Boolean Analysis on RT-PCR data

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Project Summary/Abstract Colon crypt stem cells are characterized by their ability to self-renew, or divide and produce more stem cells, as well as their ability to differentiate into various specialized cell types, such as absorptive cells, goblet cells, and enteroendocrine cells, that make up the colon lining. Abnormalities in colon crypt stem cells can lead to the development of many human gastrointestinal diseases. We have a new method that can predict differentiation hierarchy using unbiased systems biology perspective and mathematical models of large patient-derived gene expression datasets. We have mathematical models that can predict the terminally differentiated cells. The mathematical principle we use is based on Boolean implication logic that has not been commonly applied to study tissue cell populations. The Boolean analysis assigns a parameter (e.g. RNA level of a gene) with only two values, i.e., high/low , 1/0, or positive/negative. The primary goal of this proposal is to use Boolean implication relationships to decode the tissue organization of human colon. Based on our preliminary data the overall hypothesis is that Boolean principles can be used to specifically characterize the population of cell types in human colon tissue. In this proposal we are requesting availability of funds for a single equipment purchase of Bio-Rad CFX Opus Real-time PCR System that will significantly improve gene expression analysis for validation. These studies are expected to yield information about cell differentiation, diagnostic and prognostic biomarkers. Consequently, they have the potential to impact current guidelines about how to treat and manage human diseases and even help identify potential future therapeutic targets.