---
_id: '8037'
abstract:
- lang: eng
text: 'Genetic perturbations that affect bacterial resistance to antibiotics have
been characterized genome-wide, but how do such perturbations interact with subsequent
evolutionary adaptation to the drug? Here, we show that strong epistasis between
resistance mutations and systematically identified genes can be exploited to control
spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12
mutant populations in parallel, using a robotic platform that tightly controls
population size and selection pressure. We find a global diminishing-returns epistasis
pattern: strains that are initially more sensitive generally undergo larger resistance
gains. However, some gene deletion strains deviate from this general trend and
curtail the evolvability of resistance, including deletions of genes for membrane
transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force
evolution on inferior mutational paths, not explored in the wild type, and some
of these essentially block resistance evolution. This effect is due to strong
negative epistasis with resistance mutations. The identified genes and cellular
functions provide potential targets for development of adjuvants that may block
spontaneous resistance evolution when combined with antibiotics.'
article_number: '3105'
article_processing_charge: No
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Booshini
full_name: Fernando, Booshini
last_name: Fernando
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisinova M, Fernando B, Bollenbach MT. Highly parallel lab evolution reveals
that epistasis can curb the evolution of antibiotic resistance. Nature Communications.
2020;11. doi:10.1038/s41467-020-16932-z
apa: Lukacisinova, M., Fernando, B., & Bollenbach, M. T. (2020). Highly parallel
lab evolution reveals that epistasis can curb the evolution of antibiotic resistance.
Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-16932-z
chicago: Lukacisinova, Marta, Booshini Fernando, and Mark Tobias Bollenbach. “Highly
Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic
Resistance.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-16932-z.
ieee: M. Lukacisinova, B. Fernando, and M. T. Bollenbach, “Highly parallel lab evolution
reveals that epistasis can curb the evolution of antibiotic resistance,” Nature
Communications, vol. 11. Springer Nature, 2020.
ista: Lukacisinova M, Fernando B, Bollenbach MT. 2020. Highly parallel lab evolution
reveals that epistasis can curb the evolution of antibiotic resistance. Nature
Communications. 11, 3105.
mla: Lukacisinova, Marta, et al. “Highly Parallel Lab Evolution Reveals That Epistasis
Can Curb the Evolution of Antibiotic Resistance.” Nature Communications,
vol. 11, 3105, Springer Nature, 2020, doi:10.1038/s41467-020-16932-z.
short: M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020).
date_created: 2020-06-29T07:59:35Z
date_published: 2020-06-19T00:00:00Z
date_updated: 2023-08-22T07:48:30Z
day: '19'
ddc:
- '570'
doi: 10.1038/s41467-020-16932-z
extern: '1'
external_id:
isi:
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pmid:
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file_name: 2020_NatureComm_Lukacisinova.pdf
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language:
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oa: 1
oa_version: Published Version
pmid: 1
project:
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call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Nature Communications
publication_identifier:
eissn:
- '20411723'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Highly parallel lab evolution reveals that epistasis can curb the evolution
of antibiotic resistance
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: '6263'
abstract:
- lang: eng
text: 'Antibiotic resistance can emerge spontaneously through genomic mutation and render
treatment ineffective. To counteract this process, in addition to the discovery and
description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand
its determinantsis needed. To address this challenge, this thesisuncoversnew genetic
determinants of resistance evolvability using a customized robotic setup,
exploressystematic ways in which resistance evolution is perturbed due to
dose-responsecharacteristics of drugs and mutation rate differences,and mathematically investigates
the evolutionary fate of one specific type of evolvability modifier -a stress-induced
mutagenesis allele.We find severalgenes which strongly inhibit or potentiate resistance evolution. In order
to identify them, we first developedan automated high-throughput feedback-controlled
protocol whichkeeps the population size and selection pressure approximately constant
for hundreds of cultures by dynamically re-diluting the cultures and adjusting the antibiotic
concentration. We implementedthis protocol on a customized liquid handling robot and
propagated 100 different gene deletion strains of Escherichia coliin triplicate for over 100
generations in tetracycline and in chloramphenicol, and comparedtheir adaptation rates.We find a diminishing returns pattern, where initially sensitive strains adapted more
compared to less sensitive ones. Our data uncover that deletions of certain genes
which do not affect mutation rate,including efflux pump components, a chaperone and
severalstructural and regulatory genes can strongly and reproducibly alterresistance evolution.
