seeyeast - Dynamic Visualization in Genomic and Proteomic Research of Yeast for Bioenergy
What cellular mechanisms underlie the organismal tolerance to a variety of stresses that yeast is exposed to during its bioethanol production of thermochemically pretreated plant material in industrial production? How can this tolerance be enhanced? Answers to these questions bear groundbreaking significances but are hard to obtain. Although the starting point of such research are experiemental data, it is imperative to combine expertise from large scale computing, statistics and visualization to study this subject effectively and comprehensively.
The specific research problem we address here focuses on pull-down technology for mapping protein-protein interactions, a key step for revealing a complex network of biological processes in the cell. While this technology can identify protein complexes, it is vulnerable to generating false positive interactions, and the pressing need to address such false positives presents a great challenge to the entire community.
ORNL researchers, led by Dr. Nagiza Samatova, have discovered that experimental data obtained by large-scale pull-down studies can produce more comprehensive biological information and fewer false positive interactions if they are interpreted in terms of a more general common-target model. In this framework, interactive dynamic visualization, provived by SciDAC IUSV researchers, further addresses uncertainty in the data and lead to higher level of confidence in the final results. The above diagram depicts the cellular machinery involved in stress-induced transcriptional reprogramming of the yeast cells, and the discovered biological proteomic networks.
Credits: This project is featured in SciDAC Review, Issue 2, 2007. Dr. Samatova's research team at ORNL is funded by SciDAC Scientific Data Management (SDM) Center and the BioPilot program under the auspices of DOE Office of Advanced Scientific Computing.