Microorganisms play important roles in ecology, biogeochemical cycles, human diseases, bioremediation and bioenergy. Currently, high-throughput sequencing is being used to map the genomes of microbial species. However, the DNA sequence of a microbe does not provide a complete understanding of its functioning. To bridge the knowledge gap between genotype and phenotype, metabolic flux analysis is an important phenomic tool to investigate in vivo enzymatic activities. Analysis of metabolic fluxes can identify bottleneck pathways in the biosynthesis of desirable products, decipher the function of unknown genes, discover new enzymes, and reveal the mechanisms of diseases. In this project, a user-friendly metabolic flux analysis platform will be developed to provide a robust cyberinfrastructure for biologists, helping them analyze large amounts of phenomic data efficiently. In addition, this platform includes an open source database for storing and disseminating flux analysis data on diverse microbial metabolisms. This database can assist metabolic flux analyses of new microorganisms, enabling the systems biology community to benefit from the fast advancement in "Big Data" technology. Ultimately, this platform can be a springboard for future development of high throughput methodologies to analyze diverse biological systems. The broader impact of this project not only includes educational efforts for high school students (especially minority and under-represented groups) to promote their inquiry and interests in STEM fields, but also development of a wiki-styled website and discussion forum for flux analysis technologies and applications.
Current systems biology studies (for example, transcriptomics) rely on model organisms for genome annotation and have limited power to reveal novel metabolic pathways. In addition, post-transcriptional and post-translational regulations hinder the possible phenotypic information that can be determined from genomic approaches. Thereby, it is of great value to build a new metabolic flux analysis platform to decipher microbial metabolisms and metabolic regulations. This project has three tasks to develop novel capabilities for metabolic flux analysis. The first task will be to build a comprehensive carbon-fate map for 13C-assisted pathway identification and to provide effective computational algorithms for 13C-metabolic flux analysis. Thereby, this flux analysis platform can precisely quantify the pathway activities using the labeling information from 13C-tracer experiments. The second task will be to build a database of published microbial fluxomic results, which can then be analyzed via data mining approaches to predict pathway linkages and novel microbial metabolisms in new species. The third task will be to create new approaches to integrate genome-scale flux balance analysis with both 13C-labeling and data mining results to precisely characterize microbial metabolisms. Once the new model platform is developed, it will be tested using case studies. Based on available experimental data on Shewanella metabolic flux analyses, the model applicability will be validated and improved. Ultimately, this new platform can be widely used by the systems biology society to provide new insights into diverse microbial species. The project web link is: http://tang.eece.wustl.edu/