Sequencing analysis of evolved populations indicates that epistasis with resistance
mutations is the most likelyexplanation. This work could inspire treatment strategies in
which targeted inhibitors of evolvability mechanisms will be given alongside antibiotics to
slow down resistance evolution and extend theefficacy of antibiotics.We implemented astochasticpopulation genetics model,
toverifyways in which general properties, namely, dose-response characteristics of drugs and mutation rates, influence
evolutionary dynamics. In particular, under the exposure to antibiotics with shallow dose-response curves,bacteria have narrower distributions of fitness effects of new mutations.
We show that in silicothis also leads to slower resistance evolution. We
see and confirm with experiments that increased mutation rates, apart from speeding
up evolution, also leadto high reproducibility of phenotypic adaptation in a context
of continually strong selection pressure.Knowledge of these patterns can aid in predicting the dynamics of antibiotic
resistance evolutionand adapting treatment schemes accordingly.Focusing on a previously described type of evolvability modifier
–a stress-induced mutagenesis allele –we find conditions under which it can persist in a population under
periodic selectionakin to clinical treatment. We set up a deterministic
infinite populationcontinuous time model tracking the frequencies of a mutator and resistance allele and
evaluate various treatment schemes in how well they maintain a stress-induced
mutator allele. In particular,a high diversity of stresses is crucial for the persistence
of the mutator allele. This leads to a general trade-off where exactly those
diversifying treatment schemes which are likely to decrease levels of resistance could lead to stronger selection of highly
evolvable genotypes.In the long run, this work will lead to a deeper understanding of the genetic and cellular
mechanisms involved in antibiotic resistance evolution and could inspire new strategies
for slowing down its rate. '
acknowledged_ssus:
- _id: M-Shop
- _id: LifeSc
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
citation:
ama: Lukacisinova M. Genetic determinants of antibiotic resistance evolution. 2018.
doi:10.15479/AT:ISTA:th1072
apa: Lukacisinova, M. (2018). Genetic determinants of antibiotic resistance evolution.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th1072
chicago: Lukacisinova, Marta. “Genetic Determinants of Antibiotic Resistance Evolution.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:th1072.
ieee: M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,”
Institute of Science and Technology Austria, 2018.
ista: Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution.
Institute of Science and Technology Austria.
mla: Lukacisinova, Marta. Genetic Determinants of Antibiotic Resistance Evolution.
Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th1072.
short: M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution,
Institute of Science and Technology Austria, 2018.
date_created: 2019-04-09T13:57:15Z
date_published: 2018-12-28T00:00:00Z
date_updated: 2023-09-22T09:20:37Z
day: '28'
ddc:
- '570'
- '576'
- '579'
degree_awarded: PhD
department:
- _id: ToBo
doi: 10.15479/AT:ISTA:th1072
file:
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checksum: fc60585c9eaad868ac007004ef130908
content_type: application/pdf
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date_created: 2019-04-09T13:49:24Z
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language:
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page: '91'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
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relation: part_of_dissertation
status: public
- id: '696'
relation: part_of_dissertation
status: public
- id: '1027'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: Genetic determinants of antibiotic resistance evolution
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '696'
abstract:
- lang: eng
text: Mutator strains are expected to evolve when the availability and effect of
beneficial mutations are high enough to counteract the disadvantage from deleterious
mutations that will inevitably accumulate. As the population becomes more adapted
to its environment, both availability and effect of beneficial mutations necessarily
decrease and mutation rates are predicted to decrease. It has been shown that
certain molecular mechanisms can lead to increased mutation rates when the organism
finds itself in a stressful environment. While this may be a correlated response
to other functions, it could also be an adaptive mechanism, raising mutation rates
only when it is most advantageous. Here, we use a mathematical model to investigate
the plausibility of the adaptive hypothesis. We show that such a mechanism can
be mantained if the population is subjected to diverse stresses. By simulating
various antibiotic treatment schemes, we find that combination treatments can
reduce the effectiveness of second-order selection on stress-induced mutagenesis.
We discuss the implications of our results to strategies of antibiotic therapy.
article_number: e1005609
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: 'Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity
facilitates the persistence of mutator genes. PLoS Computational Biology.
2017;13(7). doi:10.1371/journal.pcbi.1005609'
apa: 'Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis:
Stress diversity facilitates the persistence of mutator genes. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609'
chicago: 'Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced
Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS
Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.'
ieee: 'M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes,” PLoS Computational
Biology, vol. 13, no. 7. Public Library of Science, 2017.'
ista: 'Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes. PLoS Computational Biology.
