NSF Award
1262637
The Institute of Systems Biology is awarded a grant to apply principles of systems biology and the power of parallel computing to the advancement of computational and experimental methods that can very rapidly delineate the gene networks in any organism. By integrating genome sequence analysis with the analysis of large amounts of genome-wide ("high-throughput") experimental data, the computational methods being developed as part of this project will reverse engineer the network models. These models will be predictive and will have extraordinary detail (down to the level of biomolecular mechanisms) that could drive rational improvements in the gene networks, with predictable outcomes. It will also uncover fundamental principles underlying the ability of even the simplest microbes to deal with complex environmental changes. While the work will be performed on two organisms - E. coli, a well-known, widely studied bacterium, and Halobacterium salinarum, an extremophile that thrives in high salt environments, it is readily applicable to all sequenced bacteria, algae, and other organisms that are of huge industrial, agricultural, and medical importance. <br/><br/>In addition to training a postdoctoral fellow and a graduate student in the methodology of systems modeling, this project will develop inquiry-driven, standards-based, high school (HS) educational materials. These education materials will incorporate concepts and methods for inferring and mathematically representing (modeling) a system as a network of interacting parts, which will help students understand and critically assess complex and important phenomena, e.g. how climate change can reach a tipping point to have cascading effects throughout an ecosystem. The education materials will incorporate a systems approach, and will be developed together with local science educators. While the goal of the module will be to educate students in various methods for statistical modeling and model inference, it will also attempt to reinforce the notion that computers do not always have the right answer, that models are only as good as the data they are based upon, and that statistical confidence of predictions must be rigorously assessed to avoid wrong conclusions. The scientists funded by this project will participate in development of educational materials through direct interactions with HS students and educators. For further information about this project and its products visit the Institute?s website at https://www.systemsbiology.org.
