---
_id: '13127'
abstract:
- lang: eng
text: Cooperative disease defense emerges as group-level collective behavior, yet
how group members make the underlying individual decisions is poorly understood.
Using garden ants and fungal pathogens as an experimental model, we derive the
rules governing individual ant grooming choices and show how they produce colony-level
hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic
modeling reveal that ants increase grooming and preferentially target highly-infectious
individuals when perceiving high pathogen load, but transiently suppress grooming
after having been groomed by nestmates. Ants thus react to both, the infectivity
of others and the social feedback they receive on their own contagiousness. While
inferred solely from momentary ant decisions, these behavioral rules quantitatively
predict hour-long experimental dynamics, and synergistically combine into efficient
colony-wide pathogen removal. Our analyses show that noisy individual decisions
based on only local, incomplete, yet dynamically-updated information on pathogen
threat and social feedback can lead to potent collective disease defense.
acknowledged_ssus:
- _id: LifeSc
acknowledgement: We thank Mike Bidochka for the fungal strains, the ISTA Social Immunity
Team for ant collection, Hanna Leitner for experimental and molecular support, Jennifer
Robb and Lukas Lindorfer for microscopy, and the LabSupport Facility at ISTA for
general laboratory support. We further thank Victor Mireles, Iain Couzin, Fabian
Theis and the Social Immunity Team for continued feedback throughout, and Michael
Sixt, Yuko Ulrich, Koos Boomsma, Erika Dawson, Megan Kutzer and Hinrich Schulenburg
for comments on the manuscript. This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
program (Grant No. 771402; EPIDEMICSonCHIP) to SC, from the Scientific Grant Agency
of the Slovak Republic (Grant No. 1/0521/20) to KB, and the Human Frontier Science
Program (Grant No. RGP0065/2012) to GT.
article_number: '3232'
article_processing_charge: Yes
article_type: original
author:
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- 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: Anna V
full_name: Grasse, Anna V
id: 406F989C-F248-11E8-B48F-1D18A9856A87
last_name: Grasse
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
citation:
ama: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. Dynamic pathogen
detection and social feedback shape collective hygiene in ants. Nature Communications.
2023;14. doi:10.1038/s41467-023-38947-y
apa: Casillas Perez, B. E., Bodova, K., Grasse, A. V., Tkačik, G., & Cremer,
S. (2023). Dynamic pathogen detection and social feedback shape collective hygiene
in ants. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-38947-y
chicago: Casillas Perez, Barbara E, Katarina Bodova, Anna V Grasse, Gašper Tkačik,
and Sylvia Cremer. “Dynamic Pathogen Detection and Social Feedback Shape Collective
Hygiene in Ants.” Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-38947-y.
ieee: B. E. Casillas Perez, K. Bodova, A. V. Grasse, G. Tkačik, and S. Cremer, “Dynamic
pathogen detection and social feedback shape collective hygiene in ants,” Nature
Communications, vol. 14. Springer Nature, 2023.
ista: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. 2023. Dynamic
pathogen detection and social feedback shape collective hygiene in ants. Nature
Communications. 14, 3232.
mla: Casillas Perez, Barbara E., et al. “Dynamic Pathogen Detection and Social Feedback
Shape Collective Hygiene in Ants.” Nature Communications, vol. 14, 3232,
Springer Nature, 2023, doi:10.1038/s41467-023-38947-y.
short: B.E. Casillas Perez, K. Bodova, A.V. Grasse, G. Tkačik, S. Cremer, Nature
Communications 14 (2023).
date_created: 2023-06-11T22:00:40Z
date_published: 2023-06-03T00:00:00Z
date_updated: 2023-08-07T13:09:09Z
day: '03'
ddc:
- '570'
department:
- _id: SyCr
- _id: GaTk
doi: 10.1038/s41467-023-38947-y
ec_funded: 1
external_id:
isi:
- '001002562700005'
pmid:
- '37270641'
file:
- access_level: open_access
checksum: 4af0393e3ed47b3fc46e68b81c3c1007
content_type: application/pdf
creator: dernst
date_created: 2023-06-13T08:05:46Z
date_updated: 2023-06-13T08:05:46Z
file_id: '13132'
file_name: 2023_NatureComm_CasillasPerez.pdf
file_size: 2358167
relation: main_file
success: 1
file_date_updated: 2023-06-13T08:05:46Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2649B4DE-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771402'
name: Epidemics in ant societies on a chip
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12945'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Dynamic pathogen detection and social feedback shape collective hygiene in
ants
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2023'
...
