Methodologies for uncovering hidden information at molecular and cellular scales
Abstract/Contents
- Abstract
- The reductionism of molecular biology has been incredibly fruitful and instrumental in scientific advancement. The advent of high-throughput technologies and improved measurement techniques has allowed systems biology and quantitative biology to further revolutionize biology in the last half-century. A quantitative, systems biology approach allows scientists to uncover unknown unknowns by performing large-scale experiments in an unbiased manner. For example, DNA/protein sequencing, growth curve measurements, and microscopy are information rich, and the latter two are highly tunable to interrogate the relationship between environment and physiology. However, to fully exploit these advances, it is important to optimize these precise, quantitative measurements and understand how to interpret the data. Furthermore, it has become increasing important and feasible to develop algorithms to exploit the large amount of data that is being generated. This dissertation describes two such methodological advances that involve new methods for disentangling signal from noise. Chapter 2 describes a novel framework for analyzing protein coevolution to gain functional understanding of a protein. Chapter 3 describes refined methods for optimizing the measurement of bacterial growth parameters by carefully considering cellular physiology and environmental conditions. Chapter 4 provides concluding thoughts on future directions for using these methods. Taken together, my PhD work showcases the importance of developing methods to link molecular and structural biology with the ongoing systems and quantitative biology revolution
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Atolia, Esha |
|---|---|
| Degree supervisor | Huang, Kerwyn Casey, 1979- |
| Thesis advisor | Huang, Kerwyn Casey, 1979- |
| Thesis advisor | Jarosz, Daniel |
| Thesis advisor | Wang, Bo, (Artificial intelligence scientist) |
| Thesis advisor | Altman, Russ |
| Degree committee member | Jarosz, Daniel |
| Degree committee member | Wang, Bo, (Artificial intelligence scientist) |
| Degree committee member | Altman, Russ |
| Associated with | Stanford University, Department of Chemical and Systems Biology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Esha Atolia |
|---|---|
| Note | Submitted to the Department of Chemical and Systems Biology |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Esha Atolia
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...