Rational design of ribozyme switches through big data and machine learning
Abstract/Contents
- Abstract
- RNA switches are a broad class of functional RNAs that support conditional regulation of gene expression. These genetic switches have broad application in creating biological systems responsive to changes in their environment - a situation that occurs frequently in the fields of metabolic engineering, environmental sensing, and therapeutics. These switches gain their function from both their structural conformation and their sequence of functional regions, implying that an improved elucidation of relationships between structure, sequence, and function can be used to improve our capacities for de novo design. One type of RNA switch is the ribozyme switch, which changes its self-cleavage activity based on the presence of the target ligand. Here we leverage sequencing data and machine learning to relate the sequence and structure of biological molecules to their function and develop models that predict the function of novel ribozyme switches. In the course of our work, we generated data on the activity of hundreds of thousands of ribozyme switch sequences. Using automated structural analysis and machine learning, we leveraged this wealth of sequence data to develop predictive models that forecast the in vivo activity of a functional RNA sequence using the sequence of functional regions. We have used these models for the de novo design of novel ribozyme switches that exhibit changes in expression activity upon introduction of a target ligand. Our work demonstrates how to use computational tools to turn RNA sequences into quantitative features that represent important sequence and structural motifs and how to use those features to develop models that can learn how the interactions of these motifs translates to device function. We have integrated the model for predicting switch activity into design software that will provide a broadly accessible tool for designing custom ribozyme switches.
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 | Schmidt, Calvin Mackenzie |
|---|---|
| Degree supervisor | Smolke, Christina D |
| Thesis advisor | Smolke, Christina D |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Das, Rhiju |
| Degree committee member | Altman, Russ |
| Degree committee member | Das, Rhiju |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Calvin M. Schmidt. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Calvin Mackenzie Schmidt
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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