---
_id: '1697'
abstract:
- lang: eng
text: Motion tracking is a challenge the visual system has to solve by reading out
the retinal population. It is still unclear how the information from different
neurons can be combined together to estimate the position of an object. Here we
recorded a large population of ganglion cells in a dense patch of salamander and
guinea pig retinas while displaying a bar moving diffusively. We show that the
bar’s position can be reconstructed from retinal activity with a precision in
the hyperacuity regime using a linear decoder acting on 100+ cells. We then took
advantage of this unprecedented precision to explore the spatial structure of
the retina’s population code. The classical view would have suggested that the
firing rates of the cells form a moving hill of activity tracking the bar’s position.
Instead, we found that most ganglion cells in the salamander fired sparsely and
idiosyncratically, so that their neural image did not track the bar. Furthermore,
ganglion cell activity spanned an area much larger than predicted by their receptive
fields, with cells coding for motion far in their surround. As a result, population
redundancy was high, and we could find multiple, disjoint subsets of neurons that
encoded the trajectory with high precision. This organization allows for diverse
collections of ganglion cells to represent high-accuracy motion information in
a form easily read out by downstream neural circuits.
acknowledgement: 'This work was supported by grants EY 014196 and EY 017934 to MJB,
ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence
Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the
Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to
VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010-
22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).'
article_number: e1004304
author:
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella Soler, Vicente
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: Kristina
full_name: Simmons, Kristina
last_name: Simmons
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy
decoding of dynamical motion from a large retinal population. PLoS Computational
Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304
apa: Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry,
M. (2015). High accuracy decoding of dynamical motion from a large retinal population.
PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304
chicago: Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora,
Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion
from a Large Retinal Population.” PLoS Computational Biology. Public Library
of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304.
ieee: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry,
“High accuracy decoding of dynamical motion from a large retinal population,”
PLoS Computational Biology, vol. 11, no. 7. Public Library of Science,
2015.
ista: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High
accuracy decoding of dynamical motion from a large retinal population. PLoS Computational
Biology. 11(7), e1004304.
mla: Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large
Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304,
Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304.
short: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS
Computational Biology 11 (2015).
date_created: 2018-12-11T11:53:31Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2021-01-12T06:52:35Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1004304
file:
- access_level: open_access
checksum: 472b979f3f1cffb37b3e503f085115ca
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:25Z
date_updated: 2020-07-14T12:45:12Z
file_id: '5212'
file_name: IST-2016-455-v1+1_journal.pcbi.1004304.pdf
file_size: 4673930
relation: main_file
file_date_updated: 2020-07-14T12:45:12Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _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_status: published
publisher: Public Library of Science
publist_id: '5447'
pubrep_id: '455'
quality_controlled: '1'
scopus_import: 1
status: public
title: High accuracy decoding of dynamical motion from a large retinal population
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: 11
year: '2015'
...
---
_id: '1701'
abstract:
- lang: eng
text: 'The activity of a neural network is defined by patterns of spiking and silence
from the individual neurons. Because spikes are (relatively) sparse, patterns
of activity with increasing numbers of spikes are less probable, but, with more
spikes, the number of possible patterns increases. This tradeoff between probability
and numerosity is mathematically equivalent to the relationship between entropy
and energy in statistical physics. We construct this relationship for populations
of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination
of direct and model-based analyses of experiments on the response of this network
to naturalistic movies. We see signs of a thermodynamic limit, where the entropy
per neuron approaches a smooth function of the energy per neuron as N increases.
The form of this function corresponds to the distribution of activity being poised
near an unusual kind of critical point. We suggest further tests of criticality,
and give a brief discussion of its functional significance. '
acknowledgement: "Research was supported in part by National Science Foundation Grants
PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant
R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support
was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation,
by the W. M. Keck Foundation, and by the Simons Foundation."
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Stephanie
full_name: Palmer, Stephanie
last_name: Palmer
- first_name: Michael
full_name: Berry Ii, Michael
last_name: Berry Ii
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality
in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112
apa: Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &
Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of
neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112
chicago: Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer,
Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality
in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112.
ieee: G. Tkačik et al., “Thermodynamics and signatures of criticality in
a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences,
pp. 11508–11513, 2015.
ista: Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015.
Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37),
11508–11513.
mla: Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network
of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015,
pp. 11508–13, doi:10.1073/pnas.1514188112.
short: G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek,
PNAS 112 (2015) 11508–11513.
date_created: 2018-12-11T11:53:33Z
date_published: 2015-09-15T00:00:00Z
date_updated: 2021-01-12T06:52:37Z
day: '15'
department:
- _id: GaTk
doi: 10.1073/pnas.1514188112
external_id:
pmid:
- '26330611'
intvolume: ' 112'
issue: '37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/
month: '09'
oa: 1
oa_version: Submitted Version
page: 11508 - 11513
pmid: 1
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5440'
quality_controlled: '1'
scopus_import: 1
status: public
title: Thermodynamics and signatures of criticality in a network of neurons
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1827'
abstract:
- lang: eng
text: Bow-tie or hourglass structure is a common architectural feature found in
many biological systems. A bow-tie in a multi-layered structure occurs when intermediate
layers have much fewer components than the input and output layers. Examples include
metabolism where a handful of building blocks mediate between multiple input nutrients
and multiple output biomass components, and signaling networks where information
from numerous receptor types passes through a small set of signaling pathways
to regulate multiple output genes. Little is known, however, about how bow-tie
architectures evolve. Here, we address the evolution of bow-tie architectures
using simulations of multi-layered systems evolving to fulfill a given input-output
goal. We find that bow-ties spontaneously evolve when the information in the evolutionary
goal can be compressed. Mathematically speaking, bow-ties evolve when the rank
of the input-output matrix describing the evolutionary goal is deficient. The
maximal compression possible (the rank of the goal) determines the size of the
narrowest part of the network—that is the bow-tie. A further requirement is that
a process is active to reduce the number of links in the network, such as product-rule
mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations.
