---
_id: '13230'
abstract:
- lang: eng
text: 'To interpret the sensory environment, the brain combines ambiguous sensory
measurements with knowledge that reflects context-specific prior experience. But
environmental contexts can change abruptly and unpredictably, resulting in uncertainty
about the current context. Here we address two questions: how should context-specific
prior knowledge optimally guide the interpretation of sensory stimuli in changing
environments, and do human decision-making strategies resemble this optimum? We
probe these questions with a task in which subjects report the orientation of
ambiguous visual stimuli that were drawn from three dynamically switching distributions,
representing different environmental contexts. We derive predictions for an ideal
Bayesian observer that leverages knowledge about the statistical structure of
the task to maximize decision accuracy, including knowledge about the dynamics
of the environment. We show that its decisions are biased by the dynamically changing
task context. The magnitude of this decision bias depends on the observer’s continually
evolving belief about the current context. The model therefore not only predicts
that decision bias will grow as the context is indicated more reliably, but also
as the stability of the environment increases, and as the number of trials since
the last context switch grows. Analysis of human choice data validates all three
predictions, suggesting that the brain leverages knowledge of the statistical
structure of environmental change when interpreting ambiguous sensory signals.'
acknowledgement: The authors thank Corey Ziemba and Zoe Boundy-Singer for valuable
discussion and feedback.
article_number: e1011104
article_processing_charge: No
article_type: original
author:
- first_name: Julie A.
full_name: Charlton, Julie A.
last_name: Charlton
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Yoon H.
full_name: Bai, Yoon H.
last_name: Bai
- first_name: Ann M.
full_name: Hermundstad, Ann M.
last_name: Hermundstad
- first_name: Robbe L.T.
full_name: Goris, Robbe L.T.
last_name: Goris
citation:
ama: Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. Environmental
dynamics shape perceptual decision bias. PLoS Computational Biology. 2023;19(6).
doi:10.1371/journal.pcbi.1011104
apa: Charlton, J. A., Mlynarski, W. F., Bai, Y. H., Hermundstad, A. M., & Goris,
R. L. T. (2023). Environmental dynamics shape perceptual decision bias. PLoS
Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1011104
chicago: Charlton, Julie A., Wiktor F Mlynarski, Yoon H. Bai, Ann M. Hermundstad,
and Robbe L.T. Goris. “Environmental Dynamics Shape Perceptual Decision Bias.”
PLoS Computational Biology. Public Library of Science, 2023. https://doi.org/10.1371/journal.pcbi.1011104.
ieee: J. A. Charlton, W. F. Mlynarski, Y. H. Bai, A. M. Hermundstad, and R. L. T.
Goris, “Environmental dynamics shape perceptual decision bias,” PLoS Computational
Biology, vol. 19, no. 6. Public Library of Science, 2023.
ista: Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. 2023. Environmental
dynamics shape perceptual decision bias. PLoS Computational Biology. 19(6), e1011104.
mla: Charlton, Julie A., et al. “Environmental Dynamics Shape Perceptual Decision
Bias.” PLoS Computational Biology, vol. 19, no. 6, e1011104, Public Library
of Science, 2023, doi:10.1371/journal.pcbi.1011104.
short: J.A. Charlton, W.F. Mlynarski, Y.H. Bai, A.M. Hermundstad, R.L.T. Goris,
PLoS Computational Biology 19 (2023).
date_created: 2023-07-16T22:01:09Z
date_published: 2023-06-08T00:00:00Z
date_updated: 2023-08-02T06:33:50Z
day: '08'
ddc:
- '570'
department:
- _id: MaJö
doi: 10.1371/journal.pcbi.1011104
external_id:
isi:
- '001003410200003'
pmid:
- '37289753'
file:
- access_level: open_access
checksum: 800761fa2c647fabd6ad034589bc526e
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T08:07:59Z
date_updated: 2023-07-18T08:07:59Z
file_id: '13247'
file_name: 2023_PloSCompBio_Charlton.pdf
file_size: 2281868
relation: main_file
success: 1
file_date_updated: 2023-07-18T08:07:59Z
has_accepted_license: '1'
intvolume: ' 19'
isi: 1
issue: '6'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Environmental dynamics shape perceptual decision bias
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: 19
year: '2023'
...
