---
_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: '7553'
abstract:
- lang: eng
text: Normative theories and statistical inference provide complementary approaches
for the study of biological systems. A normative theory postulates that organisms
have adapted to efficiently solve essential tasks, and proceeds to mathematically
work out testable consequences of such optimality; parameters that maximize the
hypothesized organismal function can be derived ab initio, without reference to
experimental data. In contrast, statistical inference focuses on efficient utilization
of data to learn model parameters, without reference to any a priori notion of
biological function, utility, or fitness. Traditionally, these two approaches
were developed independently and applied separately. Here we unify them in a coherent
Bayesian framework that embeds a normative theory into a family of maximum-entropy
“optimization priors.” This family defines a smooth interpolation between a data-rich
inference regime (characteristic of “bottom-up” statistical models), and a data-limited
ab inito prediction regime (characteristic of “top-down” normative theory). We
demonstrate the applicability of our framework using data from the visual cortex,
and argue that the flexibility it affords is essential to address a number of
fundamental challenges relating to inference and prediction in complex, high-dimensional
biological problems.
acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields
and Olivier Marre for providing the retinal receptive fields. W.M. was funded by
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers
Science grant no. HFSP RGP0032/2018.
article_processing_charge: No
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Thomas R
full_name: Sokolowski, Thomas R
id: 3E999752-F248-11E8-B48F-1D18A9856A87
last_name: Sokolowski
orcid: 0000-0002-1287-3779
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality
of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020
apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical
analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020
chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik.
“Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press,
2021. https://doi.org/10.1016/j.neuron.2021.01.020.
ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical
analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell
Press, p. 1227–1241.e5, 2021.
ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis
and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.
mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural
Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020.
short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021)
1227–1241.e5.
date_created: 2020-02-28T11:00:12Z
date_published: 2021-04-07T00:00:00Z
date_updated: 2024-03-06T14:22:51Z
day: '07'
department:
- _id: GaTk
doi: 10.1016/j.neuron.2021.01.020
ec_funded: 1
external_id:
isi:
- '000637809600006'
intvolume: ' 109'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/848374
month: '04'
oa: 1
oa_version: Preprint
page: 1227-1241.e5
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neuron
publication_status: published
publisher: Cell Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/can-evolution-be-predicted/
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Statistical analysis and optimality of neural systems
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
year: '2021'
...
---
_id: '10077'
abstract:
- lang: eng
text: Although much is known about how single neurons in the hippocampus represent
an animal’s position, how cell-cell interactions contribute to spatial coding
remains poorly understood. Using a novel statistical estimator and theoretical
modeling, both developed in the framework of maximum entropy models, we reveal
highly structured cell-to-cell interactions whose statistics depend on familiar
vs. novel environment. In both conditions the circuit interactions optimize the
encoding of spatial information, but for regimes that differ in the signal-to-noise
ratio of their spatial inputs. Moreover, the topology of the interactions facilitates
linear decodability, making the information easy to read out by downstream circuits.
These findings suggest that the efficient coding hypothesis is not applicable
only to individual neuron properties in the sensory periphery, but also to neural
interactions in the central brain.
acknowledgement: We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for
the acquisition of the data. We thank Federico Stella for comments on an earlier
version of the manuscript. MN was supported by European Union Horizon 2020 grant
665385, JC was supported by European Research Council consolidator grant 281511,
GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported
by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01,
by the National Science Foundation under NSF Award No. 1922658 and a Google faculty
award.
article_processing_charge: No
author:
- first_name: Michele
full_name: Nardin, Michele
id: 30BD0376-F248-11E8-B48F-1D18A9856A87
last_name: Nardin
orcid: 0000-0001-8849-6570
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
citation:
ama: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1
interactions optimizes spatial coding across experience. bioRxiv. doi:10.1101/2021.09.28.460602
apa: Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (n.d.). The structure
of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv.
Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.09.28.460602
chicago: Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin.
“The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across
Experience.” BioRxiv. Cold Spring Harbor Laboratory, n.d. https://doi.org/10.1101/2021.09.28.460602.
ieee: M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal
CA1 interactions optimizes spatial coding across experience,” bioRxiv.