This offers a mechanism to understand a common architectural principle of biological
systems, and a way to quantitate the effective rank of the goals under which they
evolved.
article_processing_charge: No
author:
- first_name: Tamar
full_name: Friedlander, Tamar
id: 36A5845C-F248-11E8-B48F-1D18A9856A87
last_name: Friedlander
- first_name: Avraham
full_name: Mayo, Avraham
last_name: Mayo
- first_name: Tsvi
full_name: Tlusty, Tsvi
last_name: Tlusty
- first_name: Uri
full_name: Alon, Uri
last_name: Alon
citation:
ama: Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures
in biology. PLoS Computational Biology. 2015;11(3). doi:10.1371/journal.pcbi.1004055
apa: Friedlander, T., Mayo, A., Tlusty, T., & Alon, U. (2015). Evolution of
bow-tie architectures in biology. PLoS Computational Biology. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1004055
chicago: Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution
of Bow-Tie Architectures in Biology.” PLoS Computational Biology. Public
Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.
ieee: T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures
in biology,” PLoS Computational Biology, vol. 11, no. 3. Public Library
of Science, 2015.
ista: Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures
in biology. PLoS Computational Biology. 11(3).
mla: Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.”
PLoS Computational Biology, vol. 11, no. 3, Public Library of Science,
2015, doi:10.1371/journal.pcbi.1004055.
short: T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11
(2015).
date_created: 2018-12-11T11:54:14Z
date_published: 2015-03-23T00:00:00Z
date_updated: 2023-02-23T14:07:51Z
day: '23'
ddc:
- '576'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1004055
ec_funded: 1
file:
- access_level: open_access
checksum: b8aa66f450ff8de393014b87ec7d2efb
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:39Z
date_updated: 2020-07-14T12:45:17Z
file_id: '5161'
file_name: IST-2016-452-v1+1_journal.pcbi.1004055.pdf
file_size: 1811647
relation: main_file
file_date_updated: 2020-07-14T12:45:17Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '5278'
pubrep_id: '452'
quality_controlled: '1'
related_material:
record:
- id: '9718'
relation: research_data
status: public
- id: '9773'
relation: research_data
status: public
scopus_import: 1
status: public
title: Evolution of bow-tie architectures in biology
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: 11
year: '2015'
...
---
_id: '1861'
abstract:
- lang: eng
text: Continuous-time Markov chains are commonly used in practice for modeling biochemical
reaction networks in which the inherent randomness of themolecular interactions
cannot be ignored. This has motivated recent research effort into methods for
parameter inference and experiment design for such models. The major difficulty
is that such methods usually require one to iteratively solve the chemical master
equation that governs the time evolution of the probability distribution of the
system. This, however, is rarely possible, and even approximation techniques remain
limited to relatively small and simple systems. An alternative explored in this
article is to base methods on only some low-order moments of the entire probability
distribution. We summarize the theory behind such moment-based methods for parameter
inference and experiment design and provide new case studies where we investigate
their performance.
acknowledgement: "HYCON2; EC; European Commission\r\n"
article_number: '8'
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: John
full_name: Lygeros, John
last_name: Lygeros
citation:
ama: Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment
design for stochastic biochemical reaction networks. ACM Transactions on Modeling
and Computer Simulation. 2015;25(2). doi:10.1145/2688906
apa: Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference
and experiment design for stochastic biochemical reaction networks. ACM Transactions
on Modeling and Computer Simulation. ACM. https://doi.org/10.1145/2688906
chicago: Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference
and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions
on Modeling and Computer Simulation. ACM, 2015. https://doi.org/10.1145/2688906.
ieee: J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and
experiment design for stochastic biochemical reaction networks,” ACM Transactions
on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015.
ista: Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and
experiment design for stochastic biochemical reaction networks. ACM Transactions
on Modeling and Computer Simulation. 25(2), 8.
mla: Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference
and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions
on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906.
short: J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation
25 (2015).
date_created: 2018-12-11T11:54:25Z
date_published: 2015-02-01T00:00:00Z
date_updated: 2021-01-12T06:53:41Z
day: '01'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1145/2688906
intvolume: ' 25'
issue: '2'
language:
- iso: eng
month: '02'
oa_version: None
publication: ACM Transactions on Modeling and Computer Simulation
publication_status: published
publisher: ACM
publist_id: '5238'
quality_controlled: '1'
scopus_import: 1
status: public
title: Moment-based methods for parameter inference and experiment design for stochastic
biochemical reaction networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2015'
...
---
_id: '1885'
abstract:
- lang: eng
text: 'The concept of positional information is central to our understanding of
how cells determine their location in a multicellular structure and thereby their
developmental fates. Nevertheless, positional information has neither been defined
mathematically nor quantified in a principled way. Here we provide an information-theoretic
definition in the context of developmental gene expression patterns and examine
the features of expression patterns that affect positional information quantitatively.
We connect positional information with the concept of positional error and develop
tools to directly measure information and error from experimental data. We illustrate
our framework for the case of gap gene expression patterns in the early Drosophila
embryo and show how information that is distributed among only four genes is sufficient
to determine developmental fates with nearly single-cell resolution. Our approach
can be generalized to a variety of different model systems; procedures and examples
are discussed in detail. '
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Julien
full_name: Dubuis, Julien
last_name: Dubuis
- first_name: Mariela
full_name: Petkova, Mariela
last_name: Petkova
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
citation:
ama: 'Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional
error, and readout precision in morphogenesis: A mathematical framework. Genetics.
2015;199(1):39-59. doi:10.1534/genetics.114.171850'
apa: 'Tkačik, G., Dubuis, J., Petkova, M., & Gregor, T. (2015). Positional information,
positional error, and readout precision in morphogenesis: A mathematical framework.
Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.114.171850'
chicago: 'Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional
Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical
Framework.” Genetics. Genetics Society of America, 2015. https://doi.org/10.1534/genetics.114.171850.'
ieee: 'G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information,
positional error, and readout precision in morphogenesis: A mathematical framework,”
Genetics, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.'
ista: 'Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional
error, and readout precision in morphogenesis: A mathematical framework. Genetics.
199(1), 39–59.'
mla: 'Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout
Precision in Morphogenesis: A Mathematical Framework.” Genetics, vol. 199,
no. 1, Genetics Society of America, 2015, pp. 39–59, doi:10.1534/genetics.114.171850.'
short: G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.
date_created: 2018-12-11T11:54:32Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:53:50Z
day: '01'
department:
- _id: GaTk
doi: 10.1534/genetics.114.171850
intvolume: ' 199'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1404.5599
month: '01'
oa: 1
oa_version: Preprint
page: 39 - 59
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '5210'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Positional information, positional error, and readout precision in morphogenesis:
A mathematical framework'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 199
year: '2015'
...