---
_id: '12862'
abstract:
- lang: eng
text: Despite the considerable progress of in vivo neural recording techniques,
inferring the biophysical mechanisms underlying large scale coordination of brain
activity from neural data remains challenging. One obstacle is the difficulty
to link high dimensional functional connectivity measures to mechanistic models
of network activity. We address this issue by investigating spike-field coupling
(SFC) measurements, which quantify the synchronization between, on the one hand,
the action potentials produced by neurons, and on the other hand mesoscopic “field”
signals, reflecting subthreshold activities at possibly multiple recording sites.
As the number of recording sites gets large, the amount of pairwise SFC measurements
becomes overwhelmingly challenging to interpret. We develop Generalized Phase
Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate
SFC. GPLA describes the dominant coupling between field activity and neural ensembles
across space and frequencies. We show that GPLA features are biophysically interpretable
when used in conjunction with appropriate network models, such that we can identify
the influence of underlying circuit properties on these features. We demonstrate
the statistical benefits and interpretability of this approach in various computational
models and Utah array recordings. The results suggest that GPLA, used jointly
with biophysical modeling, can help uncover the contribution of recurrent microcircuits
to the spatio-temporal dynamics observed in multi-channel experimental recordings.
acknowledgement: "We thank Britni Crocker for help with preprocessing of the data
and spike sorting; Joachim Werner and Michael Schnabel for their excellent IT support;
Andreas Tolias for help with the initial implantation’s of the Utah arrays.\r\nAll
authors were supported by the Max Planck Society. M.B. was supported by the German\r\nFederal
Ministry of Education and Research (BMBF) through the funding scheme received by\r\nthe
Tübingen AI Center, FKZ: 01IS18039B. N.K.L. and V.K. acknowledge the support from
the\r\nShanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX02).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript. "
article_number: e1010983
article_processing_charge: No
article_type: original
author:
- first_name: Shervin
full_name: Safavi, Shervin
last_name: Safavi
- first_name: Theofanis I.
full_name: Panagiotaropoulos, Theofanis I.
last_name: Panagiotaropoulos
- first_name: Vishal
full_name: Kapoor, Vishal
last_name: Kapoor
- first_name: Juan F
full_name: Ramirez Villegas, Juan F
id: 44B06F76-F248-11E8-B48F-1D18A9856A87
last_name: Ramirez Villegas
- first_name: Nikos K.
full_name: Logothetis, Nikos K.
last_name: Logothetis
- first_name: Michel
full_name: Besserve, Michel
last_name: Besserve
citation:
ama: Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK,
Besserve M. Uncovering the organization of neural circuits with Generalized Phase
Locking Analysis. PLoS Computational Biology. 2023;19(4). doi:10.1371/journal.pcbi.1010983
apa: Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez Villegas, J. F.,
Logothetis, N. K., & Besserve, M. (2023). Uncovering the organization of neural
circuits with Generalized Phase Locking Analysis. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010983
chicago: Safavi, Shervin, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F
Ramirez Villegas, Nikos K. Logothetis, and Michel Besserve. “Uncovering the Organization
of Neural Circuits with Generalized Phase Locking Analysis.” PLoS Computational
Biology. Public Library of Science, 2023. https://doi.org/10.1371/journal.pcbi.1010983.
ieee: S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez Villegas, N.
K. Logothetis, and M. Besserve, “Uncovering the organization of neural circuits
with Generalized Phase Locking Analysis,” PLoS Computational Biology, vol.
19, no. 4. Public Library of Science, 2023.
ista: Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis
NK, Besserve M. 2023. Uncovering the organization of neural circuits with Generalized
Phase Locking Analysis. PLoS Computational Biology. 19(4), e1010983.
mla: Safavi, Shervin, et al. “Uncovering the Organization of Neural Circuits with
Generalized Phase Locking Analysis.” PLoS Computational Biology, vol. 19,
no. 4, e1010983, Public Library of Science, 2023, doi:10.1371/journal.pcbi.1010983.
short: S. Safavi, T.I. Panagiotaropoulos, V. Kapoor, J.F. Ramirez Villegas, N.K.
