Type of Document Master's Thesis Author Shelton, Rebecca Kay URN etd-05132008-094821 Title Parameter Identifiability and Estimation in Gene and Protein Interaction Networks Degree Master of Science Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Baumann, William T. Committee Chair Stilwell, Daniel J. Committee Member Wyatt, Christopher L. Committee Member Keywords
- Biological Modeling
- Parameter Estimation
Date of Defense 2008-04-30 Availability unrestricted AbstractThe collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the system’s species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models.
In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for “biologically meaningful estimation”. We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings.
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