---
_id: '406'
abstract:
- lang: eng
text: 'Recent developments in automated tracking allow uninterrupted, high-resolution
recording of animal trajectories, sometimes coupled with the identification of
stereotyped changes of body pose or other behaviors of interest. Analysis and
interpretation of such data represents a challenge: the timing of animal behaviors
may be stochastic and modulated by kinematic variables, by the interaction with
the environment or with the conspecifics within the animal group, and dependent
on internal cognitive or behavioral state of the individual. Existing models for
collective motion typically fail to incorporate the discrete, stochastic, and
internal-state-dependent aspects of behavior, while models focusing on individual
animal behavior typically ignore the spatial aspects of the problem. Here we propose
a probabilistic modeling framework to address this gap. Each animal can switch
stochastically between different behavioral states, with each state resulting
in a possibly different law of motion through space. Switching rates for behavioral
transitions can depend in a very general way, which we seek to identify from data,
on the effects of the environment as well as the interaction between the animals.
We represent the switching dynamics as a Generalized Linear Model and show that:
(i) forward simulation of multiple interacting animals is possible using a variant
of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly,
the maximum likelihood inference of switching rate functions is tractably solvable
by gradient descent; (iii) model selection can be used to identify factors that
modulate behavioral state switching and to appropriately adjust model complexity
to data. To illustrate our framework, we apply it to two synthetic models of animal
motion and to real zebrafish tracking data. '
acknowledgement: This work was supported by the Human Frontier Science Program RGP0065/2012
(GT, ES).
article_processing_charge: Yes
author:
- first_name: Katarína
full_name: Bod’Ová, Katarína
last_name: Bod’Ová
- first_name: Gabriel
full_name: Mitchell, Gabriel
id: 315BCD80-F248-11E8-B48F-1D18A9856A87
last_name: Mitchell
- first_name: Roy
full_name: Harpaz, Roy
last_name: Harpaz
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Probabilistic models
of individual and collective animal behavior. PLoS One. 2018;13(3). doi:10.1371/journal.pone.0193049
apa: Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., & Tkačik, G. (2018).
Probabilistic models of individual and collective animal behavior. PLoS One.
Public Library of Science. https://doi.org/10.1371/journal.pone.0193049
chicago: Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper
Tkačik. “Probabilistic Models of Individual and Collective Animal Behavior.” PLoS
One. Public Library of Science, 2018. https://doi.org/10.1371/journal.pone.0193049.
ieee: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Probabilistic
models of individual and collective animal behavior,” PLoS One, vol. 13,
no. 3. Public Library of Science, 2018.
ista: Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Probabilistic
models of individual and collective animal behavior. PLoS One. 13(3).
mla: Bod’Ová, Katarína, et al. “Probabilistic Models of Individual and Collective
Animal Behavior.” PLoS One, vol. 13, no. 3, Public Library of Science,
2018, doi:10.1371/journal.pone.0193049.
short: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, PLoS One 13
(2018).
date_created: 2018-12-11T11:46:18Z
date_published: 2018-03-07T00:00:00Z
date_updated: 2023-09-15T12:06:19Z
day: '07'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0193049
external_id:
isi:
- '000426896800032'
file:
- access_level: open_access
checksum: 684229493db75b43e98a46cd922da497
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:43Z
date_updated: 2020-07-14T12:46:22Z
file_id: '5165'
file_name: IST-2018-995-v1+1_2018_Bodova_Probabilistic.pdf
file_size: 6887358
relation: main_file
file_date_updated: 2020-07-14T12:46:22Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Submitted Version
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '7423'
pubrep_id: '995'
quality_controlled: '1'
related_material:
record:
- id: '9831'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Probabilistic models of individual and collective animal behavior
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 13
year: '2018'
...