Logothetis, M. Besserve, PLoS Computational Biology 19 (2023).
date_created: 2023-04-23T22:01:03Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-08-01T14:15:16Z
day: '01'
ddc:
- '570'
department:
- _id: JoCs
doi: 10.1371/journal.pcbi.1010983
external_id:
isi:
- '000962668700002'
file:
- access_level: open_access
checksum: edeb9d09f3e41ba7c0251308b9e372e7
content_type: application/pdf
creator: dernst
date_created: 2023-04-25T08:59:18Z
date_updated: 2023-04-25T08:59:18Z
file_id: '12867'
file_name: 2023_PLoSCompBio_Safavi.pdf
file_size: 4737671
relation: main_file
success: 1
file_date_updated: 2023-04-25T08:59:18Z
has_accepted_license: '1'
intvolume: ' 19'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/shervinsafavi/gpla.git
scopus_import: '1'
status: public
title: Uncovering the organization of neural circuits with Generalized Phase Locking
Analysis
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: 19
year: '2023'
...
---
_id: '10939'
abstract:
- lang: eng
text: Understanding and characterising biochemical processes inside single cells
requires experimental platforms that allow one to perturb and observe the dynamics
of such processes as well as computational methods to build and parameterise models
from the collected data. Recent progress with experimental platforms and optogenetics
has made it possible to expose each cell in an experiment to an individualised
input and automatically record cellular responses over days with fine time resolution.
However, methods to infer parameters of stochastic kinetic models from single-cell
longitudinal data have generally been developed under the assumption that experimental
data is sparse and that responses of cells to at most a few different input perturbations
can be observed. Here, we investigate and compare different approaches for calculating
parameter likelihoods of single-cell longitudinal data based on approximations
of the chemical master equation (CME) with a particular focus on coupling the
linear noise approximation (LNA) or moment closure methods to a Kalman filter.
We show that, as long as cells are measured sufficiently frequently, coupling
the LNA to a Kalman filter allows one to accurately approximate likelihoods and
to infer model parameters from data even in cases where the LNA provides poor
approximations of the CME. Furthermore, the computational cost of filtering-based
iterative likelihood evaluation scales advantageously in the number of measurement
times and different input perturbations and is thus ideally suited for data obtained
from modern experimental platforms. To demonstrate the practical usefulness of
these results, we perform an experiment in which single cells, equipped with an
optogenetic gene expression system, are exposed to various different light-input
sequences and measured at several hundred time points and use parameter inference
based on iterative likelihood evaluation to parameterise a stochastic model of
the system.
acknowledgement: We thank Virgile Andreani for useful discussions about the model
and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts
of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
article_number: e1009950
article_processing_charge: No
article_type: original
author:
- first_name: Anđela
full_name: Davidović, Anđela
last_name: Davidović
- first_name: Remy P
full_name: Chait, Remy P
id: 3464AE84-F248-11E8-B48F-1D18A9856A87
last_name: Chait
orcid: 0000-0003-0876-3187
- first_name: Gregory
full_name: Batt, Gregory
last_name: Batt
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 2022;18(3). doi:10.1371/journal.pcbi.1009950
apa: Davidović, A., Chait, R. P., Batt, G., & Ruess, J. (2022). Parameter inference
for stochastic biochemical models from perturbation experiments parallelised at
the single cell level. PLoS Computational Biology. Public Library of Science.
https://doi.org/10.1371/journal.pcbi.1009950
chicago: Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter
Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised
at the Single Cell Level.” PLoS Computational Biology. Public Library of
Science, 2022. https://doi.org/10.1371/journal.pcbi.1009950.
ieee: A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for
stochastic biochemical models from perturbation experiments parallelised at the
single cell level,” PLoS Computational Biology, vol. 18, no. 3. Public
Library of Science, 2022.
ista: Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 18(3), e1009950.
