---
_id: '12081'
abstract:
- lang: eng
text: 'Selection accumulates information in the genome—it guides stochastically
evolving populations toward states (genotype frequencies) that would be unlikely
under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence
between the actual distribution of genotype frequencies and the corresponding
neutral distribution. First, we show that this population-level information sets
an upper bound on the information at the level of genotype and phenotype, limiting
how precisely they can be specified by selection. Next, we study how the accumulation
and maintenance of information is limited by the cost of selection, measured as
the genetic load or the relative fitness variance, both of which we connect to
the control-theoretic KL cost of control. The information accumulation rate is
upper bounded by the population size times the cost of selection. This bound is
very general, and applies across models (Wright–Fisher, Moran, diffusion) and
to arbitrary forms of selection, mutation, and recombination. Finally, the cost
of maintaining information depends on how it is encoded: Specifying a single allele
out of two is expensive, but one bit encoded among many weakly specified loci
(as in a polygenic trait) is cheap.'
acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and
two anonymous reviewers for discussions and comments on the manuscript. G.T. and
M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018.
N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”
article_number: e2123152119
article_processing_charge: No
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 2022;119(36).
doi:10.1073/pnas.2123152119
apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance
of information in evolution. Proceedings of the National Academy of Sciences.
Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119
chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and
Maintenance of Information in Evolution.” Proceedings of the National Academy
of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119.
ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information
in evolution,” Proceedings of the National Academy of Sciences, vol. 119,
no. 36. Proceedings of the National Academy of Sciences, 2022.
ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.
mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.”
Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119,
Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119.
short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of
Sciences 119 (2022).
date_created: 2022-09-11T22:01:55Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2024-03-06T14:22:51Z
day: '29'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1073/pnas.2123152119
ec_funded: 1
external_id:
isi:
- '000889278400014'
pmid:
- '36037343'
file:
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content_type: application/pdf
creator: dernst
date_created: 2022-09-12T08:08:12Z
date_updated: 2022-09-12T08:08:12Z
file_id: '12091'
file_name: 2022_PNAS_Hledik.pdf
file_size: 2165752
relation: main_file
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intvolume: ' 119'
isi: 1
issue: '36'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: Proceedings of the National Academy of Sciences
quality_controlled: '1'
related_material:
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Accumulation and maintenance of information in evolution
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: 119
year: '2022'
...
---
_id: '10535'
abstract:
- lang: eng
text: Realistic models of biological processes typically involve interacting components
on multiple scales, driven by changing environment and inherent stochasticity.
Such models are often analytically and numerically intractable. We revisit a dynamic
maximum entropy method that combines a static maximum entropy with a quasi-stationary
approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed
by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics,
without the need to track microscopic details. Although the method has been previously
applied to a few (rather complicated) applications in population genetics, our
main goal here is to explain and to better understand how the method works. We
demonstrate the usefulness of the method for two widely studied stochastic problems,
highlighting its accuracy in capturing important macroscopic quantities even in
rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process,
the method recovers the exact dynamics whilst for a stochastic island model with
migration from other habitats, the approximation retains high macroscopic accuracy
under a wide range of scenarios in a dynamic environment.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "Computational resources for the study were provided by the Institute
of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant
Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20."
article_number: e1009661
article_processing_charge: No
article_type: original
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation
of structured population dynamics. PLoS Computational Biology. 2021;17(12).
doi:10.1371/journal.pcbi.1009661
apa: Bodova, K., Szep, E., & Barton, N. H. (2021). Dynamic maximum entropy provides
accurate approximation of structured population dynamics. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009661
chicago: Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy
Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational
Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009661.
ieee: K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate
approximation of structured population dynamics,” PLoS Computational Biology,
vol. 17, no. 12. Public Library of Science, 2021.
ista: Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate
approximation of structured population dynamics. PLoS Computational Biology. 17(12),
e1009661.
mla: Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation
of Structured Population Dynamics.” PLoS Computational Biology, vol. 17,
no. 12, e1009661, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009661.
short: K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).
date_created: 2021-12-12T23:01:27Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-08-01T10:48:04Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1371/journal.pcbi.1009661
external_id:
arxiv:
- '2102.03669'
pmid:
- '34851948'
file:
- access_level: open_access
checksum: dcd185d4f7e0acee25edf1d6537f447e
content_type: application/pdf
creator: dernst
date_created: 2022-05-16T08:53:11Z
date_updated: 2022-05-16T08:53:11Z
file_id: '11383'
file_name: 2021_PLOsComBio_Bodova.pdf
file_size: 2299486
relation: main_file
success: 1
file_date_updated: 2022-05-16T08:53:11Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Dynamic maximum entropy provides accurate approximation of structured population
dynamics
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: 17
year: '2021'
...
---
_id: '10912'
abstract:
- lang: eng
text: Brain dynamics display collective phenomena as diverse as neuronal oscillations
and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic
scale, whereas avalanches are scale-free cascades of neural activity. Here we
show that such antithetic features can coexist in a very generic class of adaptive
neural networks. In the most simple yet fully microscopic model from this class
we make direct contact with human brain resting-state activity recordings via
tractable inference of the model's two essential parameters. The inferred model
quantitatively captures the dynamics over a broad range of scales, from single
sensor fluctuations, collective behaviors of nearly-synchronous extreme events
on multiple sensors, to neuronal avalanches unfolding over multiple sensors across
multiple time-bins. Importantly, the inferred parameters correlate with model-independent
signatures of "closeness to criticality", suggesting that the coexistence of scale-specific
(neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity
occurs close to a non-equilibrium critical point at the onset of self-sustained
oscillations.
acknowledgement: "FL acknowledges support from the European Union’s Horizon 2020 research
and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411.
GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone
Grant\r\nNo. P34015."
article_processing_charge: No
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Selver
full_name: Pepic, Selver
id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425
last_name: Pepic
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Daniele
full_name: De Martino, Daniele
last_name: De Martino
citation:
ama: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. doi:10.48550/ARXIV.2108.06686
apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (n.d.).
Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. https://doi.org/10.48550/ARXIV.2108.06686
chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele
De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.”
arXiv, n.d. https://doi.org/10.48550/ARXIV.2108.06686.
ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying
the coexistence of neuronal oscillations and avalanches.” arXiv.
ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. 10.48550/ARXIV.2108.06686.
mla: Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations
and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686.
short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).
date_created: 2022-03-21T11:41:28Z
date_published: 2021-08-17T00:00:00Z
date_updated: 2022-03-22T07:53:18Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.48550/ARXIV.2108.06686
ec_funded: 1
external_id:
arxiv:
- '2108.06686'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2108.06686
month: '08'
oa: 1
oa_version: Preprint
page: '37'
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication_status: submitted
publisher: arXiv
status: public
title: Quantifying the coexistence of neuronal oscillations and avalanches
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10579'
abstract:
- lang: eng
text: 'We consider a totally asymmetric simple exclusion process (TASEP) consisting
of particles on a lattice that require binding by a "token" to move. Using a combination
of theory and simulations, we address the following questions: (i) How token binding
kinetics affects the current-density relation; (ii) How the current-density relation
depends on the scarcity of tokens; (iii) How tokens propagate the effects of the
locally-imposed disorder (such a slow site) over the entire lattice; (iv) How
a shared pool of tokens couples concurrent TASEPs running on multiple lattices;
(v) How our results translate to TASEPs with open boundaries that exchange particles
with the reservoir. Since real particle motion (including in systems that inspired
the standard TASEP model, e.g., protein synthesis or movement of molecular motors)
is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven
TASEP dynamics analyzed in this paper should allow for a better understanding
of real systems and enable a closer match between TASEP theory and experimental
observations.'
acknowledgement: B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for
their help with the usage of the cluster. B.K. additionally thanks Călin Guet and
his group for help and advice. We thank M. Hennessey-Wesen for constructive comments
on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting
a typographical error in Eq. (49) in the preprint version of this paper.
article_number: '2112.13558'
article_processing_charge: No
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv. doi:10.48550/arXiv.2112.13558
apa: Kavcic, B., & Tkačik, G. (n.d.). Token-driven totally asymmetric simple
exclusion process. arXiv. https://doi.org/10.48550/arXiv.2112.13558
chicago: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple
Exclusion Process.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2112.13558.
ieee: B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion
process,” arXiv. .
ista: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv, 2112.13558.
mla: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion
Process.” ArXiv, 2112.13558, doi:10.48550/arXiv.2112.13558.
short: B. Kavcic, G. Tkačik, ArXiv (n.d.).
date_created: 2021-12-28T06:52:09Z
date_published: 2021-12-27T00:00:00Z
date_updated: 2023-05-03T10:54:05Z
day: '27'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.48550/arXiv.2112.13558
external_id:
arxiv:
- '2112.13558'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.13558
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Token-driven totally asymmetric simple exclusion process
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '7463'
abstract:
- lang: eng
text: Resting-state brain activity is characterized by the presence of neuronal
avalanches showing absence of characteristic size. Such evidence has been interpreted
in the context of criticality and associated with the normal functioning of the
brain. A distinctive attribute of systems at criticality is the presence of long-range
correlations. Thus, to verify the hypothesis that the brain operates close to
a critical point and consequently assess deviations from criticality for diagnostic
purposes, it is of primary importance to robustly and reliably characterize correlations
in resting-state brain activity. Recent works focused on the analysis of narrow-band
electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude
envelope, showing evidence of long-range temporal correlations (LRTC) in neural
oscillations. However, brain activity is a broadband phenomenon, and a significant
piece of information useful to precisely discriminate between normal (critical)
and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal
cortical dynamics. Here we propose to characterize the temporal correlations in
the broadband brain activity through the lens of neuronal avalanches. To this
end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding
neuronal avalanche sequences, and study their temporal correlations. We demonstrate
that the broadband resting-state brain activity consistently exhibits long-range
power-law correlations in both EEG and MEG recordings, with similar values of
the scaling exponents. Importantly, although we observe that the avalanche size
distribution depends on scale parameters, scaling exponents characterizing long-range
correlations are quite robust. In particular, they are independent of the temporal
binning (scale of analysis), indicating that our analysis captures intrinsic characteristics
of the underlying dynamics. Because neuronal avalanches constitute a fundamental
feature of neural systems with universal characteristics, the proposed approach
may serve as a general, systems- and experiment-independent procedure to infer
the existence of underlying long-range correlations in extended neural systems,
and identify pathological behaviors in the complex spatio-temporal interplay of
cortical rhythms.
acknowledgement: LdA would like to acknowledge the financial support from MIUR-PRIN2017
WZFTZP and VALERE:VAnviteLli pEr la RicErca 2019. FL acknowledges support from the
European Union’s Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie Grant Agreement No. 754411. HJH would like to thank the Agencies
CAPES and FUNCAP for financial support.
