781303185182
Date of First Compliant Deposit: 30 March 2016
Last Modified: 31 Jul 2020 02:04
URI: http://orca.cf.ac.uk/id/eprint/55787

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