---
_id: '1940'
abstract:
- lang: eng
text: We typically think of cells as responding to external signals independently
by regulating their gene expression levels, yet they often locally exchange information
and coordinate. Can such spatial coupling be of benefit for conveying signals
subject to gene regulatory noise? Here we extend our information-theoretic framework
for gene regulation to spatially extended systems. As an example, we consider
a lattice of nuclei responding to a concentration field of a transcriptional regulator
(the "input") by expressing a single diffusible target gene. When input
concentrations are low, diffusive coupling markedly improves information transmission;
optimal gene activation functions also systematically change. A qualitatively
new regulatory strategy emerges where individual cells respond to the input in
a nearly step-like fashion that is subsequently averaged out by strong diffusion.
While motivated by early patterning events in the Drosophila embryo, our framework
is generically applicable to spatially coupled stochastic gene expression models.
article_number: '062710'
author:
- 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: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks.
IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter
Physics. 2015;91(6). doi:10.1103/PhysRevE.91.062710
apa: Sokolowski, T. R., & Tkačik, G. (2015). Optimizing information flow in
small genetic networks. IV. Spatial coupling. Physical Review E Statistical
Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.91.062710
chicago: Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in
Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical
Nonlinear and Soft Matter Physics. American Institute of Physics, 2015. https://doi.org/10.1103/PhysRevE.91.062710.
ieee: T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic
networks. IV. Spatial coupling,” Physical Review E Statistical Nonlinear and
Soft Matter Physics, vol. 91, no. 6. American Institute of Physics, 2015.
ista: Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic
networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft
Matter Physics. 91(6), 062710.
mla: Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small
Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear
and Soft Matter Physics, vol. 91, no. 6, 062710, American Institute of Physics,
2015, doi:10.1103/PhysRevE.91.062710.
short: T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft
Matter Physics 91 (2015).
date_created: 2018-12-11T11:54:49Z
date_published: 2015-06-15T00:00:00Z
date_updated: 2021-01-12T06:54:13Z
day: '15'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.91.062710
intvolume: ' 91'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1501.04015
month: '06'
oa: 1
oa_version: Preprint
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5145'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimizing information flow in small genetic networks. IV. Spatial coupling
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 91
year: '2015'
...
---
_id: '1538'
abstract:
- lang: eng
text: Systems biology rests on the idea that biological complexity can be better
unraveled through the interplay of modeling and experimentation. However, the
success of this approach depends critically on the informativeness of the chosen
experiments, which is usually unknown a priori. Here, we propose a systematic
scheme based on iterations of optimal experiment design, flow cytometry experiments,
and Bayesian parameter inference to guide the discovery process in the case of
stochastic biochemical reaction networks. To illustrate the benefit of our methodology,
we apply it to the characterization of an engineered light-inducible gene expression
circuit in yeast and compare the performance of the resulting model with models
identified from nonoptimal experiments. In particular, we compare the parameter
posterior distributions and the precision to which the outcome of future experiments
can be predicted. Moreover, we illustrate how the identified stochastic model
can be used to determine light induction patterns that make either the average
amount of protein or the variability in a population of cells follow a desired
profile. Our results show that optimal experiment design allows one to derive
models that are accurate enough to precisely predict and regulate the protein
expression in heterogeneous cell populations over extended periods of time.
acknowledgement: 'J.R., F.P., and J.L. acknowledge support from the European Commission
under the Network of Excellence HYCON2 (highly-complex and networked control systems)
and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People
Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme
FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges
support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). '
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Francesca
full_name: Parise, Francesca
last_name: Parise
- first_name: Andreas
full_name: Milias Argeitis, Andreas
last_name: Milias Argeitis
- first_name: Mustafa
full_name: Khammash, Mustafa
last_name: Khammash
- first_name: John
full_name: Lygeros, John
last_name: Lygeros
citation:
ama: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment
design guides the characterization of a light-inducible gene expression circuit.
PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112
apa: Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J.
(2015). Iterative experiment design guides the characterization of a light-inducible
gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112
chicago: Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash,
and John Lygeros. “Iterative Experiment Design Guides the Characterization of
a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1423947112.
ieee: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153,
2015.
ista: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit. PNAS. 112(26), 8148–8153.
mla: Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization
of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26,
National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112.
short: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112
(2015) 8148–8153.
date_created: 2018-12-11T11:52:36Z
date_published: 2015-06-30T00:00:00Z
date_updated: 2021-01-12T06:51:27Z
day: '30'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1073/pnas.1423947112
ec_funded: 1
external_id:
pmid:
- '26085136'
intvolume: ' 112'
issue: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/
month: '06'
oa: 1
oa_version: Submitted Version
page: 8148 - 8153
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5633'
quality_controlled: '1'
scopus_import: 1
status: public
title: Iterative experiment design guides the characterization of a light-inducible
gene expression circuit
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1539'
abstract:
- lang: eng
text: 'Many stochastic models of biochemical reaction networks contain some chemical
species for which the number of molecules that are present in the system can only
be finite (for instance due to conservation laws), but also other species that
can be present in arbitrarily large amounts. The prime example of such networks
are models of gene expression, which typically contain a small and finite number
of possible states for the promoter but an infinite number of possible states
for the amount of mRNA and protein. One of the main approaches to analyze such
models is through the use of equations for the time evolution of moments of the
chemical species. Recently, a new approach based on conditional moments of the
species with infinite state space given all the different possible states of the
finite species has been proposed. It was argued that this approach allows one
to capture more details about the full underlying probability distribution with
a smaller number of equations. Here, I show that the result that less moments
provide more information can only stem from an unnecessarily complicated description
of the system in the classical formulation. The foundation of this argument will
be the derivation of moment equations that describe the complete probability distribution
over the finite state space but only low-order moments over the infinite state
space. I will show that the number of equations that is needed is always less
than what was previously claimed and always less than the number of conditional
moment equations up to the same order. To support these arguments, a symbolic
algorithm is provided that can be used to derive minimal systems of unconditional
moment equations for models with partially finite state space. '
article_number: '244103'
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Ruess J. Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space. Journal of Chemical Physics.