mla: Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models
from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS
Computational Biology, vol. 18, no. 3, e1009950, Public Library of Science,
2022, doi:10.1371/journal.pcbi.1009950.
short: A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18
(2022).
date_created: 2022-04-03T22:01:42Z
date_published: 2022-03-18T00:00:00Z
date_updated: 2022-04-04T10:21:53Z
day: '18'
ddc:
- '570'
- '000'
department:
- _id: CaGu
doi: 10.1371/journal.pcbi.1009950
file:
- access_level: open_access
checksum: 458ef542761fb714ced214f240daf6b2
content_type: application/pdf
creator: dernst
date_created: 2022-04-04T10:14:39Z
date_updated: 2022-04-04T10:14:39Z
file_id: '10947'
file_name: 2022_PLoSCompBio_Davidovic.pdf
file_size: 2958642
relation: main_file
success: 1
file_date_updated: 2022-04-04T10:14:39Z
has_accepted_license: '1'
intvolume: ' 18'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://gitlab.pasteur.fr/adavidov/inferencelnakf
scopus_import: '1'
status: public
title: Parameter inference for stochastic biochemical models from perturbation experiments
parallelised at the single cell level
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 18
year: '2022'
...
---
_id: '12084'
abstract:
- lang: eng
text: Neuronal networks encode information through patterns of activity that define
the networks’ function. The neurons’ activity relies on specific connectivity
structures, yet the link between structure and function is not fully understood.
Here, we tackle this structure-function problem with a new conceptual approach.
Instead of manipulating the connectivity directly, we focus on upper triangular
matrices, which represent the network dynamics in a given orthonormal basis obtained
by the Schur decomposition. This abstraction allows us to independently manipulate
the eigenspectrum and feedforward structures of a connectivity matrix. Using this
method, we describe a diverse repertoire of non-normal transient amplification,
and to complement the analysis of the dynamical regimes, we quantify the geometry
of output trajectories through the effective rank of both the eigenvector and
the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s
imaginary distribution leads to highly amplifying regimes in linear and long-lasting
dynamics in nonlinear networks. We also find a trade-off between amplification
and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space.
Networks that can amplify a large number of orthogonal initial conditions produce
neuronal trajectories that lie in the same subspace of the neuronal state-space.
Finally, we examine networks of excitatory and inhibitory neurons. We find that
the strength of global inhibition is directly linked with the amplitude of amplification,
such that weakening inhibitory weights also decreases amplification, and that
the eigenspectrum’s imaginary distribution grows with an increase in the ratio
between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths.
Consequently, the strength of global inhibition reveals itself as a strong signature
for amplification and a potential control mechanism to switch dynamical regimes.
Our results shed a light on how biological networks, i.e., networks constrained
by Dale’s law, may be optimised for specific dynamical regimes.
acknowledgement: 'We thank Friedemann Zenke for his comments, especially on the effect
of the self loops on the spectrum. We also thank Ken Miller and Bill Podlaski for
helpful comments. This research was funded by a Wellcome Trust and Royal Society
Henry Dale Research Fellowship (WT100000; TPV), a Wellcome Senior Research Fellowship
(214316/Z/18/Z; GC, EJA, and TPV), and a Research Project Grant by the Leverhulme
Trust (RPG-2016-446; EJA and TPV). '
article_number: e1010365
article_processing_charge: No
article_type: original
author:
- first_name: Georgia
full_name: Christodoulou, Georgia
last_name: Christodoulou
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
citation:
ama: Christodoulou G, Vogels TP, Agnes EJ. Regimes and mechanisms of transient amplification
in abstract and biological neural networks. PLoS Computational Biology.
2022;18(8). doi:10.1371/journal.pcbi.1010365
apa: Christodoulou, G., Vogels, T. P., & Agnes, E. J. (2022). Regimes and mechanisms
of transient amplification in abstract and biological neural networks. PLoS
Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010365
chicago: Christodoulou, Georgia, Tim P Vogels, and Everton J. Agnes. “Regimes and
Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.”