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: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Hans J
full_name: Herrmann, Hans J
last_name: Herrmann
- first_name: Lucilla
full_name: de Arcangelis, Lucilla
last_name: de Arcangelis
citation:
ama: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations
in the broadband resting state activity of the human brain revealed by neuronal
avalanches. Neurocomputing. 2021;461:657-666. doi:10.1016/j.neucom.2020.05.126
apa: Lombardi, F., Shriki, O., Herrmann, H. J., & de Arcangelis, L. (2021).
Long-range temporal correlations in the broadband resting state activity of the
human brain revealed by neuronal avalanches. Neurocomputing. Elsevier.
https://doi.org/10.1016/j.neucom.2020.05.126
chicago: Lombardi, Fabrizio, Oren Shriki, Hans J Herrmann, and Lucilla de Arcangelis.
“Long-Range Temporal Correlations in the Broadband Resting State Activity of the
Human Brain Revealed by Neuronal Avalanches.” Neurocomputing. Elsevier,
2021. https://doi.org/10.1016/j.neucom.2020.05.126.
ieee: F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range
temporal correlations in the broadband resting state activity of the human brain
revealed by neuronal avalanches,” Neurocomputing, vol. 461. Elsevier, pp.
657–666, 2021.
ista: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. 2021. Long-range temporal
correlations in the broadband resting state activity of the human brain revealed
by neuronal avalanches. Neurocomputing. 461, 657–666.
mla: Lombardi, Fabrizio, et al. “Long-Range Temporal Correlations in the Broadband
Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” Neurocomputing,
vol. 461, Elsevier, 2021, pp. 657–66, doi:10.1016/j.neucom.2020.05.126.
short: F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461
(2021) 657–666.
date_created: 2020-02-06T16:09:14Z
date_published: 2021-05-13T00:00:00Z
date_updated: 2023-08-04T10:46:29Z
day: '13'
department:
- _id: GaTk
doi: 10.1016/j.neucom.2020.05.126
ec_funded: 1
external_id:
isi:
- '000704086300015'
intvolume: ' 461'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.02.03.930966
month: '05'
oa: 1
oa_version: Preprint
page: 657-666
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neurocomputing
publication_identifier:
eissn:
- 1872-8286
issn:
- 0925-2312
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Long-range temporal correlations in the broadband resting state activity of
the human brain revealed by neuronal avalanches
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 461
year: '2021'
...
---
_id: '9226'
abstract:
- lang: eng
text: 'Half a century after Lewis Wolpert''s seminal conceptual advance on how cellular
fates distribute in space, we provide a brief historical perspective on how the
concept of positional information emerged and influenced the field of developmental
biology and beyond. We focus on a modern interpretation of this concept in terms
of information theory, largely centered on its application to cell specification
in the early Drosophila embryo. We argue that a true physical variable (position)
is encoded in local concentrations of patterning molecules, that this mapping
is stochastic, and that the processes by which positions and corresponding cell
fates are determined based on these concentrations need to take such stochasticity
into account. With this approach, we shift the focus from biological mechanisms,
molecules, genes and pathways to quantitative systems-level questions: where does
positional information reside, how it is transformed and accessed during development,
and what fundamental limits it is subject to?'
acknowledgement: This work was supported in part by the National Science Foundation,
through the Center for the Physics of Biological Function (PHY-1734030), by the
National Institutes of Health (R01GM097275) and by the Fonds zur Förderung der wissenschaftlichen
Forschung (FWF P28844). Deposited in PMC for release after 12 months.
article_number: dev176065
article_processing_charge: No
article_type: original
author:
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
citation:
ama: Tkačik G, Gregor T. The many bits of positional information. Development.
2021;148(2). doi:10.1242/dev.176065
apa: Tkačik, G., & Gregor, T. (2021). The many bits of positional information.
Development. The Company of Biologists. https://doi.org/10.1242/dev.176065
chicago: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.”
Development. The Company of Biologists, 2021. https://doi.org/10.1242/dev.176065.
ieee: G. Tkačik and T. Gregor, “The many bits of positional information,” Development,
vol. 148, no. 2. The Company of Biologists, 2021.
ista: Tkačik G, Gregor T. 2021. The many bits of positional information. Development.
148(2), dev176065.
mla: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.”
Development, vol. 148, no. 2, dev176065, The Company of Biologists, 2021,
doi:10.1242/dev.176065.
short: G. Tkačik, T. Gregor, Development 148 (2021).
date_created: 2021-03-07T23:01:25Z
date_published: 2021-02-01T00:00:00Z
date_updated: 2023-08-07T13:57:30Z
day: '01'
department:
- _id: GaTk
doi: 10.1242/dev.176065
external_id:
isi:
- '000613906000007'
pmid:
- '33526425'
intvolume: ' 148'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1242/dev.176065
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Development
publication_identifier:
eissn:
- 1477-9129
publication_status: published
publisher: The Company of Biologists
quality_controlled: '1'
scopus_import: '1'
status: public
title: The many bits of positional information
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 148
year: '2021'
...
