---
_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'
...
---
_id: '9283'
abstract:
- lang: eng
text: Gene expression levels are influenced by multiple coexisting molecular mechanisms.
Some of these interactions such as those of transcription factors and promoters
have been studied extensively. However, predicting phenotypes of gene regulatory
networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic
GRN to study in Escherichia coli how network phenotypes depend on local genetic
context, i.e. the genetic neighborhood of a transcription factor and its relative
position. We show that one GRN with fixed topology can display not only quantitatively
but also qualitatively different phenotypes, depending solely on the local genetic
context of its components. Transcriptional read-through is the main molecular
mechanism that places one transcriptional unit (TU) within two separate regulons
without the need for complex regulatory sequences. We propose that relative order
of individual TUs, with its potential for combinatorial complexity, plays an important
role in shaping phenotypes of GRNs.
acknowledgement: "We thank J Bollback, L Hurst, M Lagator, C Nizak, O Rivoire, M Savageau,
G Tkacik, and B Vicozo\r\nfor helpful discussions; A Dolinar and A Greshnova for
technical assistance; T Bollenbach for supplying the strain JW0336; C Rusnac, and
members of the Guet lab for comments. The research leading to these results has
received funding from the People Programme (Marie Curie Actions) of the European
Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n˚\r\n628377
(ANS) and an Austrian Science Fund (FWF) grant n˚ I 3901-B32 (CCG)."
article_number: e65993
article_processing_charge: Yes
article_type: original
author:
- first_name: Anna A
full_name: Nagy-Staron, Anna A
id: 3ABC5BA6-F248-11E8-B48F-1D18A9856A87
last_name: Nagy-Staron
orcid: 0000-0002-1391-8377
- first_name: Kathrin
full_name: Tomasek, Kathrin
id: 3AEC8556-F248-11E8-B48F-1D18A9856A87
last_name: Tomasek
orcid: 0000-0003-3768-877X
- first_name: Caroline
full_name: Caruso Carter, Caroline
last_name: Caruso Carter
- first_name: Elisabeth
full_name: Sonnleitner, Elisabeth
last_name: Sonnleitner
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Tiago
full_name: Paixão, Tiago
last_name: Paixão
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
citation:
ama: Nagy-Staron AA, Tomasek K, Caruso Carter C, et al. Local genetic context shapes
the function of a gene regulatory network. eLife. 2021;10. doi:10.7554/elife.65993
apa: Nagy-Staron, A. A., Tomasek, K., Caruso Carter, C., Sonnleitner, E., Kavcic,
B., Paixão, T., & Guet, C. C. (2021). Local genetic context shapes the function
of a gene regulatory network. ELife. eLife Sciences Publications. https://doi.org/10.7554/elife.65993
chicago: Nagy-Staron, Anna A, Kathrin Tomasek, Caroline Caruso Carter, Elisabeth
Sonnleitner, Bor Kavcic, Tiago Paixão, and Calin C Guet. “Local Genetic Context
Shapes the Function of a Gene Regulatory Network.” ELife. eLife Sciences
Publications, 2021. https://doi.org/10.7554/elife.65993.
ieee: A. A. Nagy-Staron et al., “Local genetic context shapes the function
of a gene regulatory network,” eLife, vol. 10. eLife Sciences Publications,
2021.
ista: Nagy-Staron AA, Tomasek K, Caruso Carter C, Sonnleitner E, Kavcic B, Paixão
T, Guet CC. 2021. Local genetic context shapes the function of a gene regulatory
network. eLife. 10, e65993.
mla: Nagy-Staron, Anna A., et al. “Local Genetic Context Shapes the Function of
a Gene Regulatory Network.” ELife, vol. 10, e65993, eLife Sciences Publications,
2021, doi:10.7554/elife.65993.
