---
_id: '1320'
abstract:
- lang: eng
text: 'In recent years, several biomolecular systems have been shown to be scale-invariant
(SI), i.e. to show the same output dynamics when exposed to geometrically scaled
input signals (u → pu, p > 0) after pre-adaptation to accordingly scaled constant
inputs. In this article, we show that SI systems-as well as systems invariant
with respect to other input transformations-can realize nonlinear differential
operators: when excited by inputs obeying functional forms characteristic for
a given class of invariant systems, the systems'' outputs converge to constant
values directly quantifying the speed of the input.'
acknowledgement: The research leading to these results has received funding from the
People Programme (Marie Curie Actions) of the European Union's Seventh Framework
Programme (FP7/2007-2013) under REA grant agreement n° [291734]. Work supported
in part by grants AFOSR FA9550-14-1-0060 and NIH 1R01GM100473.
article_number: '7526722'
author:
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Eduardo
full_name: Sontag, Eduardo
last_name: Sontag
citation:
ama: 'Lang M, Sontag E. Scale-invariant systems realize nonlinear differential operators.
In: Vol 2016-July. IEEE; 2016. doi:10.1109/ACC.2016.7526722'
apa: 'Lang, M., & Sontag, E. (2016). Scale-invariant systems realize nonlinear
differential operators (Vol. 2016–July). Presented at the ACC: American Control
Conference, Boston, MA, USA: IEEE. https://doi.org/10.1109/ACC.2016.7526722'
chicago: Lang, Moritz, and Eduardo Sontag. “Scale-Invariant Systems Realize Nonlinear
Differential Operators,” Vol. 2016–July. IEEE, 2016. https://doi.org/10.1109/ACC.2016.7526722.
ieee: 'M. Lang and E. Sontag, “Scale-invariant systems realize nonlinear differential
operators,” presented at the ACC: American Control Conference, Boston, MA, USA,
2016, vol. 2016–July.'
ista: 'Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential
operators. ACC: American Control Conference vol. 2016–July, 7526722.'
mla: Lang, Moritz, and Eduardo Sontag. Scale-Invariant Systems Realize Nonlinear
Differential Operators. Vol. 2016–July, 7526722, IEEE, 2016, doi:10.1109/ACC.2016.7526722.
short: M. Lang, E. Sontag, in:, IEEE, 2016.
conference:
end_date: 2016-07-08
location: Boston, MA, USA
name: 'ACC: American Control Conference'
start_date: 2016-07-06
date_created: 2018-12-11T11:51:21Z
date_published: 2016-07-28T00:00:00Z
date_updated: 2021-01-12T06:49:51Z
day: '28'
ddc:
- '003'
- '621'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1109/ACC.2016.7526722
ec_funded: 1
file:
- access_level: local
checksum: 7219432b43defc62a0d45f48d4ce6a19
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:17Z
date_updated: 2020-07-14T12:44:43Z
file_id: '5203'
file_name: IST-2017-810-v1+1_root.pdf
file_size: 539166
relation: main_file
file_date_updated: 2020-07-14T12:44:43Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: IEEE
publist_id: '5950'
pubrep_id: '810'
quality_controlled: '1'
scopus_import: 1
status: public
title: Scale-invariant systems realize nonlinear differential operators
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-July
year: '2016'
...
---
_id: '1332'
abstract:
- lang: eng
text: Antibiotic-sensitive and -resistant bacteria coexist in natural environments
with low, if detectable, antibiotic concentrations. Except possibly around localized
antibiotic sources, where resistance can provide a strong advantage, bacterial
fitness is dominated by stresses unaffected by resistance to the antibiotic. How
do such mixed and heterogeneous conditions influence the selective advantage or
disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels
of tetracyclines potentiate selection for or against tetracycline resistance around
localized sources of almost any toxin or stress. Furthermore, certain stresses
generate alternating rings of selection for and against resistance around a localized
source of the antibiotic. In these conditions, localized antibiotic sources, even
at high strengths, can actually produce a net selection against resistance to
the antibiotic. Our results show that interactions between the effects of an antibiotic
and other stresses in inhomogeneous environments can generate pervasive, complex
patterns of selection both for and against antibiotic resistance.
acknowledgement: This work was partially supported by US National Institutes of Health
grant R01-GM081617, Israeli Centers of Research Excellence I-CORE Program ISF Grant
No. 152/11, and the European Research Council FP7 ERC Grant 281891.
article_number: '10333'
author:
- 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: Adam
full_name: Palmer, Adam
last_name: Palmer
- first_name: Idan
full_name: Yelin, Idan
last_name: Yelin
- first_name: Roy
full_name: Kishony, Roy
last_name: Kishony
citation:
ama: Chait RP, Palmer A, Yelin I, Kishony R. Pervasive selection for and against
antibiotic resistance in inhomogeneous multistress environments. Nature Communications.
