Massively parallel interrogation of anti-viral and anti-cancer immunity
Abstract/Contents
- Abstract
- New genome-scale experimental methods and computational advances to extract insights from the resulting data deluge present a unique opportunity to jointly optimize experimental and computational tools to further our understanding of biology. In this thesis I present two vignettes of how the experiment-computation co-design methodology may be applied to two applications, (1) the SARS-CoV-2 pandemic and (2) cancer immunotherapy. In the first work, we identified human proteins that bind the SARS-CoV-2 viral RNA during infection and functionally characterized these proteins using targeted CRISPR/Cas9 screening (Flynn*, Belk* et al, Cell 2021). In the second work, we performed genome-wide CRISPR/Cas9 screens to systematically discover genetic regulators of T cell exhaustion, a key barrier to the efficacy of anti-cancer immunotherapies (Belk et al, Cancer Cell 2022). In vivo single cell transcriptomics paired with CRISPR perturbations (Perturb-seq) and unsupervised learning provide insights into how each perturbation exerts its function and suggests new cancer therapeutics. Experimental testing of a cell therapy based on these findings significantly prolonged survival in mice. In sum, these two examples highlight how experimental and algorithmic methodologies can be jointly optimized to minimize human labor, cost, and optionally other factors, while maximizing usable data output and informing the design of new therapeutics.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Belk, Julia Ann |
|---|---|
| Degree supervisor | Sadigh, Dorsa |
| Degree supervisor | Satpathy, Ansuman |
| Thesis advisor | Sadigh, Dorsa |
| Thesis advisor | Satpathy, Ansuman |
| Thesis advisor | Chang, Howard Y. (Howard Yuan-Hao), 1972- |
| Thesis advisor | Jaiswal, Siddhartha |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Degree committee member | Chang, Howard Y. (Howard Yuan-Hao), 1972- |
| Degree committee member | Jaiswal, Siddhartha |
| Degree committee member | Kundaje, Anshul, 1980- |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Julia Ann Belk. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/kc204ph4619 |
Access conditions
- Copyright
- © 2022 by Julia Ann Belk
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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