A computational whole-cell model of Escherichia coli : reconciliation of heterogeneous datasets and simulation of tRNA aminoacylation
Abstract/Contents
- Abstract
- Whole-cell modeling is an approach for computational simulation of the known inner workings of a living cell. As our lab developed a large-scale model of Escherichia coli in 2020, we also brought to fruition the role of whole-cell modeling as an engineering tool for studying cell biology. By describing the biological processes occurring inside the E. coli cell with mathematical equations and by parameterizing those equations with measurements from literature, we were presented with the opportunity to perform a large-scale cross-evaluation of the data reported by numerous labs over the past decades using the theories describing E. coli cell physiology -- a technique we call Deep Curation. The Deep Curation approach has enabled us to identify intriguing incompatibilities between the measurements used to parameterize the E. coli model; for example, we identified that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that the measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle -- and the cell is robust to this absence. Finally, these data as a whole led to successful predictions of protein half-lives. The E. coli Model provided a foundation upon which new functionalities could be incorporated, in this case tRNA aminoacylation, which facilitated investigations of the inconsistencies between in vitro measurements and in vivo demands concerning tRNA aminoacylation and protein synthesis -- first reported almost forty years ago. We found that in vitro measurements of tRNA synthetase activities were insufficient to support the in vivo demands for protein synthesis, and optimization of tRNA synthetase kinetic parameters yielded kcat estimations that were on average 14.5-fold higher than their highest measurements. Simulating cell growth with perturbed kcat values for individual synthetases enabled us to determine the global impact of these in vitro measurements on cellular phenotypes. For the case of the CysRS synthetase, insufficient kcats caused protein synthesis to be less robust to the natural variability in tRNA synthetase expression in single cells. Surprisingly, insufficient ArgRS kinetic capacity led to catastrophic impacts on cellular phenotype via DNA replication due to a replisome subunit whose translation was biased to Arginine codons. Taken together, the work presented in this dissertation furthers our understanding of incompatibilities amongst the data characterizing E. coli, a model organism of scientific research. The expansion to the E. coli Model reported here advances the depth of mechanistic detail incorporated into the translational machinery and enhances the breadth of potential for generating predictions and accelerating biological discovery. We anticipate that as other functionalities are incorporated into the E. coli Model, similarly remarkable and unexpected phenotypes will continue to emerge.
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 | Choi, Heejo |
|---|---|
| Degree supervisor | Covert, Markus |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Bryant, Zev David |
| Thesis advisor | Puglisi, Joseph D |
| Degree committee member | Bryant, Zev David |
| Degree committee member | Puglisi, Joseph D |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Heejo Choi. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/bj857bs4872 |
Access conditions
- Copyright
- © 2022 by Heejo Choi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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