---
_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: 2024-02-21T12:41:41Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1008529
external_id:
isi:
- '000608045000010'
file:
- access_level: open_access
checksum: e29f2b42651bef8e034781de8781ffac
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T12:30:48Z
date_updated: 2021-02-04T12:30:48Z
file_id: '9092'
file_name: 2021_PlosComBio_Kavcic.pdf
file_size: 3690053
relation: main_file
success: 1
file_date_updated: 2021-02-04T12:30:48Z
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
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'
related_material:
record:
- id: '7673'
relation: earlier_version
status: public
- id: '8930'
relation: research_data
status: public
status: public
title: Minimal biophysical model of combined antibiotic action
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: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 17
year: '2021'
...