PLoS Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1010365.
ieee: G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of
transient amplification in abstract and biological neural networks,” PLoS Computational
Biology, vol. 18, no. 8. Public Library of Science, 2022.
ista: Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient
amplification in abstract and biological neural networks. PLoS Computational Biology.
18(8), e1010365.
mla: Christodoulou, Georgia, et al. “Regimes and Mechanisms of Transient Amplification
in Abstract and Biological Neural Networks.” PLoS Computational Biology,
vol. 18, no. 8, e1010365, Public Library of Science, 2022, doi:10.1371/journal.pcbi.1010365.
short: G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18
(2022).
date_created: 2022-09-11T22:01:56Z
date_published: 2022-08-15T00:00:00Z
date_updated: 2023-08-03T14:06:29Z
day: '15'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1010365
external_id:
isi:
- '000937227700001'
file:
- access_level: open_access
checksum: 8a81ab29f837991ee0ea770817c4a50e
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T07:47:55Z
date_updated: 2022-09-12T07:47:55Z
file_id: '12090'
file_name: 2022_PLoSCompBio_Christodoulou.pdf
file_size: 2867337
relation: main_file
success: 1
file_date_updated: 2022-09-12T07:47:55Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '8'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Regimes and mechanisms of transient amplification in abstract and biological
neural networks
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: 18
year: '2022'
...
---
_id: '12280'
abstract:
- lang: eng
text: 'In repeated interactions, players can use strategies that respond to the
outcome of previous rounds. Much of the existing literature on direct reciprocity
assumes that all competing individuals use the same strategy space. Here, we study
both learning and evolutionary dynamics of players that differ in the strategy
space they explore. We focus on the infinitely repeated donation game and compare
three natural strategy spaces: memory-1 strategies, which consider the last moves
of both players, reactive strategies, which respond to the last move of the co-player,
and unconditional strategies. These three strategy spaces differ in the memory
capacity that is needed. We compute the long term average payoff that is achieved
in a pairwise learning process. We find that smaller strategy spaces can dominate
larger ones. For weak selection, unconditional players dominate both reactive
and memory-1 players. For intermediate selection, reactive players dominate memory-1
players. Only for strong selection and low cost-to-benefit ratio, memory-1 players
dominate the others. We observe that the supergame between strategy spaces can
be a social dilemma: maximum payoff is achieved if both players explore a larger
strategy space, but smaller strategy spaces dominate.'
acknowledgement: "This work was supported by the European Research Council (https://erc.europa.eu/)\r\nCoG
863818 (ForM-SMArt) (to K.C.), and the European Research Council Starting Grant
850529: E-DIRECT (to C.H.). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript."
article_number: e1010149
article_processing_charge: No
article_type: original
author:
- first_name: Laura
full_name: Schmid, Laura
id: 38B437DE-F248-11E8-B48F-1D18A9856A87
last_name: Schmid
orcid: 0000-0002-6978-7329
- first_name: Christian
full_name: Hilbe, Christian
id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87
last_name: Hilbe
orcid: 0000-0001-5116-955X
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin
full_name: Nowak, Martin
last_name: Nowak
citation:
ama: Schmid L, Hilbe C, Chatterjee K, Nowak M. Direct reciprocity between individuals
that use different strategy spaces. PLOS Computational Biology. 2022;18(6).
doi:10.1371/journal.pcbi.1010149
apa: Schmid, L., Hilbe, C., Chatterjee, K., & Nowak, M. (2022). Direct reciprocity
between individuals that use different strategy spaces. PLOS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010149
chicago: Schmid, Laura, Christian Hilbe, Krishnendu Chatterjee, and Martin Nowak.
