---
_id: '12478'
abstract:
- lang: eng
text: In Gram negative bacteria, the multiple antibiotic resistance or mar operon,
is known to control the expression of multi-drug efflux genes that protect bacteria
from a wide range of drugs. As many different chemical compounds can induce this
operon, identifying the parameters that govern the dynamics of its induction is
crucial to better characterize the processes of tolerance and resistance. Most
experiments have assumed that the properties of the mar transcriptional network
can be inferred from population measurements. However, measurements from an asynchronous
population of cells can mask underlying phenotypic variations of single cells.
We monitored the activity of the mar promoter in single Escherichia coli cells
in linear micro-colonies and established that the response to a steady level of
inducer was most heterogeneous within individual colonies for an intermediate
value of inducer. Specifically, sub-lineages defined by contiguous daughter-cells
exhibited similar promoter activity, whereas activity was greatly variable between
different sub-lineages. Specific sub-trees of uniform promoter activity persisted
over several generations. Statistical analyses of the lineages suggest that the
presence of these sub-trees is the signature of an inducible memory of the promoter
state that is transmitted from mother to daughter cells. This single-cell study
reveals that the degree of epigenetic inheritance changes as a function of inducer
concentration, suggesting that phenotypic inheritance may be an inducible phenotype.
acknowledgement: This work was supported by NIH P50 award P50GM081892-02 to the University
of Chicago, a catalyst grant from the Chicago Biomedical Consortium with support
from The Searle Funds at The Chicago Community Trust to PC, and a Yen Fellowship
to CCG. MA was partially supported by PAPIIT-UNAM grant IN-11322.
article_number: '1049255'
article_processing_charge: Yes
article_type: original
author:
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: L
full_name: Bruneaux, L
last_name: Bruneaux
- first_name: P
full_name: Oikonomou, P
last_name: Oikonomou
- first_name: M
full_name: Aldana, M
last_name: Aldana
- first_name: P
full_name: Cluzel, P
last_name: Cluzel
citation:
ama: Guet CC, Bruneaux L, Oikonomou P, Aldana M, Cluzel P. Monitoring lineages of
growing and dividing bacteria reveals an inducible memory of mar operon
expression. Frontiers in Microbiology. 2023;14. doi:10.3389/fmicb.2023.1049255
apa: Guet, C. C., Bruneaux, L., Oikonomou, P., Aldana, M., & Cluzel, P. (2023).
Monitoring lineages of growing and dividing bacteria reveals an inducible memory
of mar operon expression. Frontiers in Microbiology. Frontiers.
https://doi.org/10.3389/fmicb.2023.1049255
chicago: Guet, Calin C, L Bruneaux, P Oikonomou, M Aldana, and P Cluzel. “Monitoring
Lineages of Growing and Dividing Bacteria Reveals an Inducible Memory of Mar
Operon Expression.” Frontiers in Microbiology. Frontiers, 2023. https://doi.org/10.3389/fmicb.2023.1049255.
ieee: C. C. Guet, L. Bruneaux, P. Oikonomou, M. Aldana, and P. Cluzel, “Monitoring
lineages of growing and dividing bacteria reveals an inducible memory of mar
operon expression,” Frontiers in Microbiology, vol. 14. Frontiers, 2023.
ista: Guet CC, Bruneaux L, Oikonomou P, Aldana M, Cluzel P. 2023. Monitoring lineages
of growing and dividing bacteria reveals an inducible memory of mar operon
expression. Frontiers in Microbiology. 14, 1049255.
mla: Guet, Calin C., et al. “Monitoring Lineages of Growing and Dividing Bacteria
Reveals an Inducible Memory of Mar Operon Expression.” Frontiers in
Microbiology, vol. 14, 1049255, Frontiers, 2023, doi:10.3389/fmicb.2023.1049255.
short: C.C. Guet, L. Bruneaux, P. Oikonomou, M. Aldana, P. Cluzel, Frontiers in
Microbiology 14 (2023).
