Development and application of mass spectrometry-based metabolomics methods for disease biomarker identification

Human societies face diverse health challenges including a rapidly aging population, rising incidence of metabolic disease, and increasing antibiotic resistance. These problems involve complex interactions between genes and environment and are often not well understood. To address these challenges, high-throughput and reproducible advances in genome sequencing, transcript measurement, and protein measurement have been developed; the information resulting from these techniques has led to an increased understanding of cellular function and the identification of number of novel biomarkers for a variety of diseases.In recent years, the monitoring of such systems-level cellular behavior has naturally extended to the metabolite level, leading to the study of metabolomics. The rise of metabolomics corresponds hand in hand with the desire to address some of the phenotypic informational gaps left behind from genomics, transcriptomics, and proteomics. The study of metabolites carries several advantages. First, the number of metabolites in the human "metabolome," estimated at 2500 metabolites, remains a tractable number for analysis as compared to the 35,000 genes and 100,000-1,000,000 proteins. Metabolites also reliably provide an instantaneous "downstream" biochemical snapshot of a cell, and the typical metabolomics analysis is carried out on relatively noninvasive patient fluids such as urine or plasma.The goal of this thesis is to design, develop, and apply methods for the metabolomic analysis of blood via gas chromatography-mass spectrometry (GC-MS) instrumentation. Despite initial successes, methods in metabolomics vary widely and have not been standardized. This was first addressed via the optimization of the instrumentation itself, a topic rarely addressed in the literature but crucial toward the reliable identification of biomarkers.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Tong, Lily Victoria

Advisor dc:contributor.advisor

• Gregory N. Stephanopoulos.

• Chemical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/46014

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/46014