Quantitative models of RNA thermodynamics for the design of riboswitches
Abstract/Contents
- Abstract
- RNA has recently emerged as the ideal candidate as a building block to create the next generation of molecular medicines, with the potential to specifically disable or manipulate the genes involved in disease. RNA's functional versatility is exemplified by the development of myriad novel RNA-based synthetic biology tools, including virus-detecting RNA devices, CRISPR/Cas9 genome editing, and RNA silencing. The ability to design RNA elements is central to gaining precise control over these systems and customizing them for use as RNA-based medicines. However, doing so relies upon a thorough understanding of the energetics of RNA interactions and computational models has proven to be inadequate to quantitatively account for the biophysical behavior of RNA molecules. This lack of predictive models has been a major barrier to the advancement of RNA-based technologies. Fortunately, modern high-throughput experimental techniques have enabled us to collect massive datasets of biophysical parameters, and recent innovations in deep learning have shown unprecedented power in the extraction of relevant features from such datasets. Herein, I present work that combines high-throughput datasets with deep learning models to advance quantitative models that enable better riboswitch design. First, I introduce a method that enables the design of a diverse range of riboswitches, allowing for varied ligand types and flexible constraints. Second, I describe a recurrent neural network model that quantitatively models the energetics governing RNA-RNA interactions to facilitate evaluation of candidate riboswitch sequences. Finally, I demonstrate a convolutional neural network model for directly modeling RNA motif energies to enable generalization to all biophysical predictions of RNA's behavior. Together, these results demonstrate new computational approaches to modeling the thermodynamics of RNA and enable new routes to the design of riboswitches for a new generation of RNA biotechnological and biomedical techniques.
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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wu, Michelle Jennifer |
|---|---|
| Degree supervisor | Das, Rhiju |
| Thesis advisor | Das, Rhiju |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Greenleaf, William James |
| Degree committee member | Altman, Russ |
| Degree committee member | Greenleaf, William James |
| Associated with | Stanford University, Program in Biomedical Informatics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Michelle Wu. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Michelle Jennifer Wu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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