Probabilistic models for understanding regulation of translation
Abstract/Contents
- Abstract
- The process of translation, whereby RNA is converted to protein, is an essential biosynthetic process requiring a large fraction of the cell's resources. However, our understanding of the regulatory mechanisms at this stage of gene expression is limited. Recent high-throughput experimental techniques and our development of probabilistic models for their analysis have allowed us to better explore translation efficiency, codon preferences, and mRNA secondary structure, as well as the interplay between these factors. In this work, we first present a queuing-theory-based probabilistic model for ribosome profiling data to extract robust estimates of protein synthesis rates and translation rates per codon, which can vary across individual genes. We use this model to show that local rates and translation efficiency are not affected by manipulations of tRNA abundance in physiological conditions in yeast; this reverses the direction of causality previously assumed to hold. Instead, we propose that initiation sequence signals, such as mRNA structure, could drive translation. To further understand varying translation rates, we also apply this model to human cells and present results on allele-specific ribosome pausing. Second, we delve deeper into RNA structure, which is important more broadly throughout the pipeline of protein expression and in many aspects of regulation control. However, accurately determining RNA structure at large scale is difficult with only experimental data or algorithmic methods. We present a conditional log-linear model that can incorporate information from multiple structure probing assays, and, although limited by the data quality, improves prediction accuracy over leading algorithms. Our method can also be used to derive new insight into biological processes influenced by RNA structure, such as translation.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Pop, Cristina |
|---|---|
| Associated with | Stanford University, Department of Computer Science. |
| Primary advisor | Koller, Daphne |
| Thesis advisor | Koller, Daphne |
| Thesis advisor | Batzoglou, Serafim |
| Thesis advisor | Weissman, Jonathan |
| Advisor | Batzoglou, Serafim |
| Advisor | Weissman, Jonathan |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Cristina Pop. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Cristina Pop
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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