13(7), e1005609.'
mla: 'Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity
Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology,
vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.'
short: M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).
date_created: 2018-12-11T11:47:58Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2024-03-25T23:30:14Z
day: '18'
ddc:
- '576'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609
ec_funded: 1
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checksum: 9143c290fa6458ed2563bff4b295554a
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:01Z
date_updated: 2020-07-14T12:47:46Z
file_id: '5117'
file_name: IST-2017-894-v1+1_journal.pcbi.1005609.pdf
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file_date_updated: 2020-07-14T12:47:46Z
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- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
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call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '7004'
pubrep_id: '894'
quality_controlled: '1'
related_material:
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relation: research_data
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relation: research_data
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- id: '9852'
relation: research_data
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- id: '6263'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: 'Stress induced mutagenesis: Stress diversity facilitates the persistence of
mutator genes'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2017'
...
---
_id: '1027'
abstract:
- lang: eng
text: The rising prevalence of antibiotic resistant bacteria is an increasingly
serious public health challenge. To address this problem, recent work ranging
from clinical studies to theoretical modeling has provided valuable insights into
the mechanisms of resistance, its emergence and spread, and ways to counteract
it. A deeper understanding of the underlying dynamics of resistance evolution
will require a combination of experimental and theoretical expertise from different
disciplines and new technology for studying evolution in the laboratory. Here,
we review recent advances in the quantitative understanding of the mechanisms
and evolution of antibiotic resistance. We focus on key theoretical concepts and
new technology that enables well-controlled experiments. We further highlight
key challenges that can be met in the near future to ultimately develop effective
strategies for combating resistance.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic
resistance evolution. Current Opinion in Biotechnology. 2017;46:90-97.
doi:10.1016/j.copbio.2017.02.013
apa: Lukacisinova, M., & Bollenbach, M. T. (2017). Toward a quantitative understanding
of antibiotic resistance evolution. Current Opinion in Biotechnology. Elsevier.
https://doi.org/10.1016/j.copbio.2017.02.013
chicago: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative
Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology.
Elsevier, 2017. https://doi.org/10.1016/j.copbio.2017.02.013.
ieee: M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding
of antibiotic resistance evolution,” Current Opinion in Biotechnology,
vol. 46. Elsevier, pp. 90–97, 2017.
ista: Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of
antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97.
mla: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding
of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology,
vol. 46, Elsevier, 2017, pp. 90–97, doi:10.1016/j.copbio.2017.02.013.
short: M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017)
90–97.
date_created: 2018-12-11T11:49:45Z
date_published: 2017-08-01T00:00:00Z
date_updated: 2024-03-25T23:30:15Z
day: '01'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1016/j.copbio.2017.02.013
ec_funded: 1
external_id:
isi:
- '000408077400015'
file:
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file_size: 858338
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intvolume: ' 46'
isi: 1
language:
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month: '08'
oa: 1
oa_version: Published Version
page: 90 - 97
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Current Opinion in Biotechnology
publication_status: published
publisher: Elsevier
publist_id: '6364'
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related_material:
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scopus_import: '1'
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title: Toward a quantitative understanding of antibiotic resistance evolution
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
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volume: 46
year: '2017'
...
---
_id: '9849'
abstract:
- lang: eng
text: This text provides additional information about the model, a derivation of
the analytic results in Eq (4), and details about simulations of an additional
parameter set.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Modelling and simulation details. 2017.
doi:10.1371/journal.pcbi.1005609.s001
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Modelling and simulation
details. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s001
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Modelling and
Simulation Details.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s001.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Modelling and simulation details.”
Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Modelling and simulation details,
Public Library of Science, 10.1371/journal.pcbi.1005609.s001.
mla: Lukacisinova, Marta, et al. Modelling and Simulation Details. Public
Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s001.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:02:34Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609.s001
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
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...
---
_id: '9850'
abstract:
- lang: eng
text: In this text, we discuss how a cost of resistance and the possibility of lethal
mutations impact our model.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Extensions of the model. 2017. doi:10.1371/journal.pcbi.1005609.s002
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Extensions of the model.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s002
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Extensions of
the Model.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s002.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Extensions of the model.” Public
Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Extensions of the model, Public Library
of Science, 10.1371/journal.pcbi.1005609.s002.
mla: Lukacisinova, Marta, et al. Extensions of the Model. Public Library
of Science, 2017, doi:10.1371/journal.pcbi.1005609.s002.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:05:24Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s002
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Extensions of the model
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '9851'
abstract:
- lang: eng
text: Based on the intuitive derivation of the dynamics of SIM allele frequency
pM in the main text, we present a heuristic prediction for the long-term SIM allele
frequencies with χ > 1 stresses and compare it to numerical simulations.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Heuristic prediction for multiple stresses.