short: A.A. Nagy-Staron, K. Tomasek, C. Caruso Carter, E. Sonnleitner, B. Kavcic,
T. Paixão, C.C. Guet, ELife 10 (2021).
date_created: 2021-03-23T10:11:46Z
date_published: 2021-03-08T00:00:00Z
date_updated: 2024-02-21T12:41:57Z
day: '08'
ddc:
- '570'
department:
- _id: GaTk
- _id: CaGu
doi: 10.7554/elife.65993
ec_funded: 1
external_id:
isi:
- '000631050900001'
file:
- access_level: open_access
checksum: 3c2f44058c2dd45a5a1027f09d263f8e
content_type: application/pdf
creator: bkavcic
date_created: 2021-03-23T10:12:58Z
date_updated: 2021-03-23T10:12:58Z
file_id: '9284'
file_name: elife-65993-v2.pdf
file_size: 1390469
relation: main_file
success: 1
file_date_updated: 2021-03-23T10:12:58Z
has_accepted_license: '1'
intvolume: ' 10'
isi: 1
keyword:
- Genetics and Molecular Biology
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 2517526A-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '628377'
name: 'The Systems Biology of Transcriptional Read-Through in Bacteria: from Synthetic
Networks to Genomic Studies'
- _id: 268BFA92-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03901
name: 'CyberCircuits: Cybergenetic circuits to test composability of gene networks'
publication: eLife
publication_identifier:
issn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
related_material:
record:
- id: '8951'
relation: research_data
status: public
status: public
title: Local genetic context shapes the function of a gene regulatory network
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: 10
year: '2021'
...
---
_id: '10579'
abstract:
- lang: eng
text: 'We consider a totally asymmetric simple exclusion process (TASEP) consisting
of particles on a lattice that require binding by a "token" to move. Using a combination
of theory and simulations, we address the following questions: (i) How token binding
kinetics affects the current-density relation; (ii) How the current-density relation
depends on the scarcity of tokens; (iii) How tokens propagate the effects of the
locally-imposed disorder (such a slow site) over the entire lattice; (iv) How
a shared pool of tokens couples concurrent TASEPs running on multiple lattices;
(v) How our results translate to TASEPs with open boundaries that exchange particles
with the reservoir. Since real particle motion (including in systems that inspired
the standard TASEP model, e.g., protein synthesis or movement of molecular motors)
is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven
TASEP dynamics analyzed in this paper should allow for a better understanding
of real systems and enable a closer match between TASEP theory and experimental
observations.'
acknowledgement: B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for
their help with the usage of the cluster. B.K. additionally thanks Călin Guet and
his group for help and advice. We thank M. Hennessey-Wesen for constructive comments
on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting
a typographical error in Eq. (49) in the preprint version of this paper.
article_number: '2112.13558'
article_processing_charge: No
arxiv: 1
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
citation:
ama: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv. doi:10.48550/arXiv.2112.13558
apa: Kavcic, B., & Tkačik, G. (n.d.). Token-driven totally asymmetric simple
exclusion process. arXiv. https://doi.org/10.48550/arXiv.2112.13558
chicago: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple
Exclusion Process.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2112.13558.
ieee: B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion
process,” arXiv. .
ista: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv, 2112.13558.
mla: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion
Process.” ArXiv, 2112.13558, doi:10.48550/arXiv.2112.13558.
short: B. Kavcic, G. Tkačik, ArXiv (n.d.).
date_created: 2021-12-28T06:52:09Z
date_published: 2021-12-27T00:00:00Z
date_updated: 2023-05-03T10:54:05Z
day: '27'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.48550/arXiv.2112.13558
external_id:
arxiv:
- '2112.13558'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.13558
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Token-driven totally asymmetric simple exclusion process
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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: '8151'
abstract:
- lang: eng
text: The main idea behind the Core Project is to teach first year students at IST
scientific communication skills and let them practice by presenting their research
within an interdisciplinary environment. Over the course of the first semester,
students participated in seminars, where they shared their results with the colleagues
from other fields and took part in discussions on relevant subjects. The main
focus during this sessions was on delivering the information in a simplified and
comprehensible way, going into the very basics of a subject if necessary. At the
end, the students were asked to present their research in the written form to
exercise their writing skills. The reports were gathered in this document. All
of them were reviewed by the teaching assistants and write-ups illustrating unique
stylistic features and, in general, an outstanding level of writing skills, were
honorably mentioned in the section "Selected Reports".