date_created: 2023-02-02T08:13:28Z
date_published: 2023-06-20T00:00:00Z
date_updated: 2023-08-02T06:25:04Z
day: '20'
ddc:
- '570'
department:
- _id: CaGu
doi: 10.3389/fmicb.2023.1049255
external_id:
isi:
- '001030002600001'
pmid:
- '37485524'
file:
- access_level: open_access
checksum: 7dd322347512afaa5daf72a0154f2f07
content_type: application/pdf
creator: dernst
date_created: 2023-07-31T07:16:34Z
date_updated: 2023-07-31T07:16:34Z
file_id: '13322'
file_name: 2023_FrontiersMicrobiology_Guet.pdf
file_size: 6452841
relation: main_file
success: 1
file_date_updated: 2023-07-31T07:16:34Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: Frontiers in Microbiology
publication_identifier:
eissn:
- 1664-302X
publication_status: published
publisher: Frontiers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Monitoring lineages of growing and dividing bacteria reveals an inducible memory
of mar operon expression
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: 14
year: '2023'
...
---
_id: '10939'
abstract:
- lang: eng
text: Understanding and characterising biochemical processes inside single cells
requires experimental platforms that allow one to perturb and observe the dynamics
of such processes as well as computational methods to build and parameterise models
from the collected data. Recent progress with experimental platforms and optogenetics
has made it possible to expose each cell in an experiment to an individualised
input and automatically record cellular responses over days with fine time resolution.
However, methods to infer parameters of stochastic kinetic models from single-cell
longitudinal data have generally been developed under the assumption that experimental
data is sparse and that responses of cells to at most a few different input perturbations
can be observed. Here, we investigate and compare different approaches for calculating
parameter likelihoods of single-cell longitudinal data based on approximations
of the chemical master equation (CME) with a particular focus on coupling the
linear noise approximation (LNA) or moment closure methods to a Kalman filter.
We show that, as long as cells are measured sufficiently frequently, coupling
the LNA to a Kalman filter allows one to accurately approximate likelihoods and
to infer model parameters from data even in cases where the LNA provides poor
approximations of the CME. Furthermore, the computational cost of filtering-based
iterative likelihood evaluation scales advantageously in the number of measurement
times and different input perturbations and is thus ideally suited for data obtained
from modern experimental platforms. To demonstrate the practical usefulness of
these results, we perform an experiment in which single cells, equipped with an
optogenetic gene expression system, are exposed to various different light-input
sequences and measured at several hundred time points and use parameter inference
based on iterative likelihood evaluation to parameterise a stochastic model of
the system.
acknowledgement: We thank Virgile Andreani for useful discussions about the model
and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts
of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
article_number: e1009950
article_processing_charge: No
article_type: original
author:
- first_name: Anđela
full_name: Davidović, Anđela
last_name: Davidović
- first_name: Remy P
full_name: Chait, Remy P
id: 3464AE84-F248-11E8-B48F-1D18A9856A87
last_name: Chait
orcid: 0000-0003-0876-3187
- first_name: Gregory
full_name: Batt, Gregory
last_name: Batt
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 2022;18(3). doi:10.1371/journal.pcbi.1009950
apa: Davidović, A., Chait, R. P., Batt, G., & Ruess, J. (2022). Parameter inference
for stochastic biochemical models from perturbation experiments parallelised at
the single cell level. PLoS Computational Biology. Public Library of Science.
https://doi.org/10.1371/journal.pcbi.1009950
chicago: Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter
Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised
at the Single Cell Level.” PLoS Computational Biology. Public Library of
Science, 2022. https://doi.org/10.1371/journal.pcbi.1009950.
ieee: A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for
stochastic biochemical models from perturbation experiments parallelised at the
single cell level,” PLoS Computational Biology, vol. 18, no. 3. Public
Library of Science, 2022.
ista: Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 18(3), e1009950.
mla: Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models
from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS
Computational Biology, vol. 18, no. 3, e1009950, Public Library of Science,
2022, doi:10.1371/journal.pcbi.1009950.
short: A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18
(2022).
date_created: 2022-04-03T22:01:42Z
date_published: 2022-03-18T00:00:00Z
date_updated: 2022-04-04T10:21:53Z
day: '18'
ddc:
- '570'
- '000'
department:
- _id: CaGu
doi: 10.1371/journal.pcbi.1009950
file:
- access_level: open_access
checksum: 458ef542761fb714ced214f240daf6b2
content_type: application/pdf
creator: dernst
date_created: 2022-04-04T10:14:39Z
date_updated: 2022-04-04T10:14:39Z
file_id: '10947'
file_name: 2022_PLoSCompBio_Davidovic.pdf
file_size: 2958642
relation: main_file
success: 1
file_date_updated: 2022-04-04T10:14:39Z
has_accepted_license: '1'
intvolume: ' 18'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://gitlab.pasteur.fr/adavidov/inferencelnakf
scopus_import: '1'
status: public
title: Parameter inference for stochastic biochemical models from perturbation experiments
parallelised at the single cell level
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: 18
year: '2022'
...