2017. doi:10.1371/journal.pcbi.1005609.s003
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Heuristic prediction
for multiple stresses. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s003
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Heuristic Prediction
for Multiple Stresses.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s003.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Heuristic prediction for multiple
stresses.” Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Heuristic prediction for multiple
stresses, Public Library of Science, 10.1371/journal.pcbi.1005609.s003.
mla: Lukacisinova, Marta, et al. Heuristic Prediction for Multiple Stresses.
Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s003.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:08:14Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s003
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Heuristic prediction for multiple stresses
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '9852'
abstract:
- lang: eng
text: We show how different combination strategies affect the fraction of individuals
that are multi-resistant.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Resistance frequencies for different combination
strategies. 2017. doi:10.1371/journal.pcbi.1005609.s004
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Resistance frequencies
for different combination strategies. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s004
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Resistance Frequencies
for Different Combination Strategies.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s004.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Resistance frequencies for different
combination strategies.” Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Resistance frequencies for different
combination strategies, Public Library of Science, 10.1371/journal.pcbi.1005609.s004.
mla: Lukacisinova, Marta, et al. Resistance Frequencies for Different Combination
Strategies. Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s004.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:11:40Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s004
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Resistance frequencies for different combination strategies
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '1619'
abstract:
- lang: eng
text: The emergence of drug resistant pathogens is a serious public health problem.
It is a long-standing goal to predict rates of resistance evolution and design
optimal treatment strategies accordingly. To this end, it is crucial to reveal
the underlying causes of drug-specific differences in the evolutionary dynamics
leading to resistance. However, it remains largely unknown why the rates of resistance
evolution via spontaneous mutations and the diversity of mutational paths vary
substantially between drugs. Here we comprehensively quantify the distribution
of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics,
in the presence of eight antibiotics representing the main modes of action. Using
precise high-throughput fitness measurements for genome-wide Escherichia coli
gene deletion strains, we find that the width of the DFE varies dramatically between
antibiotics and, contrary to conventional wisdom, for some drugs the DFE width
is lower than in the absence of stress. We show that this previously underappreciated
divergence in DFE width among antibiotics is largely caused by their distinct
drug-specific dose-response characteristics. Unlike the DFE, the magnitude of
the changes in tolerated drug concentration resulting from genome-wide mutations
is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin,
i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin
than for other drugs. A population genetics model predicts that resistance evolution
for drugs with this property is severely limited and confined to reproducible
mutational paths. We tested this prediction in laboratory evolution experiments
using the “morbidostat”, a device for evolving bacteria in well-controlled drug
environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible
mutations—an almost paradoxical behavior since this drug causes DNA damage and
increases the mutation rate. Overall, we identified novel quantitative characteristics
of the evolutionary landscape that provide the conceptual foundation for predicting
the dynamics of drug resistance evolution.
article_number: e1002299
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Dravecka, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Dravecka
orcid: 0000-0002-2519-8004
- first_name: Tugce
full_name: Batur, Tugce
last_name: Batur
- first_name: Aysegul
full_name: Guvenek, Aysegul
last_name: Guvenek
- first_name: Dilay
full_name: Ayhan, Dilay
last_name: Ayhan
- first_name: Erdal
full_name: Toprak, Erdal
last_name: Toprak
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of
evolutionary dynamics leading to drug resistance. PLoS Biology. 2015;13(11).
doi:10.1371/journal.pbio.1002299
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak,
E., & Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary
dynamics leading to drug resistance. PLoS Biology. Public Library of Science.
https://doi.org/10.1371/journal.pbio.1002299
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants
of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology. Public
Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.
ieee: G. Chevereau et al., “Quantifying the determinants of evolutionary
dynamics leading to drug resistance,” PLoS Biology, vol. 13, no. 11. Public
Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach
MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug
resistance. PLoS Biology. 13(11), e1002299.
mla: Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary
Dynamics Leading to Drug Resistance.” PLoS Biology, vol. 13, no. 11, e1002299,
Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak,
M.T. Bollenbach, PLoS Biology 13 (2015).