---
_id: '9439'
abstract:
- lang: eng
text: The ability to adapt to changes in stimulus statistics is a hallmark of sensory
systems. Here, we developed a theoretical framework that can account for the dynamics
of adaptation from an information processing perspective. We use this framework
to optimize and analyze adaptive sensory codes, and we show that codes optimized
for stationary environments can suffer from prolonged periods of poor performance
when the environment changes. To mitigate the adversarial effects of these environmental
changes, sensory systems must navigate tradeoffs between the ability to accurately
encode incoming stimuli and the ability to rapidly detect and adapt to changes
in the distribution of these stimuli. We derive families of codes that balance
these objectives, and we demonstrate their close match to experimentally observed
neural dynamics during mean and variance adaptation. Our results provide a unifying
perspective on adaptation across a range of sensory systems, environments, and
sensory tasks.
acknowledgement: We thank D. Kastner and T. Münch for generously providing figures
from their work. We also thank V. Jayaraman, M. Noorman, T. Ma, and K. Krishnamurthy
for useful discussions and feedback on the manuscript. W.F.M. was funded by the
European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie
Grant Agreement No. 754411. A.M.H. was supported by the Howard Hughes Medical Institute.
article_processing_charge: No
article_type: original
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Ann M.
full_name: Hermundstad, Ann M.
last_name: Hermundstad
citation:
ama: Mlynarski WF, Hermundstad AM. Efficient and adaptive sensory codes. Nature
Neuroscience. 2021;24:998-1009. doi:10.1038/s41593-021-00846-0
apa: Mlynarski, W. F., & Hermundstad, A. M. (2021). Efficient and adaptive sensory
codes. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-021-00846-0
chicago: Mlynarski, Wiktor F, and Ann M. Hermundstad. “Efficient and Adaptive Sensory
Codes.” Nature Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41593-021-00846-0.
ieee: W. F. Mlynarski and A. M. Hermundstad, “Efficient and adaptive sensory codes,”
Nature Neuroscience, vol. 24. Springer Nature, pp. 998–1009, 2021.
ista: Mlynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes.
Nature Neuroscience. 24, 998–1009.
mla: Mlynarski, Wiktor F., and Ann M. Hermundstad. “Efficient and Adaptive Sensory
Codes.” Nature Neuroscience, vol. 24, Springer Nature, 2021, pp. 998–1009,
doi:10.1038/s41593-021-00846-0.
short: W.F. Mlynarski, A.M. Hermundstad, Nature Neuroscience 24 (2021) 998–1009.
date_created: 2021-05-30T22:01:24Z
date_published: 2021-05-20T00:00:00Z
date_updated: 2023-08-08T13:51:14Z
day: '20'
department:
- _id: GaTk
doi: 10.1038/s41593-021-00846-0
ec_funded: 1
external_id:
isi:
- '000652577300003'
intvolume: ' 24'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/669200 '
month: '05'
oa: 1
oa_version: Preprint
page: 998-1009
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient and adaptive sensory codes
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 24
year: '2021'
...
---
_id: '9822'
abstract:
- lang: eng
text: Attachment of adhesive molecules on cell culture surfaces to restrict cell
adhesion to defined areas and shapes has been vital for the progress of in vitro
research. In currently existing patterning methods, a combination of pattern properties
such as stability, precision, specificity, high-throughput outcome, and spatiotemporal
control is highly desirable but challenging to achieve. Here, we introduce a versatile
and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent
patterning step and a subsequent functionalization of the pattern via click chemistry.
This two-step process is feasible on arbitrary surfaces and allows for generation
of sustainable patterns and gradients. The method is validated in different biological
systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining
the growth and migration of cells to the designated areas. We then implement a
sequential photopatterning approach by adding a second switchable patterning step,
allowing for spatiotemporal control over two distinct surface patterns. As a proof
of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis.
Our results show that the spatiotemporal control provided by our “sequential photopatterning”
system is essential for mimicking dynamic biological processes and that our innovative
approach has great potential for further applications in cell science.
acknowledgement: We would like to thank Charlott Leu for the production of our chromium
wafers, Louise Ritter for her contribution of the IF stainings in Figure 4, Shokoufeh
Teymouri for her help with the Bioinert coated slides, and finally Prof. Dr. Joachim
Rädler for his valuable scientific guidance.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Themistoklis
full_name: Zisis, Themistoklis
last_name: Zisis
- first_name: Jan
full_name: Schwarz, Jan
id: 346C1EC6-F248-11E8-B48F-1D18A9856A87
last_name: Schwarz
- first_name: Miriam
full_name: Balles, Miriam
last_name: Balles
- first_name: Maibritt
full_name: Kretschmer, Maibritt
last_name: Kretschmer
- first_name: Maria
full_name: Nemethova, Maria
id: 34E27F1C-F248-11E8-B48F-1D18A9856A87
last_name: Nemethova
- 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: Robert
full_name: Hauschild, Robert
id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87
last_name: Hauschild
orcid: 0000-0001-9843-3522
- first_name: Janina
full_name: Lange, Janina
last_name: Lange
- 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: Michael K
full_name: Sixt, Michael K
id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87
last_name: Sixt
orcid: 0000-0002-4561-241X
- first_name: Stefan
full_name: Zahler, Stefan
last_name: Zahler
citation:
ama: Zisis T, Schwarz J, Balles M, et al. Sequential and switchable patterning for
studying cellular processes under spatiotemporal control. ACS Applied Materials
and Interfaces. 2021;13(30):35545–35560. doi:10.1021/acsami.1c09850
apa: Zisis, T., Schwarz, J., Balles, M., Kretschmer, M., Nemethova, M., Chait, R.
P., … Zahler, S. (2021). Sequential and switchable patterning for studying cellular
processes under spatiotemporal control. ACS Applied Materials and Interfaces.
