Computational methods for multi-scale biological imaging and modeling
Abstract/Contents
- Abstract
- Advances of microscopic technologies and imaging instrumentation have enabled several major discoveries in biology, many of which would have been impossible without the assistance of computational techniques. On the other hand, while the resulting images carry abundance of information for human eyes, even more insights might be revealed by using advanced analysis or images-based modeling, which are made possible by computational approaches. In this dissertation, we demonstrate how computational methods can assist biological studies with multi-scale tools by presenting a series of projects that utilize computational tools to enhance, make use of, or extract information from images. We first propose a computational imaging method that is capable of combining diffraction and lens-based images to deliver images with higher resolution. We then discuss how a diffusion-reaction model can help us reveal the extraordinary capacity of nutrient acquisition of surface layers, a family of crystalline proteins that are found in many bacteria and most of archaea. Finally, we report a machine learning pipeline that can build an atomic protein model into a cryogenic electron microscopy map in a fraction of a minute. Taken together, these methods demonstrate how the interplay of imaging, modeling and machine learning can enable new discoveries for structural biology, and pave paths toward more automatic and efficient structure determination.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Li, Po Nan |
|---|---|
| Degree supervisor | Wakatsuki, Soichi |
| Thesis advisor | Wakatsuki, Soichi |
| Thesis advisor | Howe, Roger Thomas |
| Thesis advisor | Pianetta, Piero |
| Thesis advisor | Van Den Bedem, Henry |
| Degree committee member | Howe, Roger Thomas |
| Degree committee member | Pianetta, Piero |
| Degree committee member | Van Den Bedem, Henry |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Po Nan Li. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/bv075fc6718 |
Access conditions
- Copyright
- © 2022 by Po Nan Li
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