---
_id: '720'
abstract:
- lang: eng
text: 'Advances in multi-unit recordings pave the way for statistical modeling of
activity patterns in large neural populations. Recent studies have shown that
the summed activity of all neurons strongly shapes the population response. A
separate recent finding has been that neural populations also exhibit criticality,
an anomalously large dynamic range for the probabilities of different population
activity patterns. Motivated by these two observations, we introduce a class of
probabilistic models which takes into account the prior knowledge that the neural
population could be globally coupled and close to critical. These models consist
of an energy function which parametrizes interactions between small groups of
neurons, and an arbitrary positive, strictly increasing, and twice differentiable
function which maps the energy of a population pattern to its probability. We
show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an
accurate description of the activity of retinal ganglion cells which outperforms
previous models based on the summed activity of neurons; 2) prior knowledge that
the population is critical translates to prior expectations about the shape of
the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous
latent variable globally coupling the system whose distribution we can infer from
data. Our method is independent of the underlying system’s state space; hence,
it can be applied to other systems such as natural scenes or amino acid sequences
of proteins which are also known to exhibit criticality.'
article_number: e1005763
article_processing_charge: Yes
author:
- first_name: Jan
full_name: Humplik, Jan
id: 2E9627A8-F248-11E8-B48F-1D18A9856A87
last_name: Humplik
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally
capture global coupling and criticality. PLoS Computational Biology. 2017;13(9).
doi:10.1371/journal.pcbi.1005763
apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations
that naturally capture global coupling and criticality. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763
chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763.
ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that
naturally capture global coupling and criticality,” PLoS Computational Biology,
vol. 13, no. 9. Public Library of Science, 2017.
ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that
naturally capture global coupling and criticality. PLoS Computational Biology.
13(9), e1005763.
mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763.
short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).
date_created: 2018-12-11T11:48:08Z
date_published: 2017-09-19T00:00:00Z
date_updated: 2021-01-12T08:12:21Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005763
file:
- access_level: open_access
checksum: 81107096c19771c36ddbe6f0282a3acb
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:30Z
date_updated: 2020-07-14T12:47:53Z
file_id: '5352'
file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf
file_size: 14167050
relation: main_file
file_date_updated: 2020-07-14T12:47:53Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '6960'
pubrep_id: '884'
quality_controlled: '1'
scopus_import: 1
status: public
title: Probabilistic models for neural populations that naturally capture global coupling
and 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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2017'
...
---
_id: '1021'
abstract:
- lang: eng
text: Most flows in nature and engineering are turbulent because of their large
velocities and spatial scales. Laboratory experiments on rotating quasi-Keplerian
flows, for which the angular velocity decreases radially but the angular momentum
increases, are however laminar at Reynolds numbers exceeding one million. This
is in apparent contradiction to direct numerical simulations showing that in these
experiments turbulence transition is triggered by the axial boundaries. We here
show numerically that as the Reynolds number increases, turbulence becomes progressively
confined to the boundary layers and the flow in the bulk fully relaminarizes.
Our findings support that turbulence is unlikely to occur in isothermal constant-density
quasi-Keplerian flows.
article_processing_charge: No
author:
- first_name: Jose M
full_name: Lopez Alonso, Jose M
id: 40770848-F248-11E8-B48F-1D18A9856A87
last_name: Lopez Alonso
orcid: 0000-0002-0384-2022
- first_name: Marc
full_name: Avila, Marc
last_name: Avila
citation:
ama: Lopez Alonso JM, Avila M. Boundary layer turbulence in experiments on quasi
Keplerian flows. Journal of Fluid Mechanics. 2017;817:21-34. doi:10.1017/jfm.2017.109
apa: Lopez Alonso, J. M., & Avila, M. (2017). Boundary layer turbulence in experiments
on quasi Keplerian flows. Journal of Fluid Mechanics. Cambridge University
Press. https://doi.org/10.1017/jfm.2017.109
chicago: Lopez Alonso, Jose M, and Marc Avila. “Boundary Layer Turbulence in Experiments
on Quasi Keplerian Flows.” Journal of Fluid Mechanics. Cambridge University
Press, 2017. https://doi.org/10.1017/jfm.2017.109.