2015;143(24). doi:10.1063/1.4937937
apa: Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
American Institute of Physics. https://doi.org/10.1063/1.4937937
chicago: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics.
American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937.
ieee: J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space,” Journal of Chemical Physics,
vol. 143, no. 24. American Institute of Physics, 2015.
ista: Ruess J. 2015. Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
143(24), 244103.
mla: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics,
vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937.
short: J. Ruess, Journal of Chemical Physics 143 (2015).
date_created: 2018-12-11T11:52:36Z
date_published: 2015-12-22T00:00:00Z
date_updated: 2021-01-12T06:51:28Z
day: '22'
ddc:
- '000'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1063/1.4937937
ec_funded: 1
file:
- access_level: open_access
checksum: 838657118ae286463a2b7737319f35ce
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:07:43Z
date_updated: 2020-07-14T12:45:01Z
file_id: '4641'
file_name: IST-2016-593-v1+1_Minimal_moment_equations.pdf
file_size: 605355
relation: main_file
file_date_updated: 2020-07-14T12:45:01Z
has_accepted_license: '1'
intvolume: ' 143'
issue: '24'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Journal of Chemical Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5632'
pubrep_id: '593'
quality_controlled: '1'
scopus_import: 1
status: public
title: Minimal moment equations for stochastic models of biochemical reaction networks
with partially finite state space
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 143
year: '2015'
...
---
_id: '1564'
article_number: '145'
author:
- first_name: Matthieu
full_name: Gilson, Matthieu
last_name: Gilson
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
citation:
ama: 'Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the
interaction of different forms of plasticity. Frontiers in Computational Neuroscience.
2015;9(11). doi:10.3389/fncom.2015.00145'
apa: 'Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural
computation from the interaction of different forms of plasticity. Frontiers
in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145'
chicago: 'Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent
Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers
in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.'
ieee: 'M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation
from the interaction of different forms of plasticity,” Frontiers in Computational
Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.'
ista: 'Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation
from the interaction of different forms of plasticity. Frontiers in Computational
Neuroscience. 9(11), 145.'
mla: 'Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the
Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience,
vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.'
short: M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9
(2015).
date_created: 2018-12-11T11:52:45Z
date_published: 2015-11-30T00:00:00Z
date_updated: 2021-01-12T06:51:37Z
day: '30'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.3389/fncom.2015.00145
ec_funded: 1
file:
- access_level: open_access
checksum: cea73b6d3ef1579f32da10b82f4de4fd
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:09Z
date_updated: 2020-07-14T12:45:02Z
file_id: '4927'
file_name: IST-2016-479-v1+1_fncom-09-00145.pdf
file_size: 187038
relation: main_file
file_date_updated: 2020-07-14T12:45:02Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Frontiers in Computational Neuroscience
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '5607'
pubrep_id: '479'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Editorial: Emergent neural computation from the interaction of different forms
of plasticity'
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: 9
year: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, self-organized behavioral development provides
more questions than answers. Are there special functional units for curiosity,
motivation, and creativity? This paper argues that these features can be grounded
in synaptic plasticity itself, without requiring any higher-level constructs.
We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
self-learning systems and apply it to a number of complex robotic systems as a
test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
pmid:
- '26504200'
intvolume: ' 112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1576'
abstract:
- lang: eng
text: 'Gene expression is controlled primarily by interactions between transcription
factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured
well by thermodynamic models of regulation. These models, however, neglect regulatory
crosstalk: the possibility that noncognate TFs could initiate transcription, with
potentially disastrous effects for the cell. Here, we estimate the importance
of crosstalk, suggest that its avoidance strongly constrains equilibrium models
of TF binding, and propose an alternative nonequilibrium scheme that implements
kinetic proofreading to suppress erroneous initiation. This proposal is consistent
with the observed covalent modifications of the transcriptional apparatus and
predicts increased noise in gene expression as a trade-off for improved specificity.
Using information theory, we quantify this trade-off to find when optimal proofreading
architectures are favored over their equilibrium counterparts. Such architectures
exhibit significant super-Poisson noise at low expression in steady state.'
article_number: '248101'
author:
- first_name: Sarah A
full_name: Cepeda Humerez, Sarah A
id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87
last_name: Cepeda Humerez
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Cepeda Humerez SA, Rieckh G, Tkačik G. Stochastic proofreading mechanism alleviates
crosstalk in transcriptional regulation. Physical Review Letters. 2015;115(24).
doi:10.1103/PhysRevLett.115.248101
apa: Cepeda Humerez, S. A., Rieckh, G., & Tkačik, G. (2015). Stochastic proofreading
mechanism alleviates crosstalk in transcriptional regulation. Physical Review
Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.115.248101
chicago: Cepeda Humerez, Sarah A, Georg Rieckh, and Gašper Tkačik. “Stochastic Proofreading
Mechanism Alleviates Crosstalk in Transcriptional Regulation.” Physical Review
Letters. American Physical Society, 2015. https://doi.org/10.1103/PhysRevLett.115.248101.
ieee: S. A. Cepeda Humerez, G. Rieckh, and G. Tkačik, “Stochastic proofreading mechanism
alleviates crosstalk in transcriptional regulation,” Physical Review Letters,
vol. 115, no. 24. American Physical Society, 2015.
ista: Cepeda Humerez SA, Rieckh G, Tkačik G. 2015. Stochastic proofreading mechanism
alleviates crosstalk in transcriptional regulation. Physical Review Letters. 115(24),
248101.
mla: Cepeda Humerez, Sarah A., et al. “Stochastic Proofreading Mechanism Alleviates
Crosstalk in Transcriptional Regulation.” Physical Review Letters, vol.
115, no. 24, 248101, American Physical Society, 2015, doi:10.1103/PhysRevLett.115.248101.
short: S.A. Cepeda Humerez, G. Rieckh, G. Tkačik, Physical Review Letters 115 (2015).
date_created: 2018-12-11T11:52:49Z
date_published: 2015-12-08T00:00:00Z
date_updated: 2023-09-07T12:55:21Z
day: '08'
department:
- _id: GaTk
doi: 10.1103/PhysRevLett.115.248101
ec_funded: 1
intvolume: ' 115'
issue: '24'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1504.05716
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Physical Review Letters
publication_status: published
publisher: American Physical Society
publist_id: '5595'
quality_controlled: '1'
related_material:
record:
- id: '6473'
relation: part_of_dissertation
status: public
scopus_import: 1
status: public
title: Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 115
year: '2015'
...
