---
_id: '8997'
abstract:
- lang: eng
text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable
history in physics but are still scarce in biology. This situation restrains predictive
theory. Here, we build on bacterial “growth laws,” which capture physiological
feedback between translation and cell growth, to construct a minimal biophysical
model for the combined action of ribosome-targeting antibiotics. Our model predicts
drug interactions like antagonism or synergy solely from responses to individual
drugs. We provide analytical results for limiting cases, which agree well with
numerical results. We systematically refine the model by including direct physical
interactions of different antibiotics on the ribosome. In a limiting case, our
model provides a mechanistic underpinning for recent predictions of higher-order
interactions that were derived using entropy maximization. We further refine the
model to include the effects of antibiotics that mimic starvation and the presence
of resistance genes. We describe the impact of a starvation-mimicking antibiotic
on drug interactions analytically and verify it experimentally. Our extended model
suggests a change in the type of drug interaction that depends on the strength
of resistance, which challenges established rescaling paradigms. We experimentally
show that the presence of unregulated resistance genes can lead to altered drug
interaction, which agrees with the prediction of the model. While minimal, the
model is readily adaptable and opens the door to predicting interactions of second
and higher-order in a broad range of biological systems.
acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation
(to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and
P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation
(DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)
Collaborative Research Centre (SFB) 1310 (to T.B.). '
article_number: e1008529
article_processing_charge: Yes
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic
action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical
model of combined antibiotic action. PLOS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1008529
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical
Model of Combined Antibiotic Action.” PLOS Computational Biology. Public
Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of
combined antibiotic action,” PLOS Computational Biology, vol. 17. Public
Library of Science, 2021.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined
antibiotic action. PLOS Computational Biology. 17, e1008529.
mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.”
PLOS Computational Biology, vol. 17, e1008529, Public Library of Science,
2021, doi:10.1371/journal.pcbi.1008529.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).
date_created: 2021-01-08T07:16:18Z
date_published: 2021-01-07T00:00:00Z
date_updated: 2025-06-12T06:33:18Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1008529
external_id:
isi:
- '000608045000010'
pmid:
- '33411759'
file:
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checksum: e29f2b42651bef8e034781de8781ffac
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creator: dernst
date_created: 2021-02-04T12:30:48Z
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has_accepted_license: '1'
intvolume: ' 17'
isi: 1
keyword:
- Modelling and Simulation
- Genetics
- Molecular Biology
- Antibiotics
- Drug interactions
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
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title: Minimal biophysical model of combined antibiotic action
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type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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year: '2021'
...
---
_id: '8097'
abstract:
- lang: eng
text: 'Antibiotics that interfere with translation, when combined, interact in diverse
and difficult-to-predict ways. Here, we explain these interactions by "translation
bottlenecks": points in the translation cycle where antibiotics block ribosomal
progression. To elucidate the underlying mechanisms of drug interactions between
translation inhibitors, we generate translation bottlenecks genetically using
inducible control of translation factors that regulate well-defined translation
cycle steps. These perturbations accurately mimic antibiotic action and drug interactions,
supporting that the interplay of different translation bottlenecks causes these
interactions. We further show that growth laws, combined with drug uptake and
binding kinetics, enable the direct prediction of a large fraction of observed
interactions, yet fail to predict suppression. However, varying two translation
bottlenecks simultaneously supports that dense traffic of ribosomes and competition
for translation factors account for the previously unexplained suppression. These
results highlight the importance of "continuous epistasis" in bacterial physiology.'
acknowledged_ssus:
- _id: LifeSc
article_processing_charge: No
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
citation:
ama: Kavcic B. Analysis scripts and research data for the paper “Mechanisms of drug
interactions between translation-inhibiting antibiotics.” 2020. doi:10.15479/AT:ISTA:8097
apa: Kavcic, B. (2020). Analysis scripts and research data for the paper “Mechanisms
of drug interactions between translation-inhibiting antibiotics.” Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8097
chicago: Kavcic, Bor. “Analysis Scripts and Research Data for the Paper ‘Mechanisms
of Drug Interactions between Translation-Inhibiting Antibiotics.’” Institute of
Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8097.
ieee: B. Kavcic, “Analysis scripts and research data for the paper ‘Mechanisms of
drug interactions between translation-inhibiting antibiotics.’” Institute of Science
and Technology Austria, 2020.
ista: Kavcic B. 2020. Analysis scripts and research data for the paper ‘Mechanisms
of drug interactions between translation-inhibiting antibiotics’, Institute of
Science and Technology Austria, 10.15479/AT:ISTA:8097.
