|Ph.D.,||Texas A&M University,||(2010)|
Mathematical modeling plays an important role for studying signal transduction mechanisms. However, deriving an accurate model of a signal transduction pathway is non-trivial as the mechanisms tend to involve many components and the system will have a large degree of uncertainty in both its structure and parameter values.
One avenue for improving the understanding of biological systems is starting from what is already known and validating and refining existing models. Detailed model validation and refinement can only be successful if quantitative data is available or can be generated. However, very limited quantitative data exits as most measurements of protein concentrations involve Western blots which only result in semi-quantitative information. Qualitative data, on the other hand, is easier to obtain as a rich body of literature exists that describes the key components of signal transduction pathways. Due to this modeling of signal transduction pathways is characterized by a lack of quantitative data, yet a significant amount of qualitative knowledge is available.
My work investigates procedures that are specifically tailored for developing models of signal transduction pathways. One technique deals with deriving quantitative data of protein concentrations from fluorescence microscopy images, while other aspects of my work investigate modeling techniques that are suitable for describing signal transduction pathways even if only a limited amount of information exists. These techniques will ultimately be used to develop and refine models of the TNF-a and the IL-6 signaling pathway.
Two specific topics that are under investigation right now deal with the development of a measurement technique that allows us to derive quantitative data about transcription factor concentrations using non-invasive measurements, and the use of modeling procedures that incorporate quantitative data as well as qualitative data via fuzzy modeling.