Full metadata
Title
Dense non-natural sequence peptide microarrays for epitope mapping and diagnostics
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
Date Created
2014
Contributors
- Richer, Joshua Amos (Author)
- Johnston, Stephen A. (Thesis advisor)
- Woodbury, Neal (Committee member)
- Stafford, Phillip (Committee member)
- Papandreou-Suppappola, Antonia (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xi, 210 p. : col. ill
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.26879
Statement of Responsibility
by Joshua Amos Richer
Description Source
Retrieved on Jan. 16, 2015
Level of coding
full
Note
Partial requirement for: Ph.D., Arizona State University, 2014
Note type
thesis
Includes bibliographical references (p. 170-182)
Note type
bibliography
Field of study: Biological design
System Created
- 2014-12-01 07:07:34
System Modified
- 2021-08-30 01:31:59
- 2 years 8 months ago
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