American Chemical Society. https://doi.org/10.1021/acsami.1c09850
chicago: Zisis, Themistoklis, Jan Schwarz, Miriam Balles, Maibritt Kretschmer, Maria
Nemethova, Remy P Chait, Robert Hauschild, et al. “Sequential and Switchable Patterning
for Studying Cellular Processes under Spatiotemporal Control.” ACS Applied
Materials and Interfaces. American Chemical Society, 2021. https://doi.org/10.1021/acsami.1c09850.
ieee: T. Zisis et al., “Sequential and switchable patterning for studying
cellular processes under spatiotemporal control,” ACS Applied Materials and
Interfaces, vol. 13, no. 30. American Chemical Society, pp. 35545–35560, 2021.
ista: Zisis T, Schwarz J, Balles M, Kretschmer M, Nemethova M, Chait RP, Hauschild
R, Lange J, Guet CC, Sixt MK, Zahler S. 2021. Sequential and switchable patterning
for studying cellular processes under spatiotemporal control. ACS Applied Materials
and Interfaces. 13(30), 35545–35560.
mla: Zisis, Themistoklis, et al. “Sequential and Switchable Patterning for Studying
Cellular Processes under Spatiotemporal Control.” ACS Applied Materials and
Interfaces, vol. 13, no. 30, American Chemical Society, 2021, pp. 35545–35560,
doi:10.1021/acsami.1c09850.
short: T. Zisis, J. Schwarz, M. Balles, M. Kretschmer, M. Nemethova, R.P. Chait,
R. Hauschild, J. Lange, C.C. Guet, M.K. Sixt, S. Zahler, ACS Applied Materials
and Interfaces 13 (2021) 35545–35560.
date_created: 2021-08-08T22:01:28Z
date_published: 2021-08-04T00:00:00Z
date_updated: 2023-08-10T14:22:48Z
day: '04'
ddc:
- '620'
- '570'
department:
- _id: MiSi
- _id: GaTk
- _id: Bio
- _id: CaGu
doi: 10.1021/acsami.1c09850
ec_funded: 1
external_id:
isi:
- '000683741400026'
pmid:
- '34283577'
file:
- access_level: open_access
checksum: b043a91d9f9200e467b970b692687ed3
content_type: application/pdf
creator: asandaue
date_created: 2021-08-09T09:44:03Z
date_updated: 2021-08-09T09:44:03Z
file_id: '9833'
file_name: 2021_ACSAppliedMaterialsAndInterfaces_Zisis.pdf
file_size: 7123293
relation: main_file
success: 1
file_date_updated: 2021-08-09T09:44:03Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
issue: '30'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 35545–35560
pmid: 1
project:
- _id: 25FE9508-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '724373'
name: Cellular navigation along spatial gradients
publication: ACS Applied Materials and Interfaces
publication_identifier:
eissn:
- '19448252'
issn:
- '19448244'
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sequential and switchable patterning for studying cellular processes under
spatiotemporal control
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: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13
year: '2021'
...
---
_id: '9828'
abstract:
- lang: eng
text: Amplitude demodulation is a classical operation used in signal processing.
For a long time, its effective applications in practice have been limited to narrowband
signals. In this work, we generalize amplitude demodulation to wideband signals.
We pose demodulation as a recovery problem of an oversampled corrupted signal
and introduce special iterative schemes belonging to the family of alternating
projection algorithms to solve it. Sensibly chosen structural assumptions on the
demodulation outputs allow us to reveal the high inferential accuracy of the method
over a rich set of relevant signals. This new approach surpasses current state-of-the-art
demodulation techniques apt to wideband signals in computational efficiency by
up to many orders of magnitude with no sacrifice in quality. Such performance
opens the door for applications of the amplitude demodulation procedure in new
contexts. In particular, the new method makes online and large-scale offline data
processing feasible, including the calculation of modulator-carrier pairs in higher
dimensions and poor sampling conditions, independent of the signal bandwidth.
We illustrate the utility and specifics of applications of the new method in practice
by using natural speech and synthetic signals.
acknowledgement: The author thanks his colleagues K. Huszár and G. Tkačik for valuable
discussions and comments on the manuscript.
article_processing_charge: No
article_type: original
author:
- first_name: Mantas
full_name: Gabrielaitis, Mantas
id: 4D5B0CBC-F248-11E8-B48F-1D18A9856A87
last_name: Gabrielaitis
orcid: 0000-0002-7758-2016
citation:
ama: Gabrielaitis M. Fast and accurate amplitude demodulation of wideband signals.
IEEE Transactions on Signal Processing. 2021;69:4039-4054. doi:10.1109/TSP.2021.3087899
apa: Gabrielaitis, M. (2021). Fast and accurate amplitude demodulation of wideband
signals. IEEE Transactions on Signal Processing. Institute of Electrical
and Electronics Engineers. https://doi.org/10.1109/TSP.2021.3087899
chicago: Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband
Signals.” IEEE Transactions on Signal Processing. Institute of Electrical
and Electronics Engineers, 2021. https://doi.org/10.1109/TSP.2021.3087899.
ieee: M. Gabrielaitis, “Fast and accurate amplitude demodulation of wideband signals,”
IEEE Transactions on Signal Processing, vol. 69. Institute of Electrical
and Electronics Engineers, pp. 4039–4054, 2021.
ista: Gabrielaitis M. 2021. Fast and accurate amplitude demodulation of wideband
signals. IEEE Transactions on Signal Processing. 69, 4039–4054.
mla: Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband
Signals.” IEEE Transactions on Signal Processing, vol. 69, Institute of
Electrical and Electronics Engineers, 2021, pp. 4039–54, doi:10.1109/TSP.2021.3087899.
short: M. Gabrielaitis, IEEE Transactions on Signal Processing 69 (2021) 4039–4054.
date_created: 2021-08-08T22:01:31Z
date_published: 2021-06-09T00:00:00Z
date_updated: 2023-08-10T14:19:33Z
day: '09'
department:
- _id: GaTk
doi: 10.1109/TSP.2021.3087899
external_id:
arxiv:
- '2102.04832'
isi:
- '000682123900002'
intvolume: ' 69'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.04832
month: '06'
oa: 1
oa_version: Preprint
page: 4039 - 4054
publication: IEEE Transactions on Signal Processing
publication_identifier:
eissn:
- 1941-0476
issn:
- 1053-587X
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Fast and accurate amplitude demodulation of wideband signals
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 69
year: '2021'
...