article_processing_charge: No
author:
- first_name: Mikhail
full_name: Maslov, Mikhail
id: 2E65BB0E-F248-11E8-B48F-1D18A9856A87
last_name: Maslov
orcid: 0000-0003-4074-2570
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Christina
full_name: Artner, Christina
id: 45DF286A-F248-11E8-B48F-1D18A9856A87
last_name: Artner
- first_name: Mike
full_name: Hennessey-Wesen, Mike
id: 3F338C72-F248-11E8-B48F-1D18A9856A87
last_name: Hennessey-Wesen
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
- first_name: Roshan K
full_name: Satapathy, Roshan K
id: 46046B7A-F248-11E8-B48F-1D18A9856A87
last_name: Satapathy
- first_name: Isabella
full_name: Tomanek, Isabella
id: 3981F020-F248-11E8-B48F-1D18A9856A87
last_name: Tomanek
orcid: 0000-0001-6197-363X
citation:
ama: Maslov M, Kondrashov F, Artner C, et al. Core Project Proceedings. IST
Austria; 2020.
apa: Maslov, M., Kondrashov, F., Artner, C., Hennessey-Wesen, M., Kavcic, B., Machnik,
N. N., … Tomanek, I. (2020). Core Project Proceedings. IST Austria.
chicago: Maslov, Mikhail, Fyodor Kondrashov, Christina Artner, Mike Hennessey-Wesen,
Bor Kavcic, Nick N Machnik, Roshan K Satapathy, and Isabella Tomanek. Core
Project Proceedings. IST Austria, 2020.
ieee: M. Maslov et al., Core Project Proceedings. IST Austria, 2020.
ista: Maslov M, Kondrashov F, Artner C, Hennessey-Wesen M, Kavcic B, Machnik NN,
Satapathy RK, Tomanek I. 2020. Core Project Proceedings, IST Austria, 425p.
mla: Maslov, Mikhail, et al. Core Project Proceedings. IST Austria, 2020.
short: M. Maslov, F. Kondrashov, C. Artner, M. Hennessey-Wesen, B. Kavcic, N.N.
Machnik, R.K. Satapathy, I. Tomanek, Core Project Proceedings, IST Austria, 2020.
date_created: 2020-07-22T14:48:14Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2023-02-23T13:26:00Z
day: '01'
ddc:
- '510'
- '530'
- '570'
extern: '1'
file:
- access_level: local
content_type: application/pdf
creator: dernst
date_created: 2020-07-22T14:45:07Z
date_updated: 2020-07-22T14:45:07Z
file_id: '8152'
file_name: Core_Project_Proceedings_mod.pdf
file_size: 169620437
relation: main_file
file_date_updated: 2020-07-22T14:45:07Z
has_accepted_license: '1'
language:
- iso: eng
month: '06'
oa_version: None
page: '425'
publication_status: published
publisher: IST Austria
status: public
title: Core Project Proceedings
type: report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8250'
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.'
acknowledgement: "We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K.
Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive
comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support,
which rendered this\r\nwork possible. B.K. thanks all members of Guet group for
many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges
the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work.
We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A.
Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This
work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P
27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to
T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.),
and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310
(to T.B.). Open access funding provided by\r\nProjekt DEAL."
article_number: '4013'
article_processing_charge: No
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. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 2020;11. doi:10.1038/s41467-020-17734-z
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). Mechanisms of drug
interactions between translation-inhibiting antibiotics. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-020-17734-z
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of
Drug Interactions between Translation-Inhibiting Antibiotics.” Nature Communications.
Springer Nature, 2020. https://doi.org/10.1038/s41467-020-17734-z.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions
between translation-inhibiting antibiotics,” Nature Communications, vol.
