Grants and Contracts Details
Ultra-accurate-mass and high-resolution Fourier transform mass spectrometry (FTMS) applied in stable isotope-resolved metabolomics (SIRM) experiments can generate detailed isotopologue features representing isotopic flux through cellular and systemic metabolism for thousands of metabolites from cells, tissue, and biofluids. The challenge now is in analyzing this data to derive new biological knowledge and in making this data and knowledge available. However, data analysis methods for state-of-the-art technologies are lacking. Our project will address this technical gap by developing new data analysis tools that enable effective analysis, integration, and interpretation of untargeted SIRM and non-SIRM analytical data collected from high-end instrumentation. The proposed tools development will be accomplished with the following objectives: O1. Develop peak characterization, assignment, disambiguation, and quality assessment tools for metabolomics spectral data; O2. Develop SIRM-based modeling and omics integration tools for metabolism-aware interpretation; and O3. Enable comprehensive capture, deposition, and reuse of metabolomics experiments. The proposal will develop novel tools that effectively analyze untargeted SIRM and non-SIRM data; tools that effectively integrate SIRM-derived path and flux information with other omics-derived information and organized knowledge, enabling metabolism-aware interpretation within the context of specific biological processes; and highly reusable libraries enabling comprehensive capture, deposition, and reuse of metabolomics data. Intellectual Merit: This proposal will advance scientific knowledge by enabling a wider range of basic biological science that comprehensively characterizes active metabolism and elucidates specific metabolic and regulatory mechanisms that create the resulting metabolic phenotypes. This proposal has several major innovations emanating from each objective, but the overall innovation comes from integrating many proofs-of-concept with established tools into highly untargeted metabolomics analyses that are robust. Also, O1 will develop a novel multi-scan peak characterization method that properly handles multiple data quality issues present in FTMS spectra, minimizing intra-scan variance while removing spectral artifacts that are dangerous to downstream data analyses. O1 will also develop a singularly unique tool implementing Small Molecule Isotope Resolved Formula Enumeration (SMIRFE), which will provide a truly untargeted assignment of FTMS peaks without using a database of known/expected metabolites. SMIRFE performs an extremely efficient search of huge isotope-resolved molecular formula search spaces, enabling characterization and isotopic enrichment utilization of unknown metabolites. Furthermore, we are developing assignment power analyses that fundamentally improve metabolomics experiment designs. O2 will develop a novel metabolic network placement method that utilizes SIRM isotopologue data for robust metabolite placement. But the larger innovation of O2 will come in the development of an interoperable set of omics integration tools centered on a comprehensive atom-resolved interaction network. This integration will allow cell/tissue-specific and subcellular-specific metabolite network placement, modeling, and interpretation. O3 will develop libraries and tools that create conformant depositions to an evolving standard of metadata quality, with isotope-resolved IUPAC International Chemical Identifiers (InChI). Broader Impacts: Metabolism represents the active cellular and systemic state of an organism and an ecosystem. Therefore, the comprehensive characterization and interpretation of metabolism afforded by this proposal will have broad impact in the following specific ways: (1) Provide new metabolism research infrastructure that will be impactful for non-model organisms. (2) Broadly disseminate highly-reusable, fully-documented, production-level tools with web-based graphical user interfaces (GUIs), command-line interfaces (CLIs), and application programming interfaces (APIs) via GitHub, PyPI, and Bioconductor. (3) Train approximately 10 high school, undergraduate, and graduate students from many STEM disciplines with multidisciplinary systems biology research projects using interdisciplinary approaches to research. (4) Actively recruit women and under-represented minorities (URMs) into multidisciplinary computational sciences via participation in this research project. Prior lab recruitment has included 28 women and 4 URMs
|Effective start/end date||8/15/20 → 7/31/24|
- National Science Foundation: $1,163,869.00
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