Year: 2019
Type: dissertation
Abstract: Age-related diseases and conditions give rise to societal challenges and pose a threat\nto healthy ageing. At the same time, the more recent evolutionary theories of\nageing hypothesise that the process of ageing is a consequence of living rather than\nan evolutionary strategy. Consequently, it is implied that ageing is not as inevitable\nas many might believe and, as a consequence, it is of interest to study this biological\nprocess and its underlying mechanisms.\nOn a cellular level, accumulation of damage is often regarded as the main cause\nof ageing. Since the basic properties of ageing between unicellular and multicellular\norganisms are similar on this level, it is common to use the unicellular yeast\nSaccharomyces cerevisiae as a model organism in the field of ageing research.\nThe aim of this project is to validate a mathematical damage accumulation model\nof replicative ageing in yeast. The model represents a cell by intact protein and\ndamage and describes how these quantities change as the cell grows. In addition\nto cell growth, the model takes asymmetric division, retention and cell death into\naccount.\nFor the purpose of validating the model of replicative ageing, structural and numerical\nidentifiability methods are applied and continuous optimisation is performed\nusing single-cell area data. The model is fit to experimental data obtained for wildtype\nyeast and the two deletion strains sir2 and fob1. Moreover, replicative\nlifespan data of 4,698 single-gene deletion strains is analysed and, in conjunction to\nthis, it is investigated how the model parameters affect the replicative lifespan of\nthe simulations.\nThe results show that the parameters in the model of replicative ageing that\ndescribes the rate of change of intact protein and damage in the cell, are structurally\nidentifiable. In spite of this, they are not numerically identifiable based on the\nexperimental data available; the parameter estimates obtained have high variances\nand are moderately or highly correlated with each other. Likewise, it is possible to\ngenerate parameter sets that make the mathematical model reproduce the replicative\nlifespans of the investigated strains, if a replicative lifespan constraint is inferred on\nthe optimisation.\nFor future work, it is suggested that new experimental data is generated as to\nfit the model of replicative ageing to growth curves belonging to cells of later life\nstages. Ultimately, the data should be sufficient enough for the optimisation to\ngenerate parameter sets that make the model adapt to the characteristics of the\ninvestigated strains, without having additional constraints added to the objective\nfunction.
Author: Amanda Olmin
Language: English
Subfield: Aging
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2911514179