“Direct Reciprocity between Individuals That Use Different Strategy Spaces.” PLOS
Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1010149.
ieee: L. Schmid, C. Hilbe, K. Chatterjee, and M. Nowak, “Direct reciprocity between
individuals that use different strategy spaces,” PLOS Computational Biology,
vol. 18, no. 6. Public Library of Science, 2022.
ista: Schmid L, Hilbe C, Chatterjee K, Nowak M. 2022. Direct reciprocity between
individuals that use different strategy spaces. PLOS Computational Biology. 18(6),
e1010149.
mla: Schmid, Laura, et al. “Direct Reciprocity between Individuals That Use Different
Strategy Spaces.” PLOS Computational Biology, vol. 18, no. 6, e1010149,
Public Library of Science, 2022, doi:10.1371/journal.pcbi.1010149.
short: L. Schmid, C. Hilbe, K. Chatterjee, M. Nowak, PLOS Computational Biology
18 (2022).
date_created: 2023-01-16T10:02:51Z
date_published: 2022-06-14T00:00:00Z
date_updated: 2025-07-14T09:09:49Z
day: '14'
ddc:
- '000'
- '570'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1010149
ec_funded: 1
external_id:
isi:
- '000843626800031'
pmid:
- '35700167'
file:
- access_level: open_access
checksum: 31b6b311b6731f1658277a9dfff6632c
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T11:28:13Z
date_updated: 2023-01-30T11:28:13Z
file_id: '12460'
file_name: 2022_PlosCompBio_Schmid.pdf
file_size: 3143222
relation: main_file
success: 1
file_date_updated: 2023-01-30T11:28:13Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '6'
keyword:
- Computational Theory and Mathematics
- Cellular and Molecular Neuroscience
- Genetics
- Molecular Biology
- Ecology
- Modeling and Simulation
- Ecology
- Evolution
- Behavior and Systematics
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: PLOS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Direct reciprocity between individuals that use different strategy spaces
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: 18
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
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: 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: '8767'
abstract:
- lang: eng
text: Resources are rarely distributed uniformly within a population. Heterogeneity
in the concentration of a drug, the quality of breeding sites, or wealth can all
affect evolutionary dynamics. In this study, we represent a collection of properties
affecting the fitness at a given location using a color. A green node is rich
in resources while a red node is poorer. More colors can represent a broader spectrum
of resource qualities. For a population evolving according to the birth-death
Moran model, the first question we address is which structures, identified by
graph connectivity and graph coloring, are evolutionarily equivalent. We prove
that all properly two-colored, undirected, regular graphs are evolutionarily equivalent
(where “properly colored” means that no two neighbors have the same color). We
then compare the effects of background heterogeneity on properly two-colored graphs
to those with alternative schemes in which the colors are permuted. Finally, we
discuss dynamic coloring as a model for spatiotemporal resource fluctuations,
and we illustrate that random dynamic colorings often diminish the effects of
background heterogeneity relative to a proper two-coloring.
acknowledgement: 'We thank Igor Erovenko for many helpful comments on an earlier version
of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the
Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation
(A.M.). The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.'
article_number: e1008402
article_processing_charge: No
article_type: original
author:
- first_name: Kamran
full_name: Kaveh, Kamran
last_name: Kaveh
- first_name: Alex
full_name: McAvoy, Alex
last_name: McAvoy
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin A.
full_name: Nowak, Martin A.
last_name: Nowak
citation:
ama: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 2020;16(11). doi:10.1371/journal.pcbi.1008402
apa: Kaveh, K., McAvoy, A., Chatterjee, K., & Nowak, M. A. (2020). The Moran
process on 2-chromatic graphs. PLOS Computational Biology. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1008402
chicago: Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak.
“The Moran Process on 2-Chromatic Graphs.” PLOS Computational Biology.
Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1008402.
ieee: K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on
2-chromatic graphs,” PLOS Computational Biology, vol. 16, no. 11. Public
Library of Science, 2020.
ista: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 16(11), e1008402.
mla: Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” PLOS Computational
Biology, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:10.1371/journal.pcbi.1008402.
short: K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology
16 (2020).
date_created: 2020-11-18T07:20:23Z
date_published: 2020-11-05T00:00:00Z
date_updated: 2023-08-22T12:49:18Z
day: '05'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1008402
external_id:
isi:
- '000591317200004'
file:
- access_level: open_access
checksum: 555456dd0e47bcf9e0994bcb95577e88
content_type: application/pdf
creator: dernst
date_created: 2020-11-18T07:26:10Z
date_updated: 2020-11-18T07:26:10Z
file_id: '8768'
file_name: 2020_PlosCompBio_Kaveh.pdf
file_size: 2498594
relation: main_file
success: 1
file_date_updated: 2020-11-18T07:26:10Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '11'
keyword:
- Ecology
- Modelling and Simulation
- Computational Theory and Mathematics
- Genetics
- Ecology
- Evolution
- Behavior and Systematics
- Molecular Biology
- Cellular and Molecular Neuroscience
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: PLOS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: The Moran process on 2-chromatic graphs
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: 16
year: '2020'
...
