Methods to visualize, dissect and simulate complex cellular behaviors
Abstract/Contents
- Abstract
- When we consider a problem, like a disease, our instinct is to find the source of the issue and fix it. For some diseases we encounter this is possible because we can identify a single trackable cause of the issue. For example, in the mid-1800's surgeons realized that they could prevent bacterial infections and death in patients by simply washing their hands and wearing gloves during surgery. But for other diseases, like diabetes or schizophrenia the 'cause' is not so evident, and work over time has taught us that these diseases are systemic. It's only through fully understanding the underlying system that we can tackle the disease. Systems large and small have been found to underlie all of biology, not just complex diseases. It's not enough to understand how proteins or pathways or cells work in isolation. If we really want to understand them, we need to piece together how they work as a whole -- as a system. My thesis work is a story of systems on different scales: from a pathway, to a cell, to a population of cells. I will first discuss work we did to understand how increasing stimulus into a pathway is able to produce non-monotonic outputs. Next, I report efforts to experimentally validate a central prediction that arose out of our lab's recent Escherichia coli whole-cell modeling project: sub-generational gene expression. I will then discuss how revelations from this work led to my most recent work, adding operon structure into the whole-cell model. And finally, I will discuss plans we have to experimentally determine the import of this architecture inensuring population survival, and perspectives on the future of model-driven discovery. Although these systems all seem different, they connect through common themes including the presence of emergent phenomena and the need for methods to visualize, dissect and simulate these complex cellular behaviors.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | DeFelice, Mialy Murie |
|---|---|
| Degree supervisor | Covert, Markus |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Bryant, Zev David |
| Thesis advisor | Lin, Michael Z |
| Degree committee member | Bryant, Zev David |
| Degree committee member | Lin, Michael Z |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Mialy DeFelice. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/xz033fw8864 |
Access conditions
- Copyright
- © 2021 by Mialy Murie DeFelice
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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