---
_id: '9712'
article_processing_charge: No
author:
- first_name: Murat
full_name: Tugrul, Murat
id: 37C323C6-F248-11E8-B48F-1D18A9856A87
last_name: Tugrul
orcid: 0000-0002-8523-0758
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- 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: 0000-0002-6699-1455
citation:
ama: Tugrul M, Paixao T, Barton NH, Tkačik G. Other fitness models for comparison
& for interacting TFBSs. 2015. doi:10.1371/journal.pgen.1005639.s001
apa: Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Other fitness
models for comparison & for interacting TFBSs. Public Library of Science.
https://doi.org/10.1371/journal.pgen.1005639.s001
chicago: Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Other
Fitness Models for Comparison & for Interacting TFBSs.” Public Library of
Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.s001.
ieee: M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Other fitness models for
comparison & for interacting TFBSs.” Public Library of Science, 2015.
ista: Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Other fitness models for comparison
& for interacting TFBSs, Public Library of Science, 10.1371/journal.pgen.1005639.s001.
mla: Tugrul, Murat, et al. Other Fitness Models for Comparison & for Interacting
TFBSs. Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.s001.
short: M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, (2015).
date_created: 2021-07-23T12:00:37Z
date_published: 2015-11-06T00:00:00Z
date_updated: 2025-05-28T11:57:04Z
day: '06'
department:
- _id: NiBa
- _id: CaGu
- _id: GaTk
doi: 10.1371/journal.pgen.1005639.s001
month: '11'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1666'
relation: used_in_publication
status: public
status: public
title: Other fitness models for comparison & for interacting TFBSs
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '9718'
article_processing_charge: No
author:
- first_name: Tamar
full_name: Friedlander, Tamar
id: 36A5845C-F248-11E8-B48F-1D18A9856A87
last_name: Friedlander
- first_name: Avraham E.
full_name: Mayo, Avraham E.
last_name: Mayo
- first_name: Tsvi
full_name: Tlusty, Tsvi
last_name: Tlusty
- first_name: Uri
full_name: Alon, Uri
last_name: Alon
citation:
ama: Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015.
doi:10.1371/journal.pcbi.1004055.s001
apa: Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Supporting
information text. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s001
chicago: Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting
Information Text.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s001.
ieee: T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information
text.” Public Library of Science, 2015.
ista: Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text,
Public Library of Science, 10.1371/journal.pcbi.1004055.s001.
mla: Friedlander, Tamar, et al. Supporting Information Text. Public Library
of Science, 2015, doi:10.1371/journal.pcbi.1004055.s001.
short: T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).
date_created: 2021-07-26T08:35:23Z
date_published: 2015-03-23T00:00:00Z
date_updated: 2023-02-23T10:16:13Z
day: '23'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1004055.s001
month: '03'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1827'
relation: used_in_publication
status: public
status: public
title: Supporting information text
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '9773'
article_processing_charge: No
author:
- first_name: Tamar
full_name: Friedlander, Tamar
id: 36A5845C-F248-11E8-B48F-1D18A9856A87
last_name: Friedlander
- first_name: Avraham E.
full_name: Mayo, Avraham E.
last_name: Mayo
- first_name: Tsvi
full_name: Tlusty, Tsvi
last_name: Tlusty
- first_name: Uri
full_name: Alon, Uri
last_name: Alon
citation:
ama: Friedlander T, Mayo AE, Tlusty T, Alon U. Evolutionary simulation code. 2015.
doi:10.1371/journal.pcbi.1004055.s002
apa: Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Evolutionary
simulation code. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s002
chicago: Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Evolutionary
Simulation Code.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s002.
ieee: T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation
code.” Public Library of Science, 2015.
ista: Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Evolutionary simulation code,
Public Library of Science, 10.1371/journal.pcbi.1004055.s002.
mla: Friedlander, Tamar, et al. Evolutionary Simulation Code. Public Library
of Science, 2015, doi:10.1371/journal.pcbi.1004055.s002.
short: T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).
date_created: 2021-08-05T12:58:07Z
date_published: 2015-03-23T00:00:00Z
date_updated: 2023-02-23T10:16:13Z
day: '23'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1004055.s002
month: '03'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1827'
relation: used_in_publication
status: public
status: public
title: Evolutionary simulation code
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '3263'
abstract:
- lang: eng
text: Adaptation in the retina is thought to optimize the encoding of natural light
signals into sequences of spikes sent to the brain. While adaptive changes in
retinal processing to the variations of the mean luminance level and second-order
stimulus statistics have been documented before, no such measurements have been
performed when higher-order moments of the light distribution change. We therefore
measured the ganglion cell responses in the tiger salamander retina to controlled
changes in the second (contrast), third (skew) and fourth (kurtosis) moments of
the light intensity distribution of spatially uniform temporally independent stimuli.
The skew and kurtosis of the stimuli were chosen to cover the range observed in
natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear
models that capture well the retinal encoding properties across all stimuli. We
found that the encoding properties of retinal ganglion cells change only marginally
when higher-order statistics change, compared to the changes observed in response
to the variation in contrast. By analyzing optimal coding in LN-type models, we
showed that neurons can maintain a high information rate without large dynamic
adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli,
spatio-temporal summation within the receptive field averages away non-gaussian
aspects of the light intensity distribution.
acknowledgement: "This work was supported by The Israel Science Foundation and The
Human Frontiers Science Program.\r\nWe thank the referees for helping significantly
improve this paper. We also thank Vijay Balasubramanian, Kristina Simmons, and Jason
Prentice for stimulating discussions. GT wishes to thank the faculty and students
of the “Methods in Computational Neuroscience” course at Marine Biological Laboratory,
Woods Hole.\r\n"
article_number: e85841
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Anandamohan
full_name: Ghosh, Anandamohan
last_name: Ghosh
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
- first_name: Ronen
full_name: Segev, Ronen
last_name: Segev
citation:
ama: Tkačik G, Ghosh A, Schneidman E, Segev R. Adaptation to changes in higher-order
stimulus statistics in the salamander retina. PLoS One. 2014;9(1). doi:10.1371/journal.pone.0085841
apa: Tkačik, G., Ghosh, A., Schneidman, E., & Segev, R. (2014). Adaptation to
changes in higher-order stimulus statistics in the salamander retina. PLoS
One. Public Library of Science. https://doi.org/10.1371/journal.pone.0085841
chicago: Tkačik, Gašper, Anandamohan Ghosh, Elad Schneidman, and Ronen Segev. “Adaptation
to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” PLoS
One. Public Library of Science, 2014. https://doi.org/10.1371/journal.pone.0085841.
ieee: G. Tkačik, A. Ghosh, E. Schneidman, and R. Segev, “Adaptation to changes in
higher-order stimulus statistics in the salamander retina,” PLoS One, vol.