---
_id: '9362'
abstract:
- lang: eng
text: A central goal in systems neuroscience is to understand the functions performed
by neural circuits. Previous top-down models addressed this question by comparing
the behaviour of an ideal model circuit, optimised to perform a given function,
with neural recordings. However, this requires guessing in advance what function
is being performed, which may not be possible for many neural systems. To address
this, we propose an inverse reinforcement learning (RL) framework for inferring
the function performed by a neural network from data. We assume that the responses
of each neuron in a network are optimised so as to drive the network towards ‘rewarded’
states, that are desirable for performing a given function. We then show how one
can use inverse RL to infer the reward function optimised by the network from
observing its responses. This inferred reward function can be used to predict
how the neural network should adapt its dynamics to perform the same function
when the external environment or network structure changes. This could lead to
theoretical predictions about how neural network dynamics adapt to deal with cell
death and/or varying sensory stimulus statistics.
acknowledgement: The authors would like to thank Ulisse Ferrari for useful discussions
and feedback.
article_number: e0248940
article_processing_charge: No
article_type: original
author:
- first_name: Matthew J
full_name: Chalk, Matthew J
id: 2BAAC544-F248-11E8-B48F-1D18A9856A87
last_name: Chalk
orcid: 0000-0001-7782-4436
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
citation:
ama: Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent
neural network. PLoS ONE. 2021;16(4). doi:10.1371/journal.pone.0248940
apa: Chalk, M. J., Tkačik, G., & Marre, O. (2021). Inferring the function performed
by a recurrent neural network. PLoS ONE. Public Library of Science. https://doi.org/10.1371/journal.pone.0248940
chicago: Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function
Performed by a Recurrent Neural Network.” PLoS ONE. Public Library of Science,
2021. https://doi.org/10.1371/journal.pone.0248940.
ieee: M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by
a recurrent neural network,” PLoS ONE, vol. 16, no. 4. Public Library of
Science, 2021.
ista: Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent
neural network. PLoS ONE. 16(4), e0248940.
mla: Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent
Neural Network.” PLoS ONE, vol. 16, no. 4, e0248940, Public Library of
Science, 2021, doi:10.1371/journal.pone.0248940.
short: M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).
date_created: 2021-05-02T22:01:28Z
date_published: 2021-04-15T00:00:00Z
date_updated: 2023-10-18T08:17:42Z
day: '15'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0248940
external_id:
isi:
- '000641474900072'
pmid:
- '33857170'
file:
- access_level: open_access
checksum: c52da133850307d2031f552d998f00e8
content_type: application/pdf
creator: kschuh
date_created: 2021-05-04T13:22:19Z
date_updated: 2021-05-04T13:22:19Z
file_id: '9371'
file_name: 2021_pone_Chalk.pdf
file_size: 2768282
relation: main_file
success: 1
file_date_updated: 2021-05-04T13:22:19Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
eissn:
- '19326203'
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inferring the function performed by a recurrent neural 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2021'
...
---
_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: 2024-03-27T23:30:16Z
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).
date_created: 2020-07-12T16:20:33Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2021-01-12T08:16:55Z
day: '11'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1051/epjconf/202023000005
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-07-22T06:17:11Z
date_updated: 2020-07-22T06:17:11Z
file_id: '8144'
file_name: 2020_EPJWebConf_Lombardi.pdf
file_size: 2197543
relation: main_file
success: 1
file_date_updated: 2020-07-22T06:17:11Z
has_accepted_license: '1'
intvolume: ' 230'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: EPJ Web of Conferences
publication_identifier:
issn:
- 2100-014X
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
status: public
title: Power-law correlations and coupling of active and quiet states underlie a class
of complex systems with self-organization at criticality
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: 230
year: '2020'
...
---
_id: '7490'
abstract:
- lang: eng
text: In plants, clathrin mediated endocytosis (CME) represents the major route
for cargo internalisation from the cell surface. It has been assumed to operate
in an evolutionary conserved manner as in yeast and animals. Here we report characterisation
of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement
in electron microscopy and quantitative live imaging techniques. Arabidopsis CME
appears to follow the constant curvature model and the bona fide CME population
generates vesicles of a predominantly hexagonal-basket type; larger and with faster
kinetics than in other models. Contrary to the existing paradigm, actin is dispensable
for CME events at the plasma membrane but plays a unique role in collecting endocytic
vesicles, sorting of internalised cargos and directional endosome movement that
itself actively promote CME events. Internalized vesicles display a strongly delayed
and sequential uncoating. These unique features highlight the independent evolution
of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes.
