A statistical framework for extraction of structured knowledge from biological/biotechnological systems

Despite enormous efforts to understand complex biological/biotechnological systems, a significant amount of knowledge has still remained unraveled. However, recent advances in high throughput technologies have offered new opportunities to understand these complex systems by providing us with huge amounts of data about these systems. Unlike traditional tools, these high throughput detection tools: (1) permit large-scale screening of formulations to find the optimal condition, and (2) provide us with a global scale of measurement for a given system. Thus, there has been a strong need for computational tools that effectively extract useful knowledge about systems behavior from the vast amount of data. This thesis presents a comprehensive set of computational tools that enables us to extract important information (called structured knowledge) from this huge amount of data to improve our understanding of biological and biotechnological systems. Then, in several case studies, this extracted knowledge is used to optimize these systems. These tools include: (1) optimal design of experiments (DOE) for efficient investigation of systems, and (2) various statistical methods for effective analyses of the data to capture all structured knowledge in the data. These tools have been applied to various biological and biotechnological systems for identification of: (1) discriminatory characteristics for several diseases from gene expression data to construct disease classifiers; (2) rules to improve plasma absorptions of drugs from high-throughput screening data; (3) binding rules of epitopes to MHC molecules from binding assay data to artificially activate immune responses involving these MHC molecules; (4) rules for pre-conditioning and plasma supplementation from metabolic profiling data to improve the bio-artificial liver (BAL) device;

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Hwang, Daehee, 1971-

Advisor dc:contributor.advisor

• George 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/1721.1/29603

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29603