This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Diseases occur not only due to harmful pathogens that act in isolation but also due to disruption of the balance of the human-microbial ecosystem. This emerging knowledge requires a revision of the current diagnostic approaches to incorporate information about the """"""""normal"""""""" state of these ecosystems and the nature of deviation from this state that can result in disease. It also requires making these revised approaches readily available to the medical community to facilitate diagnosing diseases. The goal for this project is to develop computational approaches to differentiate between normal and abnormal (associated with disease symptoms) microbial communities, taking metadata (such as gender, age, ethnicity, and health history) into consideration. Specifically, we aim to: 1) develop model-based clustering methods to accurately distinguish and characterize different microbial community groups and 2) develop model-based classification methods to correctly and efficiently classify newly sampled microbial communities to pre-existing, well characterized groups. We will attain these specific aims in three stages: modeling, implementation and validation. Modeling will involve the use of probability theory and a Bayesian framework to capture the information available in the data. Implementation will involve the use of Monte Carlo methods in model-parameter estimation and microbial community-group association. Validation of the accuracy of the proposed methods will be performed using real and simulated data. Achievement of our Aims will form the basis of a decision-support system to assist clinicians in identifying deviations from normal microbial community structures. For this purpose we will produce software that will be made available to the medical community for research and diagnostic purposes.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Exploratory Grants (P20)
Project #
5P20RR016448-08
Application #
8167455
Study Section
National Center for Research Resources Initial Review Group (RIRG)
Project Start
2010-02-01
Project End
2011-01-31
Budget Start
2010-02-01
Budget End
2011-01-31
Support Year
8
Fiscal Year
2010
Total Cost
$119,218
Indirect Cost
Name
University of Idaho
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
075746271
City
Moscow
State
ID
Country
United States
Zip Code
83844
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