11. Springer Nature, 2020.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 11, 4013.
mla: Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting
Antibiotics.” Nature Communications, vol. 11, 4013, Springer Nature, 2020,
doi:10.1038/s41467-020-17734-z.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).
date_created: 2020-08-12T09:13:50Z
date_published: 2020-08-11T00:00:00Z
date_updated: 2024-03-25T23:30:05Z
day: '11'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1038/s41467-020-17734-z
external_id:
isi:
- '000562769300008'
file:
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file_size: 1965672
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intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '08'
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: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8657'
relation: dissertation_contains
status: public
status: public
title: 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: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2020'
...
---
_id: '8657'
abstract:
- lang: eng
text: "Synthesis of proteins – translation – is a fundamental process of life. Quantitative
studies anchor translation into the context of bacterial physiology and reveal
several mathematical relationships, called “growth laws,” which capture physiological
feedbacks between protein synthesis and cell growth. Growth laws describe the
dependency of the ribosome abundance as a function of growth rate, which can change
depending on the growth conditions. Perturbations of translation reveal that bacteria
employ a compensatory strategy in which the reduced translation capability results
in increased expression of the translation machinery.\r\nPerturbations of translation
are achieved in various ways; clinically interesting is the application of translation-targeting
antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology
are often poorly understood. Bacterial responses to two or more simultaneously
applied antibiotics are even more puzzling. The combined antibiotic effect determines
the type of drug interaction, which ranges from synergy (the effect is stronger
than expected) to antagonism (the effect is weaker) and suppression (one of the
drugs loses its potency).\r\nIn the first part of this work, we systematically
measure the pairwise interaction network for translation inhibitors that interfere
with different steps in translation. We find that the interactions are surprisingly
diverse and tend to be more antagonistic. To explore the underlying mechanisms,
we begin with a minimal biophysical model of combined antibiotic action. We base
this model on the kinetics of antibiotic uptake and binding together with the
physiological response described by the growth laws. The biophysical model explains
some drug interactions, but not all; it specifically fails to predict suppression.\r\nIn
the second part of this work, we hypothesize that elusive suppressive drug interactions
result from the interplay between ribosomes halted in different stages of translation.
To elucidate this putative mechanism of drug interactions between translation
inhibitors, we generate translation bottlenecks genetically using in- ducible
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 partially causes
these interactions.\r\nWe extend this approach by varying two translation bottlenecks
simultaneously. This approach reveals the suppression of translocation inhibition
by inhibited translation. We rationalize this effect by modeling dense traffic
of ribosomes that move on transcripts in a translation factor-mediated manner.
This model predicts a dissolution of traffic jams caused by inhibited translocation
when the density of ribosome traffic is reduced by lowered initiation. We base
this model on the growth laws and quantitative relationships between different
translation and growth parameters.\r\nIn the final part of this work, we describe
a set of tools aimed at quantification of physiological and translation parameters.
We further develop a simple model that directly connects the abundance of a translation
factor with the growth rate, which allows us to extract physiological parameters
describing initiation. We demonstrate the development of tools for measuring translation
rate.\r\nThis thesis showcases how a combination of high-throughput growth rate
mea- surements, genetics, and modeling can reveal mechanisms of drug interactions.
Furthermore, by a gradual transition from combinations of antibiotics to precise
genetic interventions, we demonstrated the equivalency between genetic and chemi-
cal perturbations of translation. These findings tile the path for quantitative
studies of antibiotic combinations and illustrate future approaches towards the
quantitative description of translation."
acknowledged_ssus:
- _id: LifeSc
- _id: M-Shop
acknowledgement: I thank Life Science Facilities for their continuous support with
providing top-notch laboratory materials, keeping the devices humming, and coordinating
the repairs and building of custom-designed laboratory equipment with the MIBA Machine
shop.
alternative_title:
- ISTA Thesis
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. Perturbations of protein synthesis: from antibiotics to genetics
and physiology. 2020. doi:10.15479/AT:ISTA:8657'
apa: 'Kavcic, B. (2020). Perturbations of protein synthesis: from antibiotics
to genetics and physiology. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8657'
chicago: 'Kavcic, Bor. “Perturbations of Protein Synthesis: From Antibiotics to
Genetics and Physiology.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8657.'
ieee: 'B. Kavcic, “Perturbations of protein synthesis: from antibiotics to genetics
and physiology,” Institute of Science and Technology Austria, 2020.'
