Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions Among Nitrifying Bacteria

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-FG02-10ER86443
Agency Tracking Number: 95250
Amount: $749,792.00
Phase: Phase II
Program: STTR
Awards Year: 2011
Solicitation Year: 2010
Solicitation Topic Code: 35 a
Solicitation Number: DE-FOA-0000161
Small Business Information
716 Waterwood Dr., Norman, OK, 73072-4369
DUNS: 827000956
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Jizhong Zhou
 (405) 213-1790
Business Contact
 Cindy Shi
Title: Dr.
Phone: (405) 213-1790
Research Institution
 University of Oklahoma
 Three Partners Place, Suite 150
201 David L. Boren Blvd
Norman, OK, 73019-5300
 () -
 Nonprofit college or university
The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning but very little is known about the network interactions in a microbial community due to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomics technologies can rapidly produce massive data, but one of the greatest challenges is how to extract, analyze, synthesize, and transform such vast amount of information to biological knowledge. To address such challenges, a novel conceptual framework and computational approaches will be developed based on a mathematical approach, random matrix theory (RMT) using large scale, high throughput metagenomics sequencing and hybridization data. We will first use high throughput sequencing technologies to examine the diversity of AmoA genes in a grassland ecosystem exposed to elevated CO2 for 12 years to understand how nitrifying bacteria respond to elevated CO2, followed by an updated version of GeoChip for detecting nitrifying populations. GeoChip is a revolutionary, high throughput genomics technology for linking microbial community structure to ecosystem functions, which allows researchers to address scientific questions which could not be approached previously. GeoChip-based technologies, OU GeoChip won one of R & amp;D 100 Awards of 2009, which recognizes the 100 most technological innovations with the greatest commercial potentials. Based on metagenomics data from both pyrosequencing and GeoChip hybridizations, in this proposed study, we will develop a novel conceptual framework and computational approaches for identification and characterization of network interactions of microbial communities based on random matrix theory. Commercial Applications and Other Benefits: The proposed conceptual framework and computational approaches for constructing molecular ecological networks (MENs) will be developed through the Phase I support, which is not only critical for addressing the objectives outlined in this study, (developing a comprehensive computational software package for analyzing network interactions of microbial communities proposed in the Phase II study), but also important for the study of microbial ecology in general. The developed novel network approach will allow microbiologists to address fundamental questions which could not be approached previously. In addition, the development of RMT-based network approach will enhance the uniqueness of GeoChip technologies, such as GeoChip data analysis and interpretation, and hence further promote the commercialization of GeoChip-based technologies.

* Information listed above is at the time of submission. *

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