Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/29959
Title: SYSTEMS BIOLOGY OF AGING: MODELING & ANALYSIS OF MITOCHONDRIAL GENOME INTEGRITY
Authors: SURESH KUMAR POOVATHINGAL
Keywords: Computational Biology, Stochastic Drift, Aging, Mitochondrial DNA(mtDNA), mtDNA Mutations.
Issue Date: 7-Mar-2011
Source: SURESH KUMAR POOVATHINGAL (2011-03-07). SYSTEMS BIOLOGY OF AGING: MODELING & ANALYSIS OF MITOCHONDRIAL GENOME INTEGRITY. ScholarBank@NUS Repository.
Abstract: The mitochondrial free radical theory of aging (mFRTA) implicates reactive oxygen species (ROS) as the causal agent of degeneration of mitochondrial genome integrity and subsequent cellular respiratory dysfunctions, and a leading cause of tissue degeneration and aging. While several premises of the mitochondrial theory of aging are intensely debated, there exists overwhelming evidence supporting the importance of mtDNA mutations in aging. Significant challenges to the premises of mitochondrial theory of aging are associated with the high uncertainty in the reported age-dependent mitochondrial data such as the mtDNA mutation burden in cells. The source of such variability may be multifaceted, including both intrinsic cellular stochasticity and variability due to measurement protocols. In addition, while there exists ample amount of experimental evidence hinting for the direct role of mitochondria and mtDNA in cellular physiology and aging, there is still a large knowledge gap in understanding the mechanisms of origin and accumulation dynamics of mtDNA mutations during aging. Consequently, to better understand mtDNA mutation dynamics during aging, the following aspects have been addressed in this work: 1. Several novel and parsimonious stochastic models of mtDNA mutation dynamics that encompasses only the most relevant biological processes have been developed to elucidate the origin and mechanism of mtDNA mutation accumulation and its consequence to cellular aging. 2. Statistical modeling approaches have been developed to design experiments that minimize variability associated with mtDNA mutation measurement assay, i.e. maximize the signal-to-noise ratio, and 3. To complement the stochastic modeling framework, practical kinetic parameter estimation methods based on either single cell or cell population level data have also been proposed.
URI: http://scholarbank.nus.edu.sg/handle/10635/29959
Appears in Collections:Ph.D Theses (Open)

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