Topic 364: SysMet: Integrative Systems Metabolomics

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 261201700041C-3-0-0
Agency Tracking Number: N43CA170041
Amount: $49,992.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NCI
Solicitation Number: N/A
Timeline
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
2917 Georgia Ave NW, WASHINGTON, DC, 20001-3805
DUNS: 080160727
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Mohammad R. Nezami Ranjba
 (703) 504-8158
 nranjbar.m@gmail.com
Business Contact
 Mohammad R. Nezami Ranjba
Phone: (703) 504-8158
Email: nranjbar.m@gmail.com
Research Institution
N/A
Abstract
Metabolomics plays an indispensable role in the growing systems biology approaches to identify reliable cancer biomarkers. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high-throughput comparison of the levels of thousands of metabolites among biological samples. However, the potential values of many disease-associated analytes discovered by these platforms have been inadequately explored in systems biology research due to lack of computational tools. Partly due to these limitations, poor reproducibility of previously identified metabolite biomarker candidates has been observed, especially when they are evaluated through independent platforms and validation sets. This project aims to address this challenge using a new software tool (SysMet) that utilizes network-based approaches for: (1) prioritizing putative IDs to assist in metabolite identification; (2) performing differential analysis to uncover relationships between disease and metabolites by investigating the rewiring and conserved interactions among metabolites in the progression of the disease; and (3) integrating metabolomic data with transcriptomic, proteomic, and glycomic data to identify highly promising metabolites as biomarker candidates. The tool will contribute to improving the ability of researchers to discover more reliable biomarkers by enhancing the role of metabolomics in systems biology research.

* Information listed above is at the time of submission. *

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