Towards a whole-cell model of growth rate and cell size control in Escherichia coli
Abstract/Contents
- Abstract
- A central challenge in biology is understanding how systems level behaviors arise from their underlying molecular mechanisms. While significant progress has been made characterizing the mechanisms of individual cellular processes, a complete understanding of cell physiology remains an open challenge. Computational models of cell physiology that are both predictive and comprehensive are needed to integrate the scientific community's diverse knowledge into a single computational theory. Using an integrative modeling methodology we have made made significant progress towards a whole-cell computational model of Escherichia coli (E. coli). The model simulates the life cycle of a single E. coli cell growing exponentially, and includes representations of cellular processes like metabolism, transcription, translation, chromosome replication, and transcriptional regulation. Furthermore, the model is implemented with feedback mechanisms that control its cellular composition, growth rate, cell size, chromosome state, transcriptional expression, and metabolic capacity in response to its medium environment. The result is a model that grows for an arbitrary number of cell divisions, adapts to three different medium conditions, and simultaneously enables the observation of the abundance, activity, and interactions of every molecular species in E. coli. We have used the E. coli model as a computational theory to validate diverse experimental datasets and to integrate them into a consistent modeling framework. In this way we were able to show that the varied knowledge of individual cellular processes largely integrate into a single consistent understanding of cell biology in E. coli. We have also demonstrated that the model can guide biological discovery including a new pattern of RNA expression in which the majority of genes in the E. coli model transcribed at a rate of less than once per cell cycle, which leads to proteins being expressed only once in many generations with a higher fold change. This thesis presents a model that represents significant progress towards a whole-cell model of E. coli. We hope that expansions of this model will enable a more complete understanding of cell physiology, enable the construction of whole-cell models of other prokaryotes and higher organisms, and serve as a predictive theory to guide synthetic biology, engineering, and medicine.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ruggero, Nicholas Anthony |
|---|---|
| Associated with | Stanford University, Department of Chemical Engineering. |
| Primary advisor | Covert, Markus |
| Primary advisor | Dunn, Alexander Robert |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Dunn, Alexander Robert |
| Thesis advisor | Spormann, Alfred M |
| Advisor | Spormann, Alfred M |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Nicholas Anthony Ruggero. |
|---|---|
| Note | Submitted to the Department of Chemical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Nicholas Ruggero
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...