Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2014

2058  The University of Texas at San Antonio  (23618)

Principal Investigator: Ruan, Jianhua

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 302,657

Exceeds $250,000 (Is it flagged?): Yes

Start and End Dates: 10/1/12 <> 9/30/15

Restricted Research: YES

Academic Discipline: DEAN - COLLEGE OF SCIENCES  

Department, Center, School, or Institute: none

Title of Contract, Award, or Gift: Topology-based approaches to integrated analysis of transcriptomic, protein-interactomic and phenotypic data

Name of Granting or Contracting Agency/Entity: National Science Foundation
CFDA Link: NSF
47.070

Program Title: none
CFDA Linked: Computer and Information Science and Engineering

Note:

Due to the explosion of high-throughput functional genomics data, network models that represent genes / proteins as nodes and relationships as edges are increasingly used for analyzing such data with the hope to obtain a systems-level understanding of biology. However, how to best analyze / utilize such networks pose multiple challenges to biologists; these include lack of robust and easy-to-use tools to deal with high levels of experimental noise and skewed degree distributions, lack of systematic approaches to discovering connections between network topologies and functions, and lack of intuitive and effective methodology to integrate network topology in conventional data analysis workflows. With his academic training in both computer science and biology, the PI proposes the following three research objectives for this project: (1) to develop topology-based algorithms for improving the accuracy and robustness of module discovery from protein-protein interaction (PPI) and transcriptomic data, (2) to systematically analyze PPI networks to identify significant correlations between network topologies and biological functions, and (3) to develop novel models and methods for network-based analysis / classification of phenotypes, via integration of PPI and transcriptomic data. These algorithms will be applied to study several biological processes of central interests to biologist collaborators, who have committed to validate some of the computational predictions. %These includes identifying novel plant hormone response genes and DNA damage response genes, and predicting breast cancer metastasis potentials.

Discussion: No discussion notes

 

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