date_created: 2018-12-11T11:53:04Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2024-03-25T23:30:14Z
day: '18'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299
ec_funded: 1
file:
- access_level: open_access
checksum: 0e82e3279f50b15c6c170c042627802b
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:09:00Z
date_updated: 2020-07-14T12:45:07Z
file_id: '4723'
file_name: IST-2016-468-v1+1_journal.pbio.1002299.pdf
file_size: 1387760
relation: main_file
file_date_updated: 2020-07-14T12:45:07Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
publication: PLoS Biology
publication_status: published
publisher: Public Library of Science
publist_id: '5547'
pubrep_id: '468'
quality_controlled: '1'
related_material:
record:
- id: '9711'
relation: research_data
status: public
- id: '9765'
relation: research_data
status: public
- id: '6263'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Quantifying the determinants of evolutionary dynamics leading to drug resistance
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2015'
...
---
_id: '1509'
abstract:
- lang: eng
text: The Auxin Binding Protein1 (ABP1) has been identified based on its ability
to bind auxin with high affinity and studied for a long time as a prime candidate
for the extracellular auxin receptor responsible for mediating in particular the
fast non-transcriptional auxin responses. However, the contradiction between the
embryo-lethal phenotypes of the originally described Arabidopsis T-DNA insertional
knock-out alleles (abp1-1 and abp1-1s) and the wild type-like phenotypes of other
recently described loss-of-function alleles (abp1-c1 and abp1-TD1) questions the
biological importance of ABP1 and relevance of the previous genetic studies. Here
we show that there is no hidden copy of the ABP1 gene in the Arabidopsis genome
but the embryo-lethal phenotypes of abp1-1 and abp1-1s alleles are very similar
to the knock-out phenotypes of the neighboring gene, BELAYA SMERT (BSM). Furthermore,
the allelic complementation test between bsm and abp1 alleles shows that the embryo-lethality
in the abp1-1 and abp1-1s alleles is caused by the off-target disruption of the
BSM locus by the T-DNA insertions. This clarifies the controversy of different
phenotypes among published abp1 knock-out alleles and asks for reflections on
the developmental role of ABP1.
acknowledgement: "This work was supported by ERC Independent Research grant (ERC-2011-StG-20101109-PSDP
to JF). JM internship was supported by the grant “Action Austria – Slovakia”.\r\nData
associated with the article are available under the terms of the Creative Commons
Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication). \r\n\r\nData
availability: \r\nF1000Research: Dataset 1. Dataset 1, 10.5256/f1000research.7143.d104552\r\n\r\nF1000Research:
Dataset 2. Dataset 2, 10.5256/f1000research.7143.d104553\r\n\r\nF1000Research: Dataset
3. Dataset 3, 10.5256/f1000research.7143.d104554"
article_processing_charge: No
author:
- first_name: Jaroslav
full_name: Michalko, Jaroslav
id: 483727CA-F248-11E8-B48F-1D18A9856A87
last_name: Michalko
- first_name: Marta
full_name: Dravecka, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Dravecka
orcid: 0000-0002-2519-8004
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
- first_name: Jirí
full_name: Friml, Jirí
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
citation:
ama: Michalko J, Lukacisinova M, Bollenbach MT, Friml J. Embryo-lethal phenotypes
in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000
Research . 2015;4. doi:10.12688/f1000research.7143.1
apa: Michalko, J., Lukacisinova, M., Bollenbach, M. T., & Friml, J. (2015).
Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring
BSM gene. F1000 Research . F1000 Research. https://doi.org/10.12688/f1000research.7143.1
chicago: Michalko, Jaroslav, Marta Lukacisinova, Mark Tobias Bollenbach, and Jiří
Friml. “Embryo-Lethal Phenotypes in Early Abp1 Mutants Are Due to Disruption of
the Neighboring BSM Gene.” F1000 Research . F1000 Research, 2015. https://doi.org/10.12688/f1000research.7143.1.
ieee: J. Michalko, M. Lukacisinova, M. T. Bollenbach, and J. Friml, “Embryo-lethal
phenotypes in early abp1 mutants are due to disruption of the neighboring BSM
gene,” F1000 Research , vol. 4. F1000 Research, 2015.
ista: Michalko J, Lukacisinova M, Bollenbach MT, Friml J. 2015. Embryo-lethal phenotypes
in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000
Research . 4.
mla: Michalko, Jaroslav, et al. “Embryo-Lethal Phenotypes in Early Abp1 Mutants
Are Due to Disruption of the Neighboring BSM Gene.” F1000 Research , vol.
