Re-imagining and expanding the diagnostic toolbox : towards robust and scalable molecular quantification
Abstract/Contents
- Abstract
- Why is it that our local pharmacy only has pregnancy strips and CGMs as molecular monitors? What can we actually monitor at the doctors office reliably, and why not certain biomarkers? These are questions that underlay the body of work presented in this thesis. Particularly, I focus on some of the technical challenges of measuring and monitoring molecular biomarkers. The field of precision medicine is entering a critical phase, with unprecedented access to vast computational resources and diverse data types such as CT scans, X-rays, and electronic health records. These resources, combined with powerful algorithms, have demonstrated significant potential for disease diagnosis and treatment. Unfortunately, molecular information has yet to be used in a scalable and reliable manner, although it is a critical component to understanding disease and health status. As a result, unraveling the basis and respective markers for disease and health remains a daunting task. This thesis highlights and begins addressing some fundamental problems in scaling up the quantification of molecular biomarkers. Particularly, many current solutions assume that the intrinsic affinity between the affinity reagent and the target are the limiting factor, or view background signal and noise as an annoying feature. I will share some of our recent works that take advantage of these aspects to provide more robust and scalable molecular quantification techniques including: A method to tune molecular assays to expand the range of quantification across a larger portion of the proteomic dynamic range, a mathematical framework to relax the selectivity requirements on affinity reagents for assay design, assay development for scalable small molecule measurement, and a way to spatially minimize the effect of non-specific binding. These works take a step towards improving the way and scope of biomarkers that are measured from a fundamental level and have the potential to shift molecular quantification to be more accessible for medical diagnostics.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Newman, Sharon Shin |
|---|---|
| Degree supervisor | Soh, H. Tom |
| Thesis advisor | Soh, H. Tom |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Dunn, Alexander |
| Degree committee member | Covert, Markus |
| Degree committee member | Dunn, Alexander |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sharon Shin Newman. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/wy488jm0042 |
Access conditions
- Copyright
- © 2023 by Sharon Shin Newman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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