9, no. 1. Public Library of Science, 2014.
ista: Tkačik G, Ghosh A, Schneidman E, Segev R. 2014. Adaptation to changes in higher-order
stimulus statistics in the salamander retina. PLoS One. 9(1), e85841.
mla: Tkačik, Gašper, et al. “Adaptation to Changes in Higher-Order Stimulus Statistics
in the Salamander Retina.” PLoS One, vol. 9, no. 1, e85841, Public Library
of Science, 2014, doi:10.1371/journal.pone.0085841.
short: G. Tkačik, A. Ghosh, E. Schneidman, R. Segev, PLoS One 9 (2014).
date_created: 2018-12-11T12:02:20Z
date_published: 2014-01-21T00:00:00Z
date_updated: 2021-01-12T07:42:14Z
day: '21'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0085841
file:
- access_level: open_access
checksum: 1d5816b343abe5eadc3eb419bcece971
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:28Z
date_updated: 2020-07-14T12:46:06Z
file_id: '5011'
file_name: IST-2016-432-v1+1_journal.pone.0085841.pdf
file_size: 1568524
relation: main_file
file_date_updated: 2020-07-14T12:46:06Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '3385'
pubrep_id: '432'
quality_controlled: '1'
scopus_import: 1
status: public
title: Adaptation to changes in higher-order stimulus statistics in the salamander
retina
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: 3FFCCD3A-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2014'
...
---
_id: '1708'
abstract:
- lang: eng
text: It has been long argued that, because of inherent ambiguity and noise, the
brain needs to represent uncertainty in the form of probability distributions.
The neural encoding of such distributions remains however highly controversial.
Here we present a novel circuit model for representing multidimensional real-valued
distributions using a spike based spatio-temporal code. Our model combines the
computational advantages of the currently competing models for probabilistic codes
and exhibits realistic neural responses along a variety of classic measures. Furthermore,
the model highlights the challenges associated with interpreting neural activity
in relation to behavioral uncertainty and points to alternative population-level
approaches for the experimental validation of distributed representations.
author:
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Sophie
full_name: Denève, Sophie
last_name: Denève
citation:
ama: 'Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking
neural networks. In: Vol 3. Neural Information Processing Systems; 2014:2024-2032.'
apa: 'Savin, C., & Denève, S. (2014). Spatio-temporal representations of uncertainty
in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural
Information Processing Systems, Montreal, Canada: Neural Information Processing
Systems.'
chicago: Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of
Uncertainty in Spiking Neural Networks,” 3:2024–32. Neural Information Processing
Systems, 2014.
ieee: 'C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in
spiking neural networks,” presented at the NIPS: Neural Information Processing
Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.'
ista: 'Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in
spiking neural networks. NIPS: Neural Information Processing Systems vol. 3, 2024–2032.'
mla: Savin, Cristina, and Sophie Denève. Spatio-Temporal Representations of Uncertainty
in Spiking Neural Networks. Vol. 3, no. January, Neural Information Processing
Systems, 2014, pp. 2024–32.
short: C. Savin, S. Denève, in:, Neural Information Processing Systems, 2014, pp.
2024–2032.
conference:
end_date: 2014-12-13
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2014-12-08
date_created: 2018-12-11T11:53:35Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2021-01-12T06:52:40Z
day: '01'
department:
- _id: GaTk
intvolume: ' 3'
issue: January
language:
- iso: eng
main_file_link:
- url: http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf
month: '01'
oa_version: None
page: 2024 - 2032
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5427'
quality_controlled: '1'
scopus_import: 1
status: public
title: Spatio-temporal representations of uncertainty in spiking neural networks
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2014'
...
---
_id: '1886'
abstract:
- lang: eng
text: 'Information processing in the sensory periphery is shaped by natural stimulus
statistics. In the periphery, a transmission bottleneck constrains performance;
thus efficient coding implies that natural signal components with a predictably
wider range should be compressed. In a different regime—when sampling limitations
constrain performance—efficient coding implies that more resources should be allocated
to informative features that are more variable. We propose that this regime is
relevant for sensory cortex when it extracts complex features from limited numbers
of sensory samples. To test this prediction, we use central visual processing
as a model: we show that visual sensitivity for local multi-point spatial correlations,
described by dozens of independently-measured parameters, can be quantitatively
predicted from the structure of natural images. This suggests that efficient coding
applies centrally, where it extends to higher-order sensory features and operates
in a regime in which sensitivity increases with feature variability.'
article_number: e03722
author:
- first_name: Ann
full_name: Hermundstad, Ann
last_name: Hermundstad
- first_name: John
full_name: Briguglio, John
last_name: Briguglio
- first_name: Mary
full_name: Conte, Mary
last_name: Conte
- first_name: Jonathan
full_name: Victor, Jonathan
last_name: Victor
- first_name: Vijay
full_name: Balasubramanian, Vijay
last_name: Balasubramanian
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G.
Variance predicts salience in central sensory processing. eLife. 2014;(November).
doi:10.7554/eLife.03722
apa: Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V.,
& Tkačik, G. (2014). Variance predicts salience in central sensory processing.
ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.03722
chicago: Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian,
and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.”
ELife. eLife Sciences Publications, 2014. https://doi.org/10.7554/eLife.03722.
ieee: A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and
G. Tkačik, “Variance predicts salience in central sensory processing,” eLife,
no. November. eLife Sciences Publications, 2014.
ista: Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G.
2014. Variance predicts salience in central sensory processing. eLife. (November),
e03722.
mla: Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.”
ELife, no. November, e03722, eLife Sciences Publications, 2014, doi:10.7554/eLife.03722.
short: A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G.