acknowledged_ssus:
- _id: LifeSc
- _id: Bio
- _id: EM-Fac
article_number: e52067
article_processing_charge: No
article_type: original
author:
- first_name: Madhumitha
full_name: Narasimhan, Madhumitha
id: 44BF24D0-F248-11E8-B48F-1D18A9856A87
last_name: Narasimhan
orcid: 0000-0002-8600-0671
- first_name: Alexander J
full_name: Johnson, Alexander J
id: 46A62C3A-F248-11E8-B48F-1D18A9856A87
last_name: Johnson
orcid: 0000-0002-2739-8843
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Walter
full_name: Kaufmann, Walter
id: 3F99E422-F248-11E8-B48F-1D18A9856A87
last_name: Kaufmann
orcid: 0000-0001-9735-5315
- first_name: Shutang
full_name: Tan, Shutang
id: 2DE75584-F248-11E8-B48F-1D18A9856A87
last_name: Tan
orcid: 0000-0002-0471-8285
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- first_name: Jiří
full_name: Friml, Jiří
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
citation:
ama: Narasimhan M, Johnson AJ, Prizak R, et al. Evolutionarily unique mechanistic
framework of clathrin-mediated endocytosis in plants. eLife. 2020;9. doi:10.7554/eLife.52067
apa: Narasimhan, M., Johnson, A. J., Prizak, R., Kaufmann, W., Tan, S., Casillas
Perez, B. E., & Friml, J. (2020). Evolutionarily unique mechanistic framework
of clathrin-mediated endocytosis in plants. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.52067
chicago: Narasimhan, Madhumitha, Alexander J Johnson, Roshan Prizak, Walter Kaufmann,
Shutang Tan, Barbara E Casillas Perez, and Jiří Friml. “Evolutionarily Unique
Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” ELife.
eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.52067.
ieee: M. Narasimhan et al., “Evolutionarily unique mechanistic framework
of clathrin-mediated endocytosis in plants,” eLife, vol. 9. eLife Sciences
Publications, 2020.
ista: Narasimhan M, Johnson AJ, Prizak R, Kaufmann W, Tan S, Casillas Perez BE,
Friml J. 2020. Evolutionarily unique mechanistic framework of clathrin-mediated
endocytosis in plants. eLife. 9, e52067.
mla: Narasimhan, Madhumitha, et al. “Evolutionarily Unique Mechanistic Framework
of Clathrin-Mediated Endocytosis in Plants.” ELife, vol. 9, e52067, eLife
Sciences Publications, 2020, doi:10.7554/eLife.52067.
short: M. Narasimhan, A.J. Johnson, R. Prizak, W. Kaufmann, S. Tan, B.E. Casillas
Perez, J. Friml, ELife 9 (2020).
date_created: 2020-02-16T23:00:50Z
date_published: 2020-01-23T00:00:00Z
date_updated: 2023-08-18T06:33:07Z
day: '23'
ddc:
- '570'
- '580'
department:
- _id: JiFr
- _id: GaTk
- _id: EM-Fac
- _id: SyCr
doi: 10.7554/eLife.52067
ec_funded: 1
external_id:
isi:
- '000514104100001'
pmid:
- '31971511'
file:
- access_level: open_access
checksum: 2052daa4be5019534f3a42f200a09f32
content_type: application/pdf
creator: dernst
date_created: 2020-02-18T07:21:16Z
date_updated: 2020-07-14T12:47:59Z
file_id: '7494'
file_name: 2020_eLife_Narasimhan.pdf
file_size: 7247468
relation: main_file
file_date_updated: 2020-07-14T12:47:59Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 261099A6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742985'
name: Tracing Evolution of Auxin Transport and Polarity in Plants
- _id: 26538374-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03630
name: Molecular mechanisms of endocytic cargo recognition in plants
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis
in plants
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: 9
year: '2020'
...
---
_id: '9779'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:10.1371/journal.pcbi.1007642.s003
apa: Grah, R., & Friedlander, T. (2020). Distribution of crosstalk values. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s003
chicago: Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public
Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s003.
ieee: R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library
of Science, 2020.
ista: Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library
of Science, 10.1371/journal.pcbi.1007642.s003.
mla: Grah, Rok, and Tamar Friedlander. Distribution of Crosstalk Values.
Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s003.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:24:37Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s003
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: research_data
status: public
status: public
title: Distribution of crosstalk values
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9776'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Supporting information. 2020. doi:10.1371/journal.pcbi.1007642.s001
apa: Grah, R., & Friedlander, T. (2020). Supporting information. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s001
chicago: Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s001.
ieee: R. Grah and T. Friedlander, “Supporting information.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Supporting information, Public Library of Science,
10.1371/journal.pcbi.1007642.s001.
mla: Grah, Rok, and Tamar Friedlander. Supporting Information. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s001.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:15:04Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s001
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: used_in_publication
status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '7656'
abstract:
- lang: eng
text: 'We propose that correlations among neurons are generically strong enough
to organize neural activity patterns into a discrete set of clusters, which can
each be viewed as a population codeword. Our reasoning starts with the analysis
of retinal ganglion cell data using maximum entropy models, showing that the population
is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads
to an argument that neural populations in many other brain areas might share this
structure. Next, we use latent variable models to show that this glassy state
possesses well-defined clusters of neural activity. Clusters have three appealing
properties: (i) clusters exhibit error correction, i.e., they are reproducibly
elicited by the same stimulus despite variability at the level of constituent
neurons; (ii) clusters encode qualitatively different visual features than their
constituent neurons; and (iii) clusters can be learned by downstream neural circuits
in an unsupervised fashion. We hypothesize that these properties give rise to
a “learnable” neural code which the cortical hierarchy uses to extract increasingly
complex features without supervision or reinforcement.'
article_number: '20'
article_processing_charge: No
article_type: original
author:
- first_name: Michael J.
full_name: Berry, Michael J.
last_name: Berry
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: 'Berry MJ, Tkačik G. Clustering of neural activity: A design principle for
population codes. Frontiers in Computational Neuroscience. 2020;14. doi:10.3389/fncom.2020.00020'
apa: 'Berry, M. J., & Tkačik, G. (2020). Clustering of neural activity: A design
principle for population codes. Frontiers in Computational Neuroscience.