4, F1000 Research, 2015, doi:10.12688/f1000research.7143.1.
short: J. Michalko, M. Lukacisinova, M.T. Bollenbach, J. Friml, F1000 Research 4
(2015).
date_created: 2018-12-11T11:52:26Z
date_published: 2015-10-01T00:00:00Z
date_updated: 2025-05-07T11:12:30Z
day: '01'
ddc:
- '570'
department:
- _id: JiFr
- _id: ToBo
doi: 10.12688/f1000research.7143.1
ec_funded: 1
file:
- access_level: open_access
checksum: 8beae5cbe988e1060265ae7de2ee8306
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:12Z
date_updated: 2020-07-14T12:44:59Z
file_id: '5198'
file_name: IST-2016-497-v1+1_10.12688_f1000research.7143.1_20151102.pdf
file_size: 4414248
relation: main_file
file_date_updated: 2020-07-14T12:44:59Z
has_accepted_license: '1'
intvolume: ' 4'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
project:
- _id: 25716A02-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '282300'
name: Polarity and subcellular dynamics in plants
publication: 'F1000 Research '
publication_status: published
publisher: F1000 Research
publist_id: '5668'
pubrep_id: '497'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the
neighboring BSM gene
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2015'
...
---
_id: '9711'
article_processing_charge: No
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Tugce
full_name: Batur, Tugce
last_name: Batur
- first_name: Aysegul
full_name: Guvenek, Aysegul
last_name: Guvenek
- first_name: Dilay Hazal
full_name: Ayhan, Dilay Hazal
last_name: Ayhan
- first_name: Erdal
full_name: Toprak, Erdal
last_name: Toprak
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Chevereau G, Lukacisinova M, Batur T, et al. Excel file containing the raw
data for all figures. 2015. doi:10.1371/journal.pbio.1002299.s001
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak,
E., & Bollenbach, M. T. (2015). Excel file containing the raw data for all
figures. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s001
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing
the Raw Data for All Figures.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s001.
ieee: G. Chevereau et al., “Excel file containing the raw data for all figures.”
Public Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach
MT. 2015. Excel file containing the raw data for all figures, Public Library of
Science, 10.1371/journal.pbio.1002299.s001.
mla: Chevereau, Guillaume, et al. Excel File Containing the Raw Data for All
Figures. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s001.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak,
M.T. Bollenbach, (2015).
date_created: 2021-07-23T11:53:50Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2023-02-23T10:07:02Z
day: '18'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299.s001
month: '11'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1619'
relation: used_in_publication
status: public
status: public
title: Excel file containing the raw data for all figures
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '9765'
article_processing_charge: No
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Tugce
full_name: Batur, Tugce
last_name: Batur
- first_name: Aysegul
full_name: Guvenek, Aysegul
last_name: Guvenek
- first_name: Dilay Hazal
full_name: Ayhan, Dilay Hazal
last_name: Ayhan
- first_name: Erdal
full_name: Toprak, Erdal
last_name: Toprak
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Chevereau G, Lukacisinova M, Batur T, et al. Gene ontology enrichment analysis
for the most sensitive gene deletion strains for all drugs. 2015. doi:10.1371/journal.pbio.1002299.s008
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak,
E., & Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the
most sensitive gene deletion strains for all drugs. Public Library of Science.
https://doi.org/10.1371/journal.pbio.1002299.s008
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment
Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library
of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s008.
ieee: G. Chevereau et al., “Gene ontology enrichment analysis for the most
sensitive gene deletion strains for all drugs.” Public Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach
MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion
strains for all drugs, Public Library of Science, 10.1371/journal.pbio.1002299.s008.
mla: Chevereau, Guillaume, et al. Gene Ontology Enrichment Analysis for the Most
Sensitive Gene Deletion Strains for All Drugs. Public Library of Science,
2015, doi:10.1371/journal.pbio.1002299.s008.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak,
M.T. Bollenbach, (2015).
date_created: 2021-08-03T07:05:16Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2023-02-23T10:07:02Z
day: '18'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299.s008
month: '11'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1619'
relation: used_in_publication
status: public
status: public
title: Gene ontology enrichment analysis for the most sensitive gene deletion strains
for all drugs
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...