Cold Spring Harbor Laboratory.
ista: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1
interactions optimizes spatial coding across experience. bioRxiv, 10.1101/2021.09.28.460602.
mla: Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes
Spatial Coding across Experience.” BioRxiv, Cold Spring Harbor Laboratory,
doi:10.1101/2021.09.28.460602.
short: M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).
date_created: 2021-10-04T06:23:34Z
date_published: 2021-09-29T00:00:00Z
date_updated: 2024-03-28T23:30:16Z
day: '29'
department:
- _id: GradSch
- _id: JoCs
- _id: GaTk
doi: 10.1101/2021.09.28.460602
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.biorxiv.org/content/10.1101/2021.09.28.460602
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 257A4776-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '281511'
name: Memory-related information processing in neuronal circuits of the hippocampus
and entorhinal cortex
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication: bioRxiv
publication_status: submitted
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '11932'
relation: dissertation_contains
status: public
status: public
title: The structure of hippocampal CA1 interactions optimizes spatial coding across
experience
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: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '8105'
abstract:
- lang: eng
text: Physical and biological systems often exhibit intermittent dynamics with bursts
or avalanches (active states) characterized by power-law size and duration distributions.
These emergent features are typical of systems at the critical point of continuous
phase transitions, and have led to the hypothesis that such systems may self-organize
at criticality, i.e. without any fine tuning of parameters. Since the introduction
of the Bak-Tang-Wiesenfeld (BTW) model, the paradigm of self-organized criticality
(SOC) has been very fruitful for the analysis of emergent collective behaviors
in a number of systems, including the brain. Although considerable effort has
been devoted in identifying and modeling scaling features of burst and avalanche
statistics, dynamical aspects related to the temporal organization of bursts remain
often poorly understood or controversial. Of crucial importance to understand
the mechanisms responsible for emergent behaviors is the relationship between
active and quiet periods, and the nature of the correlations. Here we investigate
the dynamics of active (θ-bursts) and quiet states (δ-bursts) in brain activity
during the sleep-wake cycle. We show the duality of power-law (θ, active phase)
and exponential-like (δ, quiescent phase) duration distributions, typical of SOC,
jointly emerge with power-law temporal correlations and anti-correlated coupling
between active and quiet states. Importantly, we demonstrate that such temporal
organization shares important similarities with earthquake dynamics, and propose
that specific power-law correlations and coupling between active and quiet states
are distinctive characteristics of a class of systems with self-organization at
criticality.
article_number: '00005'
article_processing_charge: No
article_type: original
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Jilin W.J.L.
full_name: Wang, Jilin W.J.L.
last_name: Wang
- first_name: Xiyun
full_name: Zhang, Xiyun
last_name: Zhang
- first_name: Plamen Ch
full_name: Ivanov, Plamen Ch
last_name: Ivanov
citation:
ama: Lombardi F, Wang JWJL, Zhang X, Ivanov PC. Power-law correlations and coupling
of active and quiet states underlie a class of complex systems with self-organization
at criticality. EPJ Web of Conferences. 2020;230. doi:10.1051/epjconf/202023000005
apa: Lombardi, F., Wang, J. W. J. L., Zhang, X., & Ivanov, P. C. (2020). Power-law
correlations and coupling of active and quiet states underlie a class of complex
systems with self-organization at criticality. EPJ Web of Conferences.
EDP Sciences. https://doi.org/10.1051/epjconf/202023000005
chicago: Lombardi, Fabrizio, Jilin W.J.L. Wang, Xiyun Zhang, and Plamen Ch Ivanov.
“Power-Law Correlations and Coupling of Active and Quiet States Underlie a Class
of Complex Systems with Self-Organization at Criticality.” EPJ Web of Conferences.
EDP Sciences, 2020. https://doi.org/10.1051/epjconf/202023000005.
ieee: F. Lombardi, J. W. J. L. Wang, X. Zhang, and P. C. Ivanov, “Power-law correlations
and coupling of active and quiet states underlie a class of complex systems with
self-organization at criticality,” EPJ Web of Conferences, vol. 230. EDP
Sciences, 2020.
ista: Lombardi F, Wang JWJL, Zhang X, Ivanov PC. 2020. Power-law correlations and
coupling of active and quiet states underlie a class of complex systems with self-organization
at criticality. EPJ Web of Conferences. 230, 00005.
mla: Lombardi, Fabrizio, et al. “Power-Law Correlations and Coupling of Active and
Quiet States Underlie a Class of Complex Systems with Self-Organization at Criticality.”
EPJ Web of Conferences, vol. 230, 00005, EDP Sciences, 2020, doi:10.1051/epjconf/202023000005.
short: F. Lombardi, J.W.J.L. Wang, X. Zhang, P.C. Ivanov, EPJ Web of Conferences
230 (2020).
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