Frontiers. https://doi.org/10.3389/fncom.2020.00020'
chicago: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity:
A Design Principle for Population Codes.” Frontiers in Computational Neuroscience.
Frontiers, 2020. https://doi.org/10.3389/fncom.2020.00020.'
ieee: 'M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle
for population codes,” Frontiers in Computational Neuroscience, vol. 14.
Frontiers, 2020.'
ista: 'Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle
for population codes. Frontiers in Computational Neuroscience. 14, 20.'
mla: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design
Principle for Population Codes.” Frontiers in Computational Neuroscience,
vol. 14, 20, Frontiers, 2020, doi:10.3389/fncom.2020.00020.'
short: M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).
date_created: 2020-04-12T22:00:40Z
date_published: 2020-03-13T00:00:00Z
date_updated: 2023-08-18T10:30:11Z
day: '13'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.3389/fncom.2020.00020
external_id:
isi:
- '000525543200001'
pmid:
- '32231528'
file:
- access_level: open_access
checksum: 2b1da23823eae9cedbb42d701945b61e
content_type: application/pdf
creator: dernst
date_created: 2020-04-14T12:20:39Z
date_updated: 2020-07-14T12:48:01Z
file_id: '7659'
file_name: 2020_Frontiers_Berry.pdf
file_size: 4082937
relation: main_file
file_date_updated: 2020-07-14T12:48:01Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Frontiers in Computational Neuroscience
publication_identifier:
eissn:
- '16625188'
publication_status: published
publisher: Frontiers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Clustering of neural activity: A design principle for population codes'
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: 14
year: '2020'
...
---
_id: '8698'
abstract:
- lang: eng
text: The brain represents and reasons probabilistically about complex stimuli and
motor actions using a noisy, spike-based neural code. A key building block for
such neural computations, as well as the basis for supervised and unsupervised
learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional
neural activity patterns. Despite progress in statistical modeling of neural responses
and deep learning, current approaches either do not scale to large neural populations
or cannot be implemented using biologically realistic mechanisms. Inspired by
the sparse and random connectivity of real neuronal circuits, we present a model
for neural codes that accurately estimates the likelihood of individual spiking
patterns and has a straightforward, scalable, efficient, learnable, and realistic
neural implementation. This model’s performance on simultaneously recorded spiking
activity of >100 neurons in the monkey visual and prefrontal cortices is comparable
with or better than that of state-of-the-art models. Importantly, the model can
be learned using a small number of samples and using a local learning rule that
utilizes noise intrinsic to neural circuits. Slower, structural changes in random
connectivity, consistent with rewiring and pruning processes, further improve
the efficiency and sparseness of the resulting neural representations. Our results
merge insights from neuroanatomy, machine learning, and theoretical neuroscience
to suggest random sparse connectivity as a key design principle for neuronal computation.
acknowledgement: We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir
Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn
for invaluable discussions of the learning rule. This work was supported by European
Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12
(to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs.
Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to
R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration
on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative
Research in Computational Neuroscience) grant (to R.K. and E.S.).
article_processing_charge: No
article_type: original
author:
- first_name: Ori
full_name: Maoz, Ori
last_name: Maoz
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Mohamad Saleh
full_name: Esteki, Mohamad Saleh
last_name: Esteki
- first_name: Roozbeh
full_name: Kiani, Roozbeh
last_name: Kiani
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
citation:
ama: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic
neural representations with randomly connected circuits. Proceedings of the
National Academy of Sciences of the United States of America. 2020;117(40):25066-25073.
doi:10.1073/pnas.1912804117
apa: Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020).
Learning probabilistic neural representations with randomly connected circuits.
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences. https://doi.org/10.1073/pnas.1912804117
chicago: Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad
Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected
Circuits.” Proceedings of the National Academy of Sciences of the United States
of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.1912804117.
ieee: O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic
neural representations with randomly connected circuits,” Proceedings of the
National Academy of Sciences of the United States of America, vol. 117, no.
40. National Academy of Sciences, pp. 25066–25073, 2020.
ista: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic
neural representations with randomly connected circuits. Proceedings of the National
Academy of Sciences of the United States of America. 117(40), 25066–25073.
mla: Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly
Connected Circuits.” Proceedings of the National Academy of Sciences of the
United States of America, vol. 117, no. 40, National Academy of Sciences,
2020, pp. 25066–73, doi:10.1073/pnas.1912804117.
short: O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of
the National Academy of Sciences of the United States of America 117 (2020) 25066–25073.
date_created: 2020-10-25T23:01:16Z
date_published: 2020-10-06T00:00:00Z
date_updated: 2023-08-22T12:11:23Z
day: '06'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1073/pnas.1912804117
external_id:
isi:
- '000579045200012'
pmid:
- '32948691'
file:
- access_level: open_access
checksum: c6a24fdecf3f28faf447078e7a274a88
content_type: application/pdf
creator: cziletti
date_created: 2020-10-27T14:57:50Z
date_updated: 2020-10-27T14:57:50Z
file_id: '8713'
file_name: 2020_PNAS_Maoz.pdf
file_size: 1755359
relation: main_file
success: 1
file_date_updated: 2020-10-27T14:57:50Z
has_accepted_license: '1'
intvolume: ' 117'
isi: 1
issue: '40'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 25066-25073
pmid: 1
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- '10916490'
issn:
- '00278424'
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning probabilistic neural representations with randomly connected circuits
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: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 117
year: '2020'
...