Tkačik, ELife (2014).
date_created: 2018-12-11T11:54:32Z
date_published: 2014-11-14T00:00:00Z
date_updated: 2021-01-12T06:53:50Z
day: '14'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.7554/eLife.03722
file:
- access_level: open_access
checksum: 766ac8999ac6e3364f10065a06024b8f
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:04Z
date_updated: 2020-07-14T12:45:20Z
file_id: '4922'
file_name: IST-2016-420-v1+1_e03722.full.pdf
file_size: 5117086
relation: main_file
file_date_updated: 2020-07-14T12:45:20Z
has_accepted_license: '1'
issue: November
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: eLife
publication_status: published
publisher: eLife Sciences Publications
publist_id: '5209'
pubrep_id: '420'
quality_controlled: '1'
scopus_import: 1
status: public
title: Variance predicts salience in central sensory processing
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: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '1896'
abstract:
- lang: eng
text: 'Biopolymer length regulation is a complex process that involves a large number
of biological, chemical, and physical subprocesses acting simultaneously across
multiple spatial and temporal scales. An illustrative example important for genomic
stability is the length regulation of telomeres - nucleoprotein structures at
the ends of linear chromosomes consisting of tandemly repeated DNA sequences and
a specialized set of proteins. Maintenance of telomeres is often facilitated by
the enzyme telomerase but, particularly in telomerase-free systems, the maintenance
of chromosomal termini depends on alternative lengthening of telomeres (ALT) mechanisms
mediated by recombination. Various linear and circular DNA structures were identified
to participate in ALT, however, dynamics of the whole process is still poorly
understood. We propose a chemical kinetics model of ALT with kinetic rates systematically
derived from the biophysics of DNA diffusion and looping. The reaction system
is reduced to a coagulation-fragmentation system by quasi-steady-state approximation.
The detailed treatment of kinetic rates yields explicit formulas for expected
size distributions of telomeres that demonstrate the key role played by the J
factor, a quantitative measure of bending of polymers. The results are in agreement
with experimental data and point out interesting phenomena: an appearance of very
long telomeric circles if the total telomere density exceeds a critical value
(excess mass) and a nonlinear response of the telomere size distributions to the
amount of telomeric DNA in the system. The results can be of general importance
for understanding dynamics of telomeres in telomerase-independent systems as this
mode of telomere maintenance is similar to the situation in tumor cells lacking
telomerase activity. Furthermore, due to its universality, the model may also
serve as a prototype of an interaction between linear and circular DNA structures
in various settings.'
acknowledgement: The work was supported by the VEGA Grant No. 1/0459/13 (R.K. and
K.B.).
article_number: '032701'
article_processing_charge: No
author:
- first_name: Richard
full_name: Kollár, Richard
last_name: Kollár
- 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: Jozef
full_name: Nosek, Jozef
last_name: Nosek
- first_name: Ľubomír
full_name: Tomáška, Ľubomír
last_name: Tomáška
citation:
ama: Kollár R, Bodova K, Nosek J, Tomáška Ľ. Mathematical model of alternative mechanism
of telomere length maintenance. Physical Review E Statistical Nonlinear and
Soft Matter Physics. 2014;89(3). doi:10.1103/PhysRevE.89.032701
apa: Kollár, R., Bodova, K., Nosek, J., & Tomáška, Ľ. (2014). Mathematical model
of alternative mechanism of telomere length maintenance. Physical Review E
Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.
https://doi.org/10.1103/PhysRevE.89.032701
chicago: Kollár, Richard, Katarina Bodova, Jozef Nosek, and Ľubomír Tomáška. “Mathematical
Model of Alternative Mechanism of Telomere Length Maintenance.” Physical Review
E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics,
2014. https://doi.org/10.1103/PhysRevE.89.032701.
ieee: R. Kollár, K. Bodova, J. Nosek, and Ľ. Tomáška, “Mathematical model of alternative
mechanism of telomere length maintenance,” Physical Review E Statistical Nonlinear
and Soft Matter Physics, vol. 89, no. 3. American Institute of Physics, 2014.
ista: Kollár R, Bodova K, Nosek J, Tomáška Ľ. 2014. Mathematical model of alternative
mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear
and Soft Matter Physics. 89(3), 032701.
mla: Kollár, Richard, et al. “Mathematical Model of Alternative Mechanism of Telomere
Length Maintenance.” Physical Review E Statistical Nonlinear and Soft Matter
Physics, vol. 89, no. 3, 032701, American Institute of Physics, 2014, doi:10.1103/PhysRevE.89.032701.
short: R. Kollár, K. Bodova, J. Nosek, Ľ. Tomáška, Physical Review E Statistical
Nonlinear and Soft Matter Physics 89 (2014).
date_created: 2018-12-11T11:54:35Z
date_published: 2014-03-04T00:00:00Z
date_updated: 2022-08-01T10:50:10Z
day: '04'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1103/PhysRevE.89.032701
intvolume: ' 89'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1402.0430
month: '03'
oa: 1
oa_version: Submitted Version
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5198'
scopus_import: '1'
status: public
title: Mathematical model of alternative mechanism of telomere length maintenance
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2014'
...
---
_id: '1909'
abstract:
- lang: eng
text: 'Summary: Phenotypes are often environmentally dependent, which requires organisms
to track environmental change. The challenge for organisms is to construct phenotypes
using the most accurate environmental cue. Here, we use a quantitative genetic
model of adaptation by additive genetic variance, within- and transgenerational
plasticity via linear reaction norms and indirect genetic effects respectively.