ista: 'Kavcic B. 2020. Perturbations of protein synthesis: from antibiotics to genetics
and physiology. Institute of Science and Technology Austria.'
mla: 'Kavcic, Bor. Perturbations of Protein Synthesis: From Antibiotics to Genetics
and Physiology. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8657.'
short: 'B. Kavcic, Perturbations of Protein Synthesis: From Antibiotics to Genetics
and Physiology, Institute of Science and Technology Austria, 2020.'
date_created: 2020-10-13T16:46:14Z
date_published: 2020-10-14T00:00:00Z
date_updated: 2023-09-07T13:20:48Z
day: '14'
ddc:
- '571'
- '530'
- '570'
degree_awarded: PhD
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:8657
file:
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checksum: d708ecd62b6fcc3bc1feb483b8dbe9eb
content_type: application/pdf
creator: bkavcic
date_created: 2020-10-15T06:41:20Z
date_updated: 2021-10-07T22:30:03Z
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file_name: kavcicB_thesis202009.pdf
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date_updated: 2021-10-07T22:30:03Z
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language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: '271'
publication_identifier:
isbn:
- 978-3-99078-011-4
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7673'
relation: part_of_dissertation
status: public
- id: '8250'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: 'Perturbations of protein synthesis: from antibiotics to genetics and physiology'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
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
date_created: 2020-12-09T15:04:02Z
date_published: 2020-12-10T00:00:00Z
date_updated: 2024-02-21T12:41:42Z
day: '10'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:8930
file:
- access_level: open_access
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:
record:
- id: '8997'
relation: used_in_publication
status: public
status: public
title: Analysis scripts and research data for the paper "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: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7673'
abstract:
- lang: eng
text: Combining drugs can improve the efficacy of treatments. However, predicting
the effect of drug combinations is still challenging. The combined potency of
drugs determines the drug interaction, which is classified as synergistic, additive,
antagonistic, or suppressive. While probabilistic, non-mechanistic models exist,
there is currently no biophysical model that can predict antibiotic interactions.
Here, we present a physiologically relevant model of the combined action of antibiotics
that inhibit protein synthesis by targeting the ribosome. This model captures
the kinetics of antibiotic binding and transport, and uses bacterial growth laws
to predict growth in the presence of antibiotic combinations. We find that this
biophysical model can produce all drug interaction types except suppression. We
show analytically that antibiotics which cannot bind to the ribosome simultaneously
generally act as substitutes for one another, leading to additive drug interactions.
Previously proposed null expectations for higher-order drug interactions follow
as a limiting case of our model. We further extend the model to include the effects
of direct physical or allosteric interactions between individual drugs on the
ribosome. Notably, such direct interactions profoundly change the combined drug
effect, depending on the kinetic parameters of the drugs used. The model makes
additional predictions for the effects of resistance genes on drug interactions
and for interactions between ribosome-targeting antibiotics and antibiotics with
other targets. These findings enhance our understanding of the interplay between
drug action and cell physiology and are a key step toward a general framework
for predicting drug interactions.
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
- 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. A minimal biophysical model of combined
antibiotic action. bioRxiv. 2020. doi:10.1101/2020.04.18.047886
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). A minimal biophysical
model of combined antibiotic action. bioRxiv. Cold Spring Harbor Laboratory.
https://doi.org/10.1101/2020.04.18.047886
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “A Minimal Biophysical
Model of Combined Antibiotic Action.” BioRxiv. Cold Spring Harbor Laboratory,
2020. https://doi.org/10.1101/2020.04.18.047886.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “A minimal biophysical model of
combined antibiotic action,” bioRxiv. Cold Spring Harbor Laboratory, 2020.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. A minimal biophysical model of combined
antibiotic action. bioRxiv, 10.1101/2020.04.18.047886.
mla: Kavcic, Bor, et al. “A Minimal Biophysical Model of Combined Antibiotic Action.”
