Improved prediction of biodegradation pathways: visualization and performance.

Loading...

View/Download File

Gao_umn_0130E_11781.pdf (1.28 MB)

Persistent link to this item

http://purl.umn.edu/104515

Statistics

View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Improved prediction of biodegradation pathways: visualization and performance.

Authors

Gao, Junfeng

Published Date

2011-02

Publisher

Type

Thesis or Dissertation

Abstract

The University of Minnesota Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts plausible pathways for microbial degradation of organic compounds. Its biotransformation rules are based on reactions found in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) or in the scientific literature. Since the UM-PPS was created in 2002, its rule base has grown to 275 entries. The original system predicted one level of prediction at a time. It provided a limited view of prediction results and heavily relied on manual interventions. It matched the query compound with all biotransformation rules one by one, which was a time-consuming process. In 2008, the two-level visualization was first implemented to allow users to view two levels of predictions at a time. However, this visualization approach was usually not able to show the complete metabolism of a query compound, and users still needed expert knowledge to make educated choices to continue the prediction. In 2009, we started to develop a multi-level visualization and, simultaneously, work on increasing prediction speed. In 2010, the multi-level visualization was implemented to predict up to six levels of predictions at a time. Not only more products, but also common intermediates and cleavage products are displayed. Users can view prediction alternatives much more easily in a tree-like interactive graph. A multi-level prediction can be computationally intensive and requires users to wait longer than desired for the prediction results. Therefore, we used a multi-thread computing strategy that decreased the prediction run-time by half. We balanced the computing threads and pre-loaded all UM-PPS database tables to permit quick access to its data. Both of these improvements resulted in an additional 30% decrease in prediction run-time. We conducted a simulation study and used another web server to reduce the queuing interference by over 85%. Beta testers were satisfied with its visualization and performance. The above improvements lead to a smarter and faster UM-PPS that has continued its growth in the past 4 years. It now displays better graphical results and predicts biodegradation pathway in a timely manner.

Keywords

Biodegradation

Pathway prediction

Pathway visualization

Health Informatics

Description

University of Minnesota Ph.D. dissertation. February 2011. Major: Health Informatics. Advisor: Lynda B.M. Ellis. 1 computer file (PDF); viii. 92 pages, appendix A.

Related to

Replaces

License

Collections

Dissertations

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Gao, Junfeng. (2011). Improved prediction of biodegradation pathways: visualization and performance.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/104515.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.