In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 6 ( 2023-6-16), p. e1011232-
Abstract:
Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli . A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in “whole-cell” modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the “whole-colony” scale, we embedded multiple instances of a whole-cell E . coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E . coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1011232
DOI:
10.1371/journal.pcbi.1011232.g001
DOI:
10.1371/journal.pcbi.1011232.g002
DOI:
10.1371/journal.pcbi.1011232.g003
DOI:
10.1371/journal.pcbi.1011232.g004
DOI:
10.1371/journal.pcbi.1011232.t001
DOI:
10.1371/journal.pcbi.1011232.s001
DOI:
10.1371/journal.pcbi.1011232.s002
DOI:
10.1371/journal.pcbi.1011232.s003
DOI:
10.1371/journal.pcbi.1011232.s004
DOI:
10.1371/journal.pcbi.1011232.s005
DOI:
10.1371/journal.pcbi.1011232.s006
DOI:
10.1371/journal.pcbi.1011232.s007
DOI:
10.1371/journal.pcbi.1011232.s008
DOI:
10.1371/journal.pcbi.1011232.s009
DOI:
10.1371/journal.pcbi.1011232.s010
DOI:
10.1371/journal.pcbi.1011232.s011
DOI:
10.1371/journal.pcbi.1011232.s012
DOI:
10.1371/journal.pcbi.1011232.s013
DOI:
10.1371/journal.pcbi.1011232.r001
DOI:
10.1371/journal.pcbi.1011232.r002
DOI:
10.1371/journal.pcbi.1011232.r003
DOI:
10.1371/journal.pcbi.1011232.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
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