BioRxiv, Cold Spring Harbor Laboratory, 2020, doi:10.1101/2020.04.18.047886.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, BioRxiv (2020).
date_created: 2020-04-22T08:27:56Z
date_published: 2020-04-18T00:00:00Z
date_updated: 2024-03-25T23:30:05Z
day: '18'
department:
- _id: GaTk
doi: 10.1101/2020.04.18.047886
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/2020.04.18.047886 '
month: '04'
oa: 1
oa_version: Preprint
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: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '8997'
relation: later_version
status: public
- id: '8657'
relation: dissertation_contains
status: public
status: public
title: A minimal biophysical model of combined antibiotic action
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '5817'
abstract:
- lang: eng
text: We theoretically study the shapes of lipid vesicles confined to a spherical
cavity, elaborating a framework based on the so-called limiting shapes constructed
from geometrically simple structural elements such as double-membrane walls and
edges. Partly inspired by numerical results, the proposed non-compartmentalized
and compartmentalized limiting shapes are arranged in the bilayer-couple phase
diagram which is then compared to its free-vesicle counterpart. We also compute
the area-difference-elasticity phase diagram of the limiting shapes and we use
it to interpret shape transitions experimentally observed in vesicles confined
within another vesicle. The limiting-shape framework may be generalized to theoretically
investigate the structure of certain cell organelles such as the mitochondrion.
article_processing_charge: No
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: A.
full_name: Sakashita, A.
last_name: Sakashita
- first_name: H.
full_name: Noguchi, H.
last_name: Noguchi
- first_name: P.
full_name: Ziherl, P.
last_name: Ziherl
citation:
ama: Kavcic B, Sakashita A, Noguchi H, Ziherl P. Limiting shapes of confined lipid
vesicles. Soft Matter. 2019;15(4):602-614. doi:10.1039/c8sm01956h
apa: Kavcic, B., Sakashita, A., Noguchi, H., & Ziherl, P. (2019). Limiting shapes
of confined lipid vesicles. Soft Matter. Royal Society of Chemistry. https://doi.org/10.1039/c8sm01956h
chicago: Kavcic, Bor, A. Sakashita, H. Noguchi, and P. Ziherl. “Limiting Shapes
of Confined Lipid Vesicles.” Soft Matter. Royal Society of Chemistry, 2019.
https://doi.org/10.1039/c8sm01956h.
ieee: B. Kavcic, A. Sakashita, H. Noguchi, and P. Ziherl, “Limiting shapes of confined
lipid vesicles,” Soft Matter, vol. 15, no. 4. Royal Society of Chemistry,
pp. 602–614, 2019.
ista: Kavcic B, Sakashita A, Noguchi H, Ziherl P. 2019. Limiting shapes of confined
lipid vesicles. Soft Matter. 15(4), 602–614.
mla: Kavcic, Bor, et al. “Limiting Shapes of Confined Lipid Vesicles.” Soft Matter,
vol. 15, no. 4, Royal Society of Chemistry, 2019, pp. 602–14, doi:10.1039/c8sm01956h.
short: B. Kavcic, A. Sakashita, H. Noguchi, P. Ziherl, Soft Matter 15 (2019) 602–614.
date_created: 2019-01-11T07:37:47Z
date_published: 2019-01-10T00:00:00Z
date_updated: 2023-09-13T08:47:16Z
day: '10'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1039/c8sm01956h
external_id:
isi:
- '000457329700003'
pmid:
- '30629082'
file:
- access_level: open_access
checksum: 614c337d6424ccd3d48d1b1f9513510d
content_type: application/pdf
creator: bkavcic
date_created: 2020-10-09T11:00:05Z
date_updated: 2020-10-09T11:00:05Z
file_id: '8641'
file_name: lmt_sftmtr_V8.pdf
file_size: 5370762
relation: main_file
success: 1
file_date_updated: 2020-10-09T11:00:05Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/3.0/
month: '01'
oa: 1
oa_version: Submitted Version
page: 602-614
pmid: 1
publication: Soft Matter
publication_identifier:
eissn:
- 1744-6848
issn:
- 1744-683X
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Limiting shapes of confined lipid vesicles
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 15
year: '2019'
...