We show how the relative influence on the eventual phenotype of these components
depends on the predictability of environmental change (fast or slow, sinusoidal
or stochastic) and the developmental lag τ between when the environment is perceived
and when selection acts. We then decompose expected mean fitness into three components
(variance load, adaptation and fluctuation load) to study the fitness costs of
within- and transgenerational plasticity. A strongly negative maternal effect
coefficient m minimizes the variance load, but a strongly positive m minimises
the fluctuation load. The adaptation term is maximized closer to zero, with positive
or negative m preferred under different environmental scenarios. Phenotypic plasticity
is higher when τ is shorter and when the environment changes frequently between
seasonal extremes. Expected mean population fitness is highest away from highest
observed levels of phenotypic plasticity. Within- and transgenerational plasticity
act in concert to deliver well-adapted phenotypes, which emphasizes the need to
study both simultaneously when investigating phenotypic evolution.'
acknowledgement: 'Engineering and Physical Sciences Research Council. Grant Number:
EP/H031928/1'
author:
- first_name: Thomas
full_name: Ezard, Thomas
last_name: Ezard
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Rebecca
full_name: Hoyle, Rebecca
last_name: Hoyle
citation:
ama: Ezard T, Prizak R, Hoyle R. The fitness costs of adaptation via phenotypic
plasticity and maternal effects. Functional Ecology. 2014;28(3):693-701.
doi:10.1111/1365-2435.12207
apa: Ezard, T., Prizak, R., & Hoyle, R. (2014). The fitness costs of adaptation
via phenotypic plasticity and maternal effects. Functional Ecology. Wiley-Blackwell.
https://doi.org/10.1111/1365-2435.12207
chicago: Ezard, Thomas, Roshan Prizak, and Rebecca Hoyle. “The Fitness Costs of
Adaptation via Phenotypic Plasticity and Maternal Effects.” Functional Ecology.
Wiley-Blackwell, 2014. https://doi.org/10.1111/1365-2435.12207.
ieee: T. Ezard, R. Prizak, and R. Hoyle, “The fitness costs of adaptation via phenotypic
plasticity and maternal effects,” Functional Ecology, vol. 28, no. 3. Wiley-Blackwell,
pp. 693–701, 2014.
ista: Ezard T, Prizak R, Hoyle R. 2014. The fitness costs of adaptation via phenotypic
plasticity and maternal effects. Functional Ecology. 28(3), 693–701.
mla: Ezard, Thomas, et al. “The Fitness Costs of Adaptation via Phenotypic Plasticity
and Maternal Effects.” Functional Ecology, vol. 28, no. 3, Wiley-Blackwell,
2014, pp. 693–701, doi:10.1111/1365-2435.12207.
short: T. Ezard, R. Prizak, R. Hoyle, Functional Ecology 28 (2014) 693–701.
date_created: 2018-12-11T11:54:40Z
date_published: 2014-06-01T00:00:00Z
date_updated: 2021-01-12T06:54:00Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1111/1365-2435.12207
file:
- access_level: open_access
checksum: 3cbe8623174709a8ceec2103246f8fe0
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:45Z
date_updated: 2020-07-14T12:45:20Z
file_id: '5167'
file_name: IST-2016-419-v1+1_Ezard_et_al-2014-Functional_Ecology.pdf
file_size: 536154
relation: main_file
file_date_updated: 2020-07-14T12:45:20Z
has_accepted_license: '1'
intvolume: ' 28'
issue: '3'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 693 - 701
publication: Functional Ecology
publication_status: published
publisher: Wiley-Blackwell
publist_id: '5186'
pubrep_id: '419'
scopus_import: 1
status: public
title: The fitness costs of adaptation via phenotypic plasticity and maternal effects
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: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 28
year: '2014'
...
---
_id: '1928'
abstract:
- lang: eng
text: In infectious disease epidemiology the basic reproductive ratio, R0, is defined
as the average number of new infections caused by a single infected individual
in a fully susceptible population. Many models describing competition for hosts
between non-interacting pathogen strains in an infinite population lead to the
conclusion that selection favors invasion of new strains if and only if they have
higher R0 values than the resident. Here we demonstrate that this picture fails
in finite populations. Using a simple stochastic SIS model, we show that in general
there is no analogous optimization principle. We find that successive invasions
may in some cases lead to strains that infect a smaller fraction of the host population,
and that mutually invasible pathogen strains exist. In the limit of weak selection
we demonstrate that an optimization principle does exist, although it differs
from R0 maximization. For strains with very large R0, we derive an expression
for this local fitness function and use it to establish a lower bound for the
error caused by neglecting stochastic effects. Furthermore, we apply this weak
selection limit to investigate the selection dynamics in the presence of a trade-off
between the virulence and the transmission rate of a pathogen.
acknowledgement: J.H. received support from the Zdenek Bakala Foundation and the Mobility
Fund of Charles University in Prague.
author:
- first_name: Jan
full_name: Humplik, Jan
id: 2E9627A8-F248-11E8-B48F-1D18A9856A87
last_name: Humplik
- first_name: Alison
full_name: Hill, Alison
last_name: Hill
- first_name: Martin
full_name: Nowak, Martin
last_name: Nowak
citation:
ama: Humplik J, Hill A, Nowak M. Evolutionary dynamics of infectious diseases in
finite populations. Journal of Theoretical Biology. 2014;360:149-162. doi:10.1016/j.jtbi.2014.06.039
apa: Humplik, J., Hill, A., & Nowak, M. (2014). Evolutionary dynamics of infectious
diseases in finite populations. Journal of Theoretical Biology. Elsevier.
https://doi.org/10.1016/j.jtbi.2014.06.039
chicago: Humplik, Jan, Alison Hill, and Martin Nowak. “Evolutionary Dynamics of
Infectious Diseases in Finite Populations.” Journal of Theoretical Biology.
Elsevier, 2014. https://doi.org/10.1016/j.jtbi.2014.06.039.
ieee: J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases
in finite populations,” Journal of Theoretical Biology, vol. 360. Elsevier,
pp. 149–162, 2014.
ista: Humplik J, Hill A, Nowak M. 2014. Evolutionary dynamics of infectious diseases
in finite populations. Journal of Theoretical Biology. 360, 149–162.
mla: Humplik, Jan, et al. “Evolutionary Dynamics of Infectious Diseases in Finite
Populations.” Journal of Theoretical Biology, vol. 360, Elsevier, 2014,
pp. 149–62, doi:10.1016/j.jtbi.2014.06.039.
short: J. Humplik, A. Hill, M. Nowak, Journal of Theoretical Biology 360 (2014)
149–162.
date_created: 2018-12-11T11:54:46Z
date_published: 2014-11-07T00:00:00Z
date_updated: 2021-01-12T06:54:08Z
day: '07'
department:
- _id: GaTk
doi: 10.1016/j.jtbi.2014.06.039
intvolume: ' 360'
language:
- iso: eng
month: '11'
oa_version: None
page: 149 - 162
publication: Journal of Theoretical Biology
publication_status: published
publisher: Elsevier
publist_id: '5166'
scopus_import: 1
status: public
title: Evolutionary dynamics of infectious diseases in finite populations
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 360
year: '2014'
...