The construction and experimental validation of a computational whole-cell model of Mycoplasma genitalium
- Significant progress has been made in experimentally discovering and understanding the molecular mechanisms of various cellular processes, from metabolism to cell division. However, integrating this knowledge into a comprehensive understanding of cellular physiology remains a challenge. We have attempted to synthesize the scientific community's knowledge of cell biology into one system by building the first computational model of the life cycle of a single cell. Our model describes Mycoplasma genitalium, the simplest known self-replicating organism. The model accounts for all known gene functions and molecular interactions. This "whole-cell" model provides a better understanding of basic cellular physiology and cell-to-cell variation. Furthermore, this model can be used to make systems level predictions and biological discoveries that would not have been possible without this integrated view of a cell. In order to represent all of the known gene functions of M. genitalium, we divided the genes into 28 functional groups describing cellular processes such as replication, transcription, translation, metabolism, supercoiling, and cytokinesis. We developed independent computational models for each of these cellular processes using the mathematical representation best fit for the given process, such as linear optimization, ordinary differential equations, and probabilistic and stochastic methods. To integrate the system, information was passed between these sub-modules at each second of the simulated cell cycle. Data and parameters for the model were acquired from hundreds of publications in the literature. The model was fit, benchmarked, and tested such that the cell grows and divides according to our understanding of cell physiology. The whole-cell model outputs the counts, actions, and interactions of every molecule at every time point of the cell cycle. It has made novel predictions about various aspects of cellular biology including protein occupation of the chromosomes, energy usage, and non-transcriptional forms of cell-cycle regulation. We performed an experimental study, measuring the growth rates of single-gene disruption M. genitalium strains, and found that 84% of the model predicted growth rates matched the experimental results, thus validating the predictive power of the model. The remaining 16% of growth rates indicated misrepresentations in the model--opportunities for biological discovery. We were able to predict biological behavior that would reconcile most of these discrepancies, and in three cases the model was able to predict refined kinetic parameters of compensatory metabolic reactions in the system. We performed kinetic assays to validate the accuracy of all three self-refining model predictions. This thesis presents the first gene-complete model of an organism that has been experimentally validated. Using the model to guide and support future experimentation, we hope to continue to discover previously unknown cellular physiology. Overall, the whole cell model enables a view of the entire inner workings of a cell, an integrated understanding that is difficult to achieve by experimentation alone. We hope that expansions of this model will continue to enable discovery of cellular biology, will increase our understanding of prokaryotes and higher organisms, will elucidate multifaceted behaviors like complex disease states, and will serve as predictive tools to guide synthetic biology.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Sanghvi, Jayodita Chetan
|Stanford University, Department of Bioengineering.
|Bryant, Zev David
|Swartz, James R
|Bryant, Zev David
|Swartz, James R
|Statement of responsibility
|Jayodita Chetan Sanghvi.
|Submitted to the Department of Bioengineering.
|Ph.D. Stanford University 2013
- © 2013 by Jayodita Chetan Sanghvi
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...