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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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The last two decades have seen an explosion of novel ideas in the areas of biotechnology and information technology. The impact of these discoveries are being felt in diverse areas such as genetics, finance, e-commerce, biology, and internet traffic, to mention a few. The scientific experiments carried out using these modern technologies are yielding data that possess an "evolutionary branching process like structure". In this talk I will describe statistical models and methodologies to understand, analyze, and interpret the data emerging from these experiments. More specifically, I will focus on the data resulting from polymerase chain reaction experiments.
The modeling and the methodological part of this talk are motivated by the following three scientific questions:
(a) How does one "statistically quantify" the unknown amount of gene in a "sample" using PCR amplification methods?
(b) What are the factors affecting the amplification rate of a PCR experiment?
(c) How does one efficiently design experiments to study the mutations induced by PCR experiments?
Statistical quantification can lead to an accurate estimate of the HIV-1 viral load in HIV-1 infected patients. This not only helps with disease diagnosis but also in the disease prognosis. Answers to questions (b) and (c) will facilitate a better understanding of the dynamics of a PCR process.
