Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.600282
Title: A network inference approach to understanding musculoskeletal disorders
Author: Turan, Nil
ISNI:       0000 0004 5350 6160
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2014
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Abstract:
Musculoskeletal disorders are among the most important health problem affecting the quality of life and contributing to a high burden on healthcare systems worldwide. Understanding the molecular mechanisms underlying these disorders is crucial for the development of efficient treatments. In this thesis, musculoskeletal disorders including muscle wasting, bone loss and cartilage deformation have been studied using systems biology approaches. Muscle wasting occurring as a systemic effect in COPD patients has been investigated with an integrative network inference approach. This work has lead to a model describing the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. This model has shown for the first time that oxygen dependent changes in the expression of epigenetic modifiers and not chronic inflammation may be causally linked to muscle dysfunction. Bone and cartilage deformation observed in ageing, arthritis and multiple myeloma (MM) patients have also been investigated by using a novel modularization approach developed within this thesis. This methodology allows integration of multi-level dataset with large interaction networks. It aims to identify sub-networks with genes differentially expressed between experimental conditions that are co-regulated across samples in different biological systems. This study has identified several potential key players such as Myc, DUSP6 and components of Notch that could enhance osteogenic differentiation in MM patients. In conclusion, this thesis present the effectiveness of systems biology approaches in understanding complex diseases and these approaches could be applied for studying other systems and datasets.
Supervisor: Not available Sponsor: BioBridge ; ERASys
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.600282  DOI: Not available
Keywords: QM Human anatomy ; QP Physiology ; QR Microbiology
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