mla: Kavcic, Bor. Analysis Scripts and Research Data for the Paper “Mechanisms
of Drug Interactions between Translation-Inhibiting Antibiotics.” Institute
of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8097.
short: B. Kavcic, (2020).
contributor:
- contributor_type: research_group
first_name: Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- contributor_type: research_group
first_name: Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
date_created: 2020-07-06T20:40:19Z
date_published: 2020-07-15T00:00:00Z
date_updated: 2024-02-21T12:40:51Z
day: '15'
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:8097
file:
- access_level: open_access
checksum: 5c321dbbb6d4b3c85da786fd3ebbdc98
content_type: application/zip
creator: bkavcic
date_created: 2020-07-06T20:38:27Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8098'
file_name: natComm_2020_scripts.zip
file_size: 255770756
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
keyword:
- Escherichia coli
- antibiotic combinations
- translation
- growth laws
- drug interactions
- bacterial physiology
- translation inhibitors
month: '07'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
status: public
title: Analysis scripts and research data for the paper "Mechanisms of drug interactions
between translation-inhibiting antibiotics"
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8930'
abstract:
- lang: eng
text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable
history in physics but are still scarce in biology. This situation restrains predictive
theory. Here, we build on bacterial “growth laws,” which capture physiological
feedback between translation and cell growth, to construct a minimal biophysical
model for the combined action of ribosome-targeting antibiotics. Our model predicts
drug interactions like antagonism or synergy solely from responses to individual
drugs. We provide analytical results for limiting cases, which agree well with
numerical results. We systematically refine the model by including direct physical
interactions of different antibiotics on the ribosome. In a limiting case, our
model provides a mechanistic underpinning for recent predictions of higher-order
interactions that were derived using entropy maximization. We further refine the
model to include the effects of antibiotics that mimic starvation and the presence
of resistance genes. We describe the impact of a starvation-mimicking antibiotic
on drug interactions analytically and verify it experimentally. Our extended model
suggests a change in the type of drug interaction that depends on the strength
of resistance, which challenges established rescaling paradigms. We experimentally
show that the presence of unregulated resistance genes can lead to altered drug
interaction, which agrees with the prediction of the model. While minimal, the
model is readily adaptable and opens the door to predicting interactions of second
and higher-order in a broad range of biological systems.
article_processing_charge: No
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
citation:
ama: Kavcic B. Analysis scripts and research data for the paper “Minimal biophysical
model of combined antibiotic action.” 2020. doi:10.15479/AT:ISTA:8930
apa: Kavcic, B. (2020). Analysis scripts and research data for the paper “Minimal
biophysical model of combined antibiotic action.” Institute of Science and Technology
Austria. https://doi.org/10.15479/AT:ISTA:8930
chicago: Kavcic, Bor. “Analysis Scripts and Research Data for the Paper ‘Minimal
Biophysical Model of Combined Antibiotic Action.’” Institute of Science and Technology
Austria, 2020. https://doi.org/10.15479/AT:ISTA:8930.
ieee: B. Kavcic, “Analysis scripts and research data for the paper ‘Minimal biophysical
model of combined antibiotic action.’” Institute of Science and Technology Austria,
2020.
ista: Kavcic B. 2020. Analysis scripts and research data for the paper ‘Minimal
biophysical model of combined antibiotic action’, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:8930.
mla: Kavcic, Bor. Analysis Scripts and Research Data for the Paper “Minimal Biophysical
Model of Combined Antibiotic Action.” Institute of Science and Technology
Austria, 2020, doi:10.15479/AT:ISTA:8930.
short: B. Kavcic, (2020).
contributor:
- contributor_type: supervisor
first_name: Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- contributor_type: supervisor
first_name: Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
corr_author: '1'
date_created: 2020-12-09T15:04:02Z
date_published: 2020-12-10T00:00:00Z
date_updated: 2025-06-12T06:33:18Z
day: '10'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:8930
file:
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checksum: 60a818edeffaa7da1ebf5f8fbea9ba18
content_type: application/zip
creator: bkavcic
date_created: 2020-12-09T15:00:19Z
date_updated: 2020-12-09T15:00:19Z
file_id: '8932'
file_name: PLoSCompBiol2020_datarep.zip
file_size: 315494370
relation: main_file
success: 1
file_date_updated: 2020-12-09T15:00:19Z
has_accepted_license: '1'
keyword:
- Escherichia coli
- antibiotic combinations
- translation
- growth laws
- drug interactions
- bacterial physiology
- translation inhibitors
month: '12'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
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relation: used_in_publication
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title: Analysis scripts and research data for the paper "Minimal biophysical model
of combined antibiotic action"
tmp:
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type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...