| Popis: |
Understanding the properties of biomolecular networks is of central importance in life sciences. Optical microscopy has been very useful to determine the sub-cellular localisation of proteins but it cannot reveal whether proteins interact with one another. Micro-spectroscopic techniques (combining microscopy with spectroscopy) can provide direct information on molecular interactions and dynamic events involving biomolecules with minimal perturbation of cellular integrity and function and they are particularly useful for studying life cells. Over the last few years the spatial-temporal resolution and sensitivity of these techniques have improved considerably. Detecting protein-protein interactions within a biological cell can lead to a greater understanding of the key mechanisms that regulate the fundamental processes of the cell. However, analysis of fluorescence microspectroscopy data is not a trivial task. Well-designed data analysis techniques could significantly improve the interpretation of parameters and characteristics of photophysical processes in complex molecular systems. The most commonly used methods of time-resolved spectroscopic data analysis are, respectively, nonlinear least squares, deconvolution, global analysis and maximum entropy. These methods often lead to a good description of the data but do not necessarily provide physically relevant parameters. The aim of this thesis is to develop new multidimensional fluorescence analysis methods dedicated to improve the visualization and the quantitative analysis and physical interpretation of complex spectral, spatial and time-resolved data sets. |
