The interest for cell-to-cell variation has in recent years increased in a steady pace. Several studies have shown that a large portion of the observed variation in the nature originates from the fact that all biochemical reactions are in some respect stochastic. Interestingly, nature has evolved highly advanced frameworks specialized in dealing with stochasticity in order to still be able to produce the delicate signalling pathways that are present in even very simple single-cell organisms. Such a simple organism is Saccharomyces cerevisiae, which is the organism that has been studied in this thesis. More particulary, the distribution of the transport rate in S. cerevisiae has been studied by a mathematical modelling approach. It is shown that a two-compartment model can adequately describe the flow of a yellow fluorescent protein (YFP) between the cytosol and the nucleus. A profile likelihood (PLH) analysis shows that the parameters in the two-compartment model are identifiable and well-defined under the experimental data of YFP. Furthermore, the result from this model shows that the distribution of the transport rates in the 80 studied cells is lognormal. Also, in contradiction to prior beliefs, no significant difference between recently divided mother and daughter cells in terms of transport rates of YFP is to be seen. The modelling is performed by using both standard two-stage(STS) and nonlinear mixed effect model (NONMEM). A methodological comparison between the two very different mathematical STS and NONMEM is also presented. STS is today the conventional approach in studies of cell-to-cell variation. However, in this thesis it is shown that NONMEM, which has originally been developed for population pharmacokinetic/ pharmacodynamic (PK/PD) studies, is at least as good, or in some cases even a better approach than STS in studies of cell-to-cell variation. Finally, a new approach in studies of cell-to-cell variation is suggested that involves a combination of STS, NONMEM and PLH. In particular, it is shown that this combination of different methods would be especially useful if the data is sparse. By applying this combination of methods, the uncertainty in the estimation of the variability could be greatly reduced.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-78486 |
| Date | January 2012 |
| Creators | Janzén, David |
| Publisher | Linköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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