Computational methods for multi-scale biological imaging and modeling

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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.


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


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


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.

Access conditions

© 2022 by Po Nan Li

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