The new high-throughput sequencing technologies have led to a rapid accumulation of genomic data across a great diversity of organisms. Those data can now be used to build models that describe how genome variation is translated into phenotypic differences. This research will build a new computational framework to combine all of the genomic variants and simulate the resulting metabolic phenotypes. This framework will be broadly applicable, so it can analyze any group of organisms, and will include good documentation for the user-friendly interface, designed to be immediately accessible instead of requiring extensive training. The project will develop an online game based on the research framework and data that will be used to educate students and interested members of the public about the basic concepts of metabolic activities and metabolic evolution. Graduate students who have developed applications in the advanced framework will advise undergraduate students, who will be recruited from the University of Rhode Island's Seed of Success (SOS) program for summer internships, and who will develop and test the game interfaces. Finally, the educational game will be used during the two-day Engineering Challenges in the Science and Math Investigative Learning Experiences (SMILE) Program to provide an interactive learning experience for high-school students. The computational tools and the educational gaming system will be released through public web servers and software repositories, and the research outcomes from this project will be disseminated through open-source documentations, journal publications, and conference presentations.
The genome-scale models (GEMs) of metabolic networks have broad applications in phenotype prediction, evolutionary reconstruction, functional analysis, and metabolic engineering. Despite the reconstruction of over 100 GEMs in public literature, significant challenge remains in integrating the heterogeneous information about genes, proteins, reactions and metabolites into a comprehensive understanding of the associations between genome diversity and metabolic plasticity. To solve this problem, this research will focus on constructing a new computational infrastructure that combines the simulation of GEMs with the analysis of pan-genomes (i.e. an ensemble of genomes from any given group of organisms). The computational infrastructure will include a user-friendly interface that supports the collaborative construction, annotation, and quality checking of metabolic models. It will also enable the identification of genome-wide metabolic variations by providing new algorithms for GEM comparison and pan-genome analysis. Specifically, this project will focus on achieving three aims: (1) implement a new data format that will integrate heterogeneous information into the construction of metabolic models; (2) build a computational tool that will support the comparative analysis of metabolic networks; (3) develop a new algorithm that will improve the efficiency of ortholog mapping in pan-genome analysis and permit the comparison of metabolic variants among different organisms. More information about this project can be found at https://zhanglab.github.io/psamm/.