2016;7. doi:10.1038/ncomms10333
apa: Chait, R. P., Palmer, A., Yelin, I., & Kishony, R. (2016). Pervasive selection
for and against antibiotic resistance in inhomogeneous multistress environments.
Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms10333
chicago: Chait, Remy P, Adam Palmer, Idan Yelin, and Roy Kishony. “Pervasive Selection
for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.”
Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms10333.
ieee: R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for
and against antibiotic resistance in inhomogeneous multistress environments,”
Nature Communications, vol. 7. Nature Publishing Group, 2016.
ista: Chait RP, Palmer A, Yelin I, Kishony R. 2016. Pervasive selection for and
against antibiotic resistance in inhomogeneous multistress environments. Nature
Communications. 7, 10333.
mla: Chait, Remy P., et al. “Pervasive Selection for and against Antibiotic Resistance
in Inhomogeneous Multistress Environments.” Nature Communications, vol.
7, 10333, Nature Publishing Group, 2016, doi:10.1038/ncomms10333.
short: R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016).
date_created: 2018-12-11T11:51:25Z
date_published: 2016-01-20T00:00:00Z
date_updated: 2021-01-12T06:49:57Z
day: '20'
ddc:
- '570'
- '579'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1038/ncomms10333
file:
- access_level: open_access
checksum: ef147bcbb8bd37e9079cf3ce06f5815d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:52Z
date_updated: 2020-07-14T12:44:44Z
file_id: '5039'
file_name: IST-2016-662-v1+1_ncomms10333.pdf
file_size: 1844107
relation: main_file
file_date_updated: 2020-07-14T12:44:44Z
has_accepted_license: '1'
intvolume: ' 7'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_status: published
publisher: Nature Publishing Group
publist_id: '5936'
pubrep_id: '662'
quality_controlled: '1'
scopus_import: 1
status: public
title: Pervasive selection for and against antibiotic resistance in inhomogeneous
multistress environments
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: 7
year: '2016'
...
---
_id: '1342'
abstract:
- lang: eng
text: A key aspect of bacterial survival is the ability to evolve while migrating
across spatially varying environmental challenges. Laboratory experiments, however,
often study evolution in well-mixed systems. Here, we introduce an experimental
device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria
spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that
allowed visual observation of mutation and selection in a migrating bacterial
front.While resistance increased consistently, multiple coexisting lineages diversified
both phenotypically and genotypically. Analyzing mutants at and behind the propagating
front,we found that evolution is not always led by the most resistant mutants;
highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate
provides a versatile platformfor studying microbial adaption and directly visualizing
evolutionary dynamics.
author:
- first_name: Michael
full_name: Baym, Michael
last_name: Baym
- first_name: Tami
full_name: Lieberman, Tami
last_name: Lieberman
- first_name: Eric
full_name: Kelsic, Eric
last_name: Kelsic
- 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: Rotem
full_name: Gross, Rotem
last_name: Gross
- first_name: Idan
full_name: Yelin, Idan
last_name: Yelin
- first_name: Roy
full_name: Kishony, Roy
last_name: Kishony
citation:
ama: Baym M, Lieberman T, Kelsic E, et al. Spatiotemporal microbial evolution on
antibiotic landscapes. Science. 2016;353(6304):1147-1151. doi:10.1126/science.aag0822
apa: Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., &
Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes.
Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aag0822
chicago: Baym, Michael, Tami Lieberman, Eric Kelsic, Remy P Chait, Rotem Gross,
Idan Yelin, and Roy Kishony. “Spatiotemporal Microbial Evolution on Antibiotic
Landscapes.” Science. American Association for the Advancement of Science,
2016. https://doi.org/10.1126/science.aag0822.
ieee: M. Baym et al., “Spatiotemporal microbial evolution on antibiotic landscapes,”
Science, vol. 353, no. 6304. American Association for the Advancement of
Science, pp. 1147–1151, 2016.
ista: Baym M, Lieberman T, Kelsic E, Chait RP, Gross R, Yelin I, Kishony R. 2016.
Spatiotemporal microbial evolution on antibiotic landscapes. Science. 353(6304),
1147–1151.
mla: Baym, Michael, et al. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.”
