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Abstract:
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Protein phosphorylation is important for protein post-translational modifications and is the essential mechanism to adjust function of proteins and to control the viability of proteins. Targeting on protein phosphorylation provides a feasible way to develop therapies to treat some severe human disease. However, some pre-analysis variations due to the lack of monitoring may exist in the actual experiment operations. For example, freezing delay of sample procurement may lead to cold ischemia, which may result in inaccurate study results. The objective of the current study is to examine the impact of cold ischemia on tissue phosphorylation using statistical models based on the longitudinal data observed from ovarian cancer patients. Since a typical time series analysis method is not sufficient to model the short-term time course data, the current project uses a Bayesian approach and an algorithm order-restricted inference to examine the impact of cold ischemia on tissue phosphorylation.
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