Hardware and software tools to enable the study of sequence-function relationships
Abstract/Contents
- Abstract
- Although humanity has become adept at sequencing nucleic acids and proteins, our ability to predict the consequences of mutations remains limited, as does our ability to design proteins from basic biophysical principles. Inexpensive whole genome sequencing enables us to rapidly identify mutations, but we are yet to realize the full promise of precision medicine: the capacity to predict the significance of novel mutations and, when necessary, respond with targeted treatment. Likewise, we struggle to improve upon natural protein catalysts, keeping designer enzymes, which would find applications from industrial chemistry to environmental remediation, out of reach. What we need are accurate, comprehensive models of how any given biological sequence interacts with itself and its environment to result in function (i.e. binding or catalysis). Since biological sequence space is untraversable in size and resistant to efforts to reduce its complexity, such a model will need to integrate over a far wider range of non-linear factors than any human mind can negotiate. Training these high-capacity computer models will require commensurately high-throughput generation of data that relate biological sequence to quantitative measurements of function. Towards this greater endeavor, my dissertation work contributes a collection of software and hardware to enable high-throughput in vitro functional measurements of large nucleic acid and protein sequence libraries. To aid in the initial planning of massively parallel binding assays, I have developed notebook-based simulations in which integrated systems of ordinary differential equations are visualized to inform experimental duration and library membership. Next, to plan the preparation of large libraries themselves, I have developed opTile, a graph-based approach to determine optimal tilings of biological sequences, particularly for the purpose of high-throughput site-directed mutagenesis. Finally, high-throughput experiments require not just software but also hardware to manipulate physical samples and reagents. My work towards this end is represented in micrIO, an automated platform for microfluidic input-output which can serially introduce samples in our macro world onto microfluidic devices while collecting resulting effluents.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Longwell, Scott Alden |
|---|---|
| Degree supervisor | Fordyce, Polly |
| Thesis advisor | Fordyce, Polly |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Lundberg, Emma O. (Emma Octavia) |
| Degree committee member | Altman, Russ |
| Degree committee member | Lundberg, Emma O. (Emma Octavia) |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Scott Longwell. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/vv008gj9429 |
Access conditions
- Copyright
- © 2023 by Scott Alden Longwell
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