Science, vol. 353, no. 6304, American Association for the Advancement of
Science, 2016, pp. 1147–51, doi:10.1126/science.aag0822.
short: M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony,
Science 353 (2016) 1147–1151.
date_created: 2018-12-11T11:51:29Z
date_published: 2016-09-09T00:00:00Z
date_updated: 2021-01-12T06:50:01Z
day: '09'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1126/science.aag0822
intvolume: ' 353'
issue: '6304'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/
month: '09'
oa: 1
oa_version: Preprint
page: 1147 - 1151
publication: Science
publication_status: published
publisher: American Association for the Advancement of Science
publist_id: '5911'
quality_controlled: '1'
scopus_import: 1
status: public
title: Spatiotemporal microbial evolution on antibiotic landscapes
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 353
year: '2016'
...
---
_id: '1394'
abstract:
- lang: eng
text: "The solution space of genome-scale models of cellular metabolism provides
a map between physically\r\nviable flux configurations and cellular metabolic
phenotypes described, at the most basic level, by the\r\ncorresponding growth
rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat
empirical growth rate distributions recently obtained in experiments at single-cell
resolution can\r\nbe explained in terms of a trade-off between the higher fitness
of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based
on this, we propose a minimal model for the evolution of\r\na large bacterial
population that captures this trade-off. The scaling relationships observed in\r\nexperiments
encode, in such frameworks, for the same distance from the maximum achievable
growth\r\nrate, the same degree of growth rate maximization, and/or the same rate
of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions,
these results allow for multiple\r\nimplications and extensions in spite of the
underlying conceptual simplicity."
acknowledgement: "The research leading to these results has received funding from
the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038."
article_number: '036005'
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Fabrizio
full_name: Capuani, Fabrizio
last_name: Capuani
- first_name: Andrea
full_name: De Martino, Andrea
last_name: De Martino
citation:
ama: 'De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005'
apa: 'De Martino, D., Capuani, F., & De Martino, A. (2016). Growth against entropy
in bacterial metabolism: the phenotypic trade-off behind empirical growth rate
distributions in E. coli. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/3/036005'
chicago: 'De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth
against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical
Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing
Ltd., 2016. https://doi.org/10.1088/1478-3975/13/3/036005.'
ieee: 'D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in
bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.'
ista: 'De Martino D, Capuani F, De Martino A. 2016. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 13(3), 036005.'
mla: 'De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism:
The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.”
Physical Biology, vol. 13, no. 3, 036005, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/3/036005.'
short: D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).
date_created: 2018-12-11T11:51:46Z
date_published: 2016-05-27T00:00:00Z
date_updated: 2021-01-12T06:50:23Z
day: '27'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/3/036005
ec_funded: 1
intvolume: ' 13'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1601.03243
month: '05'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Biology
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '5815'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Growth against entropy in bacterial metabolism: the phenotypic trade-off behind
empirical growth rate distributions in E. coli'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2016'
...
---
_id: '1420'
abstract:
- lang: eng
text: 'Selection, mutation, and random drift affect the dynamics of allele frequencies
and consequently of quantitative traits. While the macroscopic dynamics of quantitative
traits can be measured, the underlying allele frequencies are typically unobserved.
Can we understand how the macroscopic observables evolve without following these
microscopic processes? This problem has been studied previously by analogy with
statistical mechanics: the allele frequency distribution at each time point is
approximated by the stationary form, which maximizes entropy. We explore the limitations
of this method when mutation is small (4Nμ < 1) so that populations are typically
close to fixation, and we extend the theory in this regime to account for changes
in mutation strength. We consider a single diallelic locus either under directional
selection or with overdominance and then generalize to multiple unlinked biallelic
loci with unequal effects. We find that the maximum-entropy approximation is remarkably
accurate, even when mutation and selection change rapidly. '
article_processing_charge: No
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of
quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127
apa: Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation
for the dynamics of quantitative traits. Genetics. Genetics Society of
America. https://doi.org/10.1534/genetics.115.184127
chicago: Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation
for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of
America, 2016. https://doi.org/10.1534/genetics.115.184127.
ieee: K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics
of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of
America, pp. 1523–1548, 2016.
ista: Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics
of quantitative traits. Genetics. 202(4), 1523–1548.
mla: Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative
Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016,
pp. 1523–48, doi:10.1534/genetics.115.184127.
short: K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.
date_created: 2018-12-11T11:51:55Z
date_published: 2016-04-06T00:00:00Z
date_updated: 2022-08-01T10:49:55Z
day: '06'
department:
- _id: GaTk
- _id: NiBa
doi: 10.1534/genetics.115.184127
ec_funded: 1
external_id:
arxiv:
- '1510.08344'
intvolume: ' 202'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1510.08344
month: '04'
oa: 1
oa_version: Preprint
page: 1523 - 1548
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '5787'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A general approximation for the dynamics of quantitative traits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2016'
...