---
_id: '11713'
abstract:
- lang: eng
text: "Objective: MazF is a sequence-specific endoribonuclease-toxin of the MazEF
toxin–antitoxin system. MazF cleaves single-stranded ribonucleic acid (RNA) regions
at adenine–cytosine–adenine (ACA) sequences in the bacterium Escherichia coli.
The MazEF system has been used in various biotechnology and synthetic biology
applications. In this study, we infer how ectopic mazF overexpression affects
production of heterologous proteins. To this end, we quantified the levels of
fluorescent proteins expressed in E. coli from reporters translated from the ACA-containing
or ACA-less messenger RNAs (mRNAs). Additionally, we addressed the impact of the
5′-untranslated region of these reporter mRNAs under the same conditions by comparing
expression from mRNAs that comprise (canonical mRNA) or lack this region (leaderless
mRNA).\r\nResults: Flow cytometry analysis indicates that during mazF overexpression,
fluorescent proteins are translated from the canonical as well as leaderless mRNAs.
Our analysis further indicates that longer mazF overexpression generally increases
the concentration of fluorescent proteins translated from ACA-less mRNAs, however
it also substantially increases bacterial population heterogeneity. Finally, our
results suggest that the strength and duration of mazF overexpression should be
optimized for each experimental setup, to maximize the heterologous protein production
and minimize the amount of phenotypic heterogeneity in bacterial populations,
which is unfavorable in biotechnological processes."
acknowledgement: "We acknowledge the Max Perutz Labs FACS Facility together with Thomas
Sauer. NN is grateful to Călin C. Guet for his support.\r\nThis work was funded
by the Elise Richter grant V738 of the Austrian Science Fund (FWF), and the FWF
Lise Meitner grant M1697, to NN; and by the FWF grant P22249, FWF Special Research
Program RNA-REG F43 (subproject F4316), and FWF doctoral program RNA Biology (W1207),
to IM. Open access funding provided by the Austrian Science Fund."
article_number: '173'
article_processing_charge: No
article_type: letter_note
author:
- first_name: Nela
full_name: Nikolic, Nela
id: 42D9CABC-F248-11E8-B48F-1D18A9856A87
last_name: Nikolic
orcid: 0000-0001-9068-6090
- first_name: Martina
full_name: Sauert, Martina
last_name: Sauert
- first_name: Tanino G.
full_name: Albanese, Tanino G.
last_name: Albanese
- first_name: Isabella
full_name: Moll, Isabella
last_name: Moll
citation:
ama: Nikolic N, Sauert M, Albanese TG, Moll I. Quantifying heterologous gene expression
during ectopic MazF production in Escherichia coli. BMC Research Notes.
2022;15. doi:10.1186/s13104-022-06061-9
apa: Nikolic, N., Sauert, M., Albanese, T. G., & Moll, I. (2022). Quantifying
heterologous gene expression during ectopic MazF production in Escherichia coli.
BMC Research Notes. Springer Nature. https://doi.org/10.1186/s13104-022-06061-9
chicago: Nikolic, Nela, Martina Sauert, Tanino G. Albanese, and Isabella Moll. “Quantifying
Heterologous Gene Expression during Ectopic MazF Production in Escherichia Coli.”
BMC Research Notes. Springer Nature, 2022. https://doi.org/10.1186/s13104-022-06061-9.
ieee: N. Nikolic, M. Sauert, T. G. Albanese, and I. Moll, “Quantifying heterologous
gene expression during ectopic MazF production in Escherichia coli,” BMC Research
Notes, vol. 15. Springer Nature, 2022.
ista: Nikolic N, Sauert M, Albanese TG, Moll I. 2022. Quantifying heterologous gene
expression during ectopic MazF production in Escherichia coli. BMC Research Notes.
