Tracking resilience to infections by mapping disease space
Abstract/Contents
- Abstract
- In ecology, resilience is the ability of a system to absorb a change and still maintain the same function, structure, and identity (Holling, 1973). I wanted to study resilience in response to infections, where I define resilience as the ability of a host to return to its original health. Here I explore resilience by monitoring the full course of an infection and plot the path that infected individuals take through "disease space." Disease space is a multi-dimensional universe with quantitative measurements of disease symptoms for axes. I refer to a host's trajectory through disease space as a "disease map." These maps allow us to observe the paths hosts take when they follow a successful outcome or when they shift towards permanent disability or death. Ultimately, I would like to learn how to control the shape of these trajectories to help improve health. The work in this thesis takes the first steps towards this goal by characterizing and quantifying the differences between resilient and non-resilient paths. To study host resilience, I followed the effects of an acute infection on host health. I monitored mice infected with the non-lethal malaria parasite, P.chaubaudi, and created disease maps with the physiological and transcriptional data collected overtime. I used nearest neighbor and topological data analysis to cluster the multi-dimensional data and disease maps using either temporal or non-temporal data. I compared and quantified the different routes taken by resilient and non-resilient mice by transforming the trajectories into polar coordinates, centering the data, and obtaining an angle and radius to describe each sample. This transformation let me measure deviations from the path by comparing radii between survivors and non-survivors. Using this approach, I determined that hosts with large radii were more likely to die. I applied these ideas to an existing data set of humans diagnosed with malaria and found that human malaria patients who are heterozygotes for the sickle cell trait also took a smaller loop in this disease space suggesting that they are resilient against malaria. This observation suggests that our disease maps could be used to identify resilient patients using cross sectional data. I applied a similar framework in studying the resilience of the vaginal microbiota. I present some preliminary work that suggests a new way of visualizing longitudinal microbiota data.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Torres, Brenda Yvonne | |
|---|---|---|
| Associated with | Stanford University, Program in Immunology. | |
| Primary advisor | Schneider, David (David Samuel) | |
| Thesis advisor | Schneider, David (David Samuel) | |
| Thesis advisor | Boothroyd, John C | |
| Thesis advisor | Relman, David A | |
| Thesis advisor | Sonnenburg, Justin, 1973- | |
| Advisor | Boothroyd, John C | |
| Advisor | Relman, David A | |
| Advisor | Sonnenburg, Justin, 1973- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Brenda Yvonne Torres. |
|---|---|
| Note | Submitted to the Program in Immunology. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Brenda Yvonne Torres
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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