SBIR-STTR Award

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&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.

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.