15, 173.
mla: Nikolic, Nela, et al. “Quantifying Heterologous Gene Expression during Ectopic
MazF Production in Escherichia Coli.” BMC Research Notes, vol. 15, 173,
Springer Nature, 2022, doi:10.1186/s13104-022-06061-9.
short: N. Nikolic, M. Sauert, T.G. Albanese, I. Moll, BMC Research Notes 15 (2022).
date_created: 2022-08-01T09:04:27Z
date_published: 2022-05-13T00:00:00Z
date_updated: 2022-08-01T09:27:40Z
day: '13'
ddc:
- '570'
department:
- _id: CaGu
doi: 10.1186/s13104-022-06061-9
external_id:
pmid:
- '35562780'
file:
- access_level: open_access
checksum: 008156e5340e9789f0f6d82bde4d347a
content_type: application/pdf
creator: dernst
date_created: 2022-08-01T09:24:42Z
date_updated: 2022-08-01T09:24:42Z
file_id: '11714'
file_name: 2022_BMCResearchNotes_Nikolic.pdf
file_size: 1545310
relation: main_file
success: 1
file_date_updated: 2022-08-01T09:24:42Z
has_accepted_license: '1'
intvolume: ' 15'
keyword:
- General Biochemistry
- Genetics and Molecular Biology
- General Medicine
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 26956E74-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: V00738
name: Bacterial toxin-antitoxin systems as antiphage defense mechanisms
publication: BMC Research Notes
publication_identifier:
issn:
- 1756-0500
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1186/s13104-022-06152-7
scopus_import: '1'
status: public
title: Quantifying heterologous gene expression during ectopic MazF production in
Escherichia coli
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: 15
year: '2022'
...
---
_id: '10736'
abstract:
- lang: eng
text: Predicting function from sequence is a central problem of biology. Currently,
this is possible only locally in a narrow mutational neighborhood around a wildtype
sequence rather than globally from any sequence. Using random mutant libraries,
we developed a biophysical model that accounts for multiple features of σ70 binding
bacterial promoters to predict constitutive gene expression levels from any sequence.
We experimentally and theoretically estimated that 10–20% of random sequences
lead to expression and ~80% of non-expressing sequences are one mutation away
from a functional promoter. The potential for generating expression from random
sequences is so pervasive that selection acts against σ70-RNA polymerase binding
sites even within inter-genic, promoter-containing regions. This pervasiveness
of σ70-binding sites implies that emergence of promoters is not the limiting step
in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter
function into a mechanistic model enabled not only more accurate predictions of
gene expression levels, but also identified that promoters evolve more rapidly
than previously thought.
acknowledgement: 'We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao,
Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on
the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded
by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML;
IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme
7 (2007–2013, grant agreement number 648440) to JPB.'
article_number: e64543
article_processing_charge: No
article_type: original
author:
- first_name: Mato
full_name: Lagator, Mato
id: 345D25EC-F248-11E8-B48F-1D18A9856A87
last_name: Lagator
- first_name: Srdjan
full_name: Sarikas, Srdjan
id: 35F0286E-F248-11E8-B48F-1D18A9856A87
last_name: Sarikas
- first_name: Magdalena
full_name: Steinrueck, Magdalena
last_name: Steinrueck
- first_name: David
full_name: Toledo-Aparicio, David
last_name: Toledo-Aparicio
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function
and evolution from random sequences. eLife. 2022;11. doi:10.7554/eLife.64543
apa: Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J.
P., Guet, C. C., & Tkačik, G. (2022). Predicting bacterial promoter function
and evolution from random sequences. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.64543
chicago: Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio,
Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter
Function and Evolution from Random Sequences.” ELife. eLife Sciences Publications,
2022. https://doi.org/10.7554/eLife.64543.
ieee: M. Lagator et al., “Predicting bacterial promoter function and evolution
from random sequences,” eLife, vol. 11. eLife Sciences Publications, 2022.
ista: Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC,
Tkačik G. 2022. Predicting bacterial promoter function and evolution from random
sequences. eLife. 11, e64543.
mla: Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution
from Random Sequences.” ELife, vol. 11, e64543, eLife Sciences Publications,
2022, doi:10.7554/eLife.64543.
short: M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback,
C.C. Guet, G. Tkačik, ELife 11 (2022).
