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http://eprints.org/relation/isVolatileVersionOf http://etheses.bham.ac.uk/id/document/1541 http://eprints.org/relation/ispreviewThumbnailVersionOf http://etheses.bham.ac.uk/id/document/1541 http://eprints.org/relation/isVersionOf http://etheses.bham.ac.uk/id/document/1541 archive 4031 disk0/00/00/06/36 2010-04-29 14:17:56 2019-01-23 09:52:32 2011-07-19 16:20:45 thesis show 0 Treviño Alvarado Victor Manuel vtrevino@itesm.mx Falciani Francesco University of Birmingham unpub QH426 30sch_bios Feature selection, Microarrays, Functional Genomics, Bioinformatics Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data. 2007-12 2007-06 graduation eng author unchecked This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Consejo Nacional de Ciencia y Tecnología, Mexico The Darwin Trust of Edinburgh public 243 University of Birmingham School of Biosciences d_ph other 2007-12 graduation