ieee: J. M. Lopez Alonso and M. Avila, “Boundary layer turbulence in experiments
on quasi Keplerian flows,” Journal of Fluid Mechanics, vol. 817. Cambridge
University Press, pp. 21–34, 2017.
ista: Lopez Alonso JM, Avila M. 2017. Boundary layer turbulence in experiments on
quasi Keplerian flows. Journal of Fluid Mechanics. 817, 21–34.
mla: Lopez Alonso, Jose M., and Marc Avila. “Boundary Layer Turbulence in Experiments
on Quasi Keplerian Flows.” Journal of Fluid Mechanics, vol. 817, Cambridge
University Press, 2017, pp. 21–34, doi:10.1017/jfm.2017.109.
short: J.M. Lopez Alonso, M. Avila, Journal of Fluid Mechanics 817 (2017) 21–34.
date_created: 2018-12-11T11:49:44Z
date_published: 2017-04-25T00:00:00Z
date_updated: 2023-09-22T09:39:46Z
day: '25'
department:
- _id: BjHo
doi: 10.1017/jfm.2017.109
external_id:
isi:
- '000398179100006'
intvolume: ' 817'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1608.05527
month: '04'
oa: 1
oa_version: Submitted Version
page: 21 - 34
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Journal of Fluid Mechanics
publication_identifier:
issn:
- '00221120'
publication_status: published
publisher: Cambridge University Press
publist_id: '6371'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Boundary layer turbulence in experiments on quasi Keplerian flows
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 817
year: '2017'
...
---
_id: '1420'
abstract:
- lang: eng
text: 'Selection, mutation, and random drift affect the dynamics of allele frequencies
and consequently of quantitative traits. While the macroscopic dynamics of quantitative
traits can be measured, the underlying allele frequencies are typically unobserved.
Can we understand how the macroscopic observables evolve without following these
microscopic processes? This problem has been studied previously by analogy with
statistical mechanics: the allele frequency distribution at each time point is
approximated by the stationary form, which maximizes entropy. We explore the limitations
of this method when mutation is small (4Nμ < 1) so that populations are typically
close to fixation, and we extend the theory in this regime to account for changes
in mutation strength. We consider a single diallelic locus either under directional
selection or with overdominance and then generalize to multiple unlinked biallelic
loci with unequal effects. We find that the maximum-entropy approximation is remarkably
accurate, even when mutation and selection change rapidly. '
article_processing_charge: No
arxiv: 1
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: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- 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, Tkačik G, Barton NH. A general approximation for the dynamics of
quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127
apa: Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation
for the dynamics of quantitative traits. Genetics. Genetics Society of
America. https://doi.org/10.1534/genetics.115.184127
chicago: Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation
for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of
America, 2016. https://doi.org/10.1534/genetics.115.184127.
ieee: K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics
of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of
America, pp. 1523–1548, 2016.
ista: Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics
of quantitative traits. Genetics. 202(4), 1523–1548.
mla: Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative
Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016,
pp. 1523–48, doi:10.1534/genetics.115.184127.
short: K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.
date_created: 2018-12-11T11:51:55Z
date_published: 2016-04-06T00:00:00Z
date_updated: 2025-05-28T11:42:47Z
day: '06'
department:
- _id: GaTk
- _id: NiBa
doi: 10.1534/genetics.115.184127
ec_funded: 1
external_id:
arxiv:
- '1510.08344'
intvolume: ' 202'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1510.08344
month: '04'
oa: 1
oa_version: Preprint
page: 1523 - 1548
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '5787'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A general approximation for the dynamics of quantitative traits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2016'
...