date_created: 2022-02-06T23:01:32Z
date_published: 2022-01-26T00:00:00Z
date_updated: 2023-08-02T14:09:02Z
day: '26'
ddc:
- '576'
department:
- _id: CaGu
- _id: GaTk
- _id: NiBa
doi: 10.7554/eLife.64543
ec_funded: 1
external_id:
isi:
- '000751104400001'
pmid:
- '35080492'
file:
- access_level: open_access
checksum: decdcdf600ff51e9a9703b49ca114170
content_type: application/pdf
creator: cchlebak
date_created: 2022-02-07T07:14:09Z
date_updated: 2022-02-07T07:14:09Z
file_id: '10739'
file_name: 2022_ELife_Lagator.pdf
file_size: 5604343
relation: main_file
success: 1
file_date_updated: 2022-02-07T07:14:09Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2578D616-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '648440'
name: Selective Barriers to Horizontal Gene Transfer
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predicting bacterial promoter function and evolution from random sequences
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: '2022'
...
---
_id: '10812'
abstract:
- lang: eng
text: Several promising strategies based on combining or cycling different antibiotics
have been proposed to increase efficacy and counteract resistance evolution, but
we still lack a deep understanding of the physiological responses and genetic
mechanisms that underlie antibiotic interactions and the clinical applicability
of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological
stress responses and emerging resistance mutations (occurring at different time
scales) generate complex and often unpredictable dynamics. In this Review, we
present our current understanding of bacterial cell physiology and genetics of
responses to antibiotics. We emphasize recently discovered mechanisms of synergistic
and antagonistic drug interactions, hysteresis in temporal interactions between
antibiotics that arise from microbial physiology and interactions between antibiotics
and resistance mutations that can cause collateral sensitivity or cross-resistance.
We discuss possible connections between the different phenomena and indicate relevant
research directions. A better and more unified understanding of drug and genetic
interactions is likely to advance antibiotic therapy.
acknowledgement: The authors thank B. Kavčič and H. Schulenburg for constructive feedback
on the manuscript.
article_processing_charge: No
article_type: review
author:
- first_name: Roderich
full_name: Römhild, Roderich
id: 68E56E44-62B0-11EA-B963-444F3DDC885E
last_name: Römhild
orcid: 0000-0001-9480-5261
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
- first_name: Dan I.
full_name: Andersson, Dan I.
last_name: Andersson
citation:
ama: Römhild R, Bollenbach MT, Andersson DI. The physiology and genetics of bacterial
responses to antibiotic combinations. Nature Reviews Microbiology. 2022;20:478-490.
doi:10.1038/s41579-022-00700-5
apa: Römhild, R., Bollenbach, M. T., & Andersson, D. I. (2022). The physiology
and genetics of bacterial responses to antibiotic combinations. Nature Reviews
Microbiology. Springer Nature. https://doi.org/10.1038/s41579-022-00700-5
chicago: Römhild, Roderich, Mark Tobias Bollenbach, and Dan I. Andersson. “The Physiology
and Genetics of Bacterial Responses to Antibiotic Combinations.” Nature Reviews
Microbiology. Springer Nature, 2022. https://doi.org/10.1038/s41579-022-00700-5.
ieee: R. Römhild, M. T. Bollenbach, and D. I. Andersson, “The physiology and genetics
of bacterial responses to antibiotic combinations,” Nature Reviews Microbiology,
vol. 20. Springer Nature, pp. 478–490, 2022.
ista: Römhild R, Bollenbach MT, Andersson DI. 2022. The physiology and genetics
of bacterial responses to antibiotic combinations. Nature Reviews Microbiology.
20, 478–490.
mla: Römhild, Roderich, et al. “The Physiology and Genetics of Bacterial Responses
to Antibiotic Combinations.” Nature Reviews Microbiology, vol. 20, Springer
Nature, 2022, pp. 478–90, doi:10.1038/s41579-022-00700-5.
short: R. Römhild, M.T. Bollenbach, D.I. Andersson, Nature Reviews Microbiology
20 (2022) 478–490.
date_created: 2022-03-04T04:33:49Z
date_published: 2022-08-01T00:00:00Z
date_updated: 2023-08-02T14:41:44Z
day: '01'
department:
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doi: 10.1038/s41579-022-00700-5
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keyword:
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- Infectious Diseases
language:
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month: '08'
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page: 478-490
pmid: 1
publication: Nature Reviews Microbiology
publication_identifier:
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issn:
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publisher: Springer Nature
quality_controlled: '1'
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status: public
title: The physiology and genetics of bacterial responses to antibiotic combinations
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 20
year: '2022'
...