Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications. System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear systems exists. This is a master thesis in systems biology treating identification of biochemical systems. Methods based on both local parameter perturbation data and time series data have been tested and evaluated in silico. The advantage of local parameter perturbation data methods proved to be that they demand less complex data, but the drawbacks are the reduced information content of this data and sensitivity to noise. Methods employing time series data are generally more robust to noise but the lack of available data limits the use of these methods. The work has been conducted at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics in Göteborg, and at the division of Computational Biology at the Department of Physics and Measurement Technology, Biology, and Chemistry at Linköping University during the autumn of 2004.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-2811 |
| Date | January 2005 |
| Creators | Jauhiainen, Alexandra |
| Publisher | Linköpings universitet, Institutionen för fysik, kemi och biologi, Institutionen för fysik, kemi och biologi |
| 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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