---
_id: '6784'
abstract:
- lang: eng
text: Mathematical models have been used successfully at diverse scales of biological
organization, ranging from ecology and population dynamics to stochastic reaction
events occurring between individual molecules in single cells. Generally, many
biological processes unfold across multiple scales, with mutations being the best
studied example of how stochasticity at the molecular scale can influence outcomes
at the population scale. In many other contexts, however, an analogous link between
micro- and macro-scale remains elusive, primarily due to the challenges involved
in setting up and analyzing multi-scale models. Here, we employ such a model to
investigate how stochasticity propagates from individual biochemical reaction
events in the bacterial innate immune system to the ecology of bacteria and bacterial
viruses. We show analytically how the dynamics of bacterial populations are shaped
by the activities of immunity-conferring enzymes in single cells and how the ecological
consequences imply optimal bacterial defense strategies against viruses. Our results
suggest that bacterial populations in the presence of viruses can either optimize
their initial growth rate or their population size, with the first strategy favoring
simple immunity featuring a single restriction modification system and the second
strategy favoring complex bacterial innate immunity featuring several simultaneously
active restriction modification systems.
article_number: e1007168
article_processing_charge: No
article_type: original
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Maros
full_name: Pleska, Maros
id: 4569785E-F248-11E8-B48F-1D18A9856A87
last_name: Pleska
orcid: 0000-0001-7460-7479
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Ruess J, Pleska M, Guet CC, Tkačik G. Molecular noise of innate immunity shapes
bacteria-phage ecologies. PLoS Computational Biology. 2019;15(7). doi:10.1371/journal.pcbi.1007168
apa: Ruess, J., Pleska, M., Guet, C. C., & Tkačik, G. (2019). Molecular noise
of innate immunity shapes bacteria-phage ecologies. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007168
chicago: Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Molecular
Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” PLoS Computational
Biology. Public Library of Science, 2019. https://doi.org/10.1371/journal.pcbi.1007168.
ieee: J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Molecular noise of innate
immunity shapes bacteria-phage ecologies,” PLoS Computational Biology,
vol. 15, no. 7. Public Library of Science, 2019.
ista: Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Molecular noise of innate immunity
shapes bacteria-phage ecologies. PLoS Computational Biology. 15(7), e1007168.
mla: Ruess, Jakob, et al. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage
Ecologies.” PLoS Computational Biology, vol. 15, no. 7, e1007168, Public
Library of Science, 2019, doi:10.1371/journal.pcbi.1007168.
short: J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, PLoS Computational Biology 15
(2019).
date_created: 2019-08-11T21:59:19Z
date_published: 2019-07-02T00:00:00Z
date_updated: 2023-08-29T07:10:06Z
day: '02'
ddc:
- '570'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1371/journal.pcbi.1007168
external_id:
isi:
- '000481577700032'
file:
- access_level: open_access
checksum: 7ded4721b41c2a0fc66a1c634540416a
content_type: application/pdf
creator: dernst
date_created: 2019-08-12T12:27:26Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6803'
file_name: 2019_PlosComputBiology_Ruess.pdf
file_size: 2200003
relation: main_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 251D65D8-B435-11E9-9278-68D0E5697425
grant_number: '24210'
name: Effects of Stochasticity on the Function of Restriction-Modi cation Systems
at the Single-Cell Level
- _id: 251BCBEC-B435-11E9-9278-68D0E5697425
grant_number: RGY0079/2011
name: Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification
Systems
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '9786'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Molecular noise of innate immunity shapes bacteria-phage ecologies
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: 15
year: '2019'
...