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Whole-Colony Modeling of Escherichia coli

View ORCID ProfileChristopher J. Skalnik, View ORCID ProfileEran Agmon, Ryan K. Spangler, Lee Talman, View ORCID ProfileJerry H. Morrison, Shayn M. Peirce, View ORCID ProfileMarkus W. Covert
doi: https://doi.org/10.1101/2021.04.27.441666
Christopher J. Skalnik
1Department of Bioengineering, Stanford University, Stanford, CA 94305
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Eran Agmon
2Department of Bioengineering, Stanford University, Stanford, CA 94305
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Ryan K. Spangler
3Department of Bioengineering, Stanford University, Stanford, CA, United States
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Lee Talman
4Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
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Jerry H. Morrison
5Department of Bioengineering, Stanford University, Stanford, CA 94305
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Shayn M. Peirce
6Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
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Markus W. Covert
7Department of Bioengineering, Stanford University, Stanford, CA 94305,
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  • For correspondence: mcovert@stanford.edu mcovert@stanford.edu
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Abstract

Bacterial behavior is the outcome of both molecular mechanisms within each cell and interactions between cells in the context of their environment. Whereas whole-cell models simulate a single cell’s behavior using molecular mechanisms, agent-based models simulate many agents independently acting and interacting to generate complex collective phenomena. To synthesize agent-based and whole-cell modeling, we used a novel model integration software, called Vivarium, to construct an agent-based model of E. coli colonies where each agent is represented by a current source code snapshot from the E. coli Whole-Cell Modeling Project and interacts with other cells in a shared spatial environment. The result is the first “whole-colony” computational model that mechanistically links expression of individual proteins to a population-level phenotype. Simulated colonies exhibit heterogeneous effects on their environments, heterogeneous gene expression, and media-dependent growth. Extending the cellular model with mechanisms of antibiotic susceptibility and resistance, our model also suggested that variation in the expression level of the betalactamase AmpC, and not of the multi-drug efflux pump AcrAB-TolC, was the key mechanistic driver of survival in the presence of nitrocefin. We see this as a significant step forward in the creation of more comprehensive multi-scale models, and it broadens the range of phenomena that can be modeled in mechanistic terms.

Author summary This work combines several models of molecular and physical processes that impact the physiology and behavior of the common microbe Escherichia coli into a multiscale model. Colonies comprised of multiple individual cells are simulated as they grow and divide—each with complex internal mechanisms, and with physical interactions and molecular diffusion in their environments. The integrative modeling methodology supports the addition of new submodels. The flexibility of this methodology is demonstrated by adding models of antibiotic resistance and simulating the colony’s response to antibiotic treatment.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://doi.org/10.5281/zenodo.4695018

  • https://doi.org/10.5281/zenodo.4697519

  • https://doi.org/10.5281/zenodo.4706184

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 27, 2021.
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Whole-Colony Modeling of Escherichia coli
Christopher J. Skalnik, Eran Agmon, Ryan K. Spangler, Lee Talman, Jerry H. Morrison, Shayn M. Peirce, Markus W. Covert
bioRxiv 2021.04.27.441666; doi: https://doi.org/10.1101/2021.04.27.441666
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Whole-Colony Modeling of Escherichia coli
Christopher J. Skalnik, Eran Agmon, Ryan K. Spangler, Lee Talman, Jerry H. Morrison, Shayn M. Peirce, Markus W. Covert
bioRxiv 2021.04.27.441666; doi: https://doi.org/10.1101/2021.04.27.441666

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