Cells are complex biological units with advanced signalling systems, a dynamic capacity to adapt to its environment, and the ability to divide and grow. In fact, they are of such high level of complexity that it has deemed extremely difficult or even impossible to completely understand cells as complete units. The search for comprehending the cell has instead been divided into small, relatively isolated research fields, in which simplified models are used to explain cell biology. The result produced through these reductionistic investigations is integral for our current description of biology. However, there comes a time when it is possible to go beyond such simplifications and investigate cell biology at a higher level of complexity. That time is now. This thesis describes the development of mathematical tools to investigate intricate biological systems, with focus on heterogeneous protein interactions. By the use of simulations, real-time measurements and kinetic fits, standard assays for specificity measurements and receptor quantification were scrutinized in order to find optimal experimental settings and reduce labour time as well as reagent cost. A novel analysis platform, called Interaction Map, was characterized and applied on several types of interactions. Interaction Map decomposes a time-resolved binding curve and presents information on the kinetics and magnitude of each interaction that contributed to the curve. This provides a greater understanding of parallel interactions involved in the same biological system, such as a cell. The heterogeneity of the epidermal growth factor receptor (EGFR) system was investigated with Interaction Map applied on data from the instrument LigandTracer, together with complementing manual assays. By further introducing disturbances to the system, such as tyrosine kinase inhibitors and variation in temperature, information was obtained about dimerization, internalization and degradation rates. In the long term, analysis of binding kinetics and combinations of parallel interactions can improve the understanding of complex biomolecular mechanisms in cells and may explain some of the differences observed between cell lines, medical treatments and groups of patients.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-183872 |
| Date | January 2013 |
| Creators | Björkelund, Hanna |
| Publisher | Uppsala universitet, Enheten för biomedicinsk strålningsvetenskap, Uppsala |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, 1651-6206 ; 854 |
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