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Computational Biology


Computational Biology research encompasses many important aspects including molecular biophysics for bio-energy, genetic level pathogen identification for bio-security, and development of data analytics for human health. We develop and apply theoretical methods and computational algorithms for investigating processes that occur at the biomolecular level to entire organisms and microbial communities. At the fundamental level, we are interested in biomolecular structure, dynamics and function particularly in relation to the biophysical mechanism of enzyme catalysis. For applications in the area of bio-energy, we focus on the discovery of pathways and regulatory networks in plant-microbial communities (in association with the Plant Microbial Interfaces, PMI, initiative) and bio-energy related crops (in association with the Bio-energy Science Center, BESC).

In particular, we utilize novel algorithms to assign functions to the so-called hypothetical genes that could not be annotated by existing techniques and advance genome-wide association approaches to link genotypes and phenotypes in plants. These investigations take advantage of analytical approaches in mass-spectrometry and high-throughput transcriptomics algorithms developed in-house. We also utilize molecular simulations to dissect the mechanism for enzyme catalyzed biochemical processes that allow conversion of biomass into easily fermentable sugars. For the area of bio-security, we develop and apply high-performance algorithms to analyze metagenomics, transcriptomics, proteomics, and metabolomics data for genetic level pathogen identification.

Another major emphasis of the Computational Biology effort is to obtain fundamental knowledge that cannot be obtained by experimental techniques, but we also work closely with the experimental facilities including the Spallation Neutron Source (SNS) at ORNL for improving the quality of information that is obtained. Another aspect of the research involves most effective utilization of heterogeneous and energy efficient computer architectures for modeling and simulations related to biology. In the area of human health, we are developing hardware and software infrastructure that will enable real-time and near real-time analysis and extraction of knowledge from the vast amount of data that is being generated.

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