---
_id: '1485'
abstract:
- lang: eng
text: In this article the notion of metabolic turnover is revisited in the light
of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo
methods we perform an exact sampling of the enzymatic fluxes in a genome scale
metabolic network of E. Coli in stationary growth conditions from which we infer
the metabolites turnover times. However the latter are inferred from net fluxes,
and we argue that this approximation is not valid for enzymes working nearby thermodynamic
equilibrium. We recalculate turnover times from total fluxes by performing an
energy balance analysis of the network and recurring to the fluctuation theorem.
We find in many cases values one of order of magnitude lower, implying a faster
picture of intermediate metabolism.
article_number: '016003'
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
citation:
ama: De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from
the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003
apa: De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E.
Coli from the energy balance analysis. Physical Biology. IOP Publishing
Ltd. https://doi.org/10.1088/1478-3975/13/1/016003
chicago: De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of
E. Coli from the Energy Balance Analysis.” Physical Biology. IOP Publishing
Ltd., 2016. https://doi.org/10.1088/1478-3975/13/1/016003.
ieee: D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli
from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP
Publishing Ltd., 2016.
ista: De Martino D. 2016. Genome-scale estimate of the metabolic turnover of E.
Coli from the energy balance analysis. Physical Biology. 13(1), 016003.
mla: De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E.
Coli from the Energy Balance Analysis.” Physical Biology, vol. 13, no.
1, 016003, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/1/016003.
short: D. De Martino, Physical Biology 13 (2016).
date_created: 2018-12-11T11:52:18Z
date_published: 2016-01-29T00:00:00Z
date_updated: 2021-01-12T06:51:04Z
day: '29'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/1/016003
ec_funded: 1
intvolume: ' 13'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1505.04613
month: '01'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Biology
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '5702'
quality_controlled: '1'
scopus_import: 1
status: public
title: Genome-scale estimate of the metabolic turnover of E. Coli from the energy
balance analysis
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2016'
...
---
_id: '1148'
abstract:
- lang: eng
text: Continuous-time Markov chain (CTMC) models have become a central tool for
understanding the dynamics of complex reaction networks and the importance of
stochasticity in the underlying biochemical processes. When such models are employed
to answer questions in applications, in order to ensure that the model provides
a sufficiently accurate representation of the real system, it is of vital importance
that the model parameters are inferred from real measured data. This, however,
is often a formidable task and all of the existing methods fail in one case or
the other, usually because the underlying CTMC model is high-dimensional and computationally
difficult to analyze. The parameter inference methods that tend to scale best
in the dimension of the CTMC are based on so-called moment closure approximations.
However, there exists a large number of different moment closure approximations
and it is typically hard to say a priori which of the approximations is the most
suitable for the inference procedure. Here, we propose a moment-based parameter
inference method that automatically chooses the most appropriate moment closure
method. Accordingly, contrary to existing methods, the user is not required to
be experienced in moment closure techniques. In addition to that, our method adaptively
changes the approximation during the parameter inference to ensure that always
the best approximation is used, even in cases where different approximations are
best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd
acknowledgement: This work is based on the CMSB 2015 paper “Adaptive moment closure
for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015).
The work was partly supported by the German Research Foundation (DFG) as part of
the Transregional Collaborative Research Center “Automatic Verification and Analysis
of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under
grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23
(RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People
Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme
(FP7/2007-2013) under REA grant agreement no. 291734.
author:
- first_name: Christian
full_name: Schilling, Christian
last_name: Schilling
- first_name: Sergiy
full_name: Bogomolov, Sergiy
id: 369D9A44-F248-11E8-B48F-1D18A9856A87
last_name: Bogomolov
orcid: 0000-0002-0686-0365
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Andreas
full_name: Podelski, Andreas
last_name: Podelski
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment
closure for parameter inference of biochemical reaction networks. Biosystems.
2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005
apa: Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess,
J. (2016). Adaptive moment closure for parameter inference of biochemical reaction
networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005
chicago: Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski,
and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical
Reaction Networks.” Biosystems. Elsevier, 2016. https://doi.org/10.1016/j.biosystems.2016.07.005.
ieee: C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive
moment closure for parameter inference of biochemical reaction networks,” Biosystems,
vol. 149. Elsevier, pp. 15–25, 2016.
ista: Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive
moment closure for parameter inference of biochemical reaction networks. Biosystems.
149, 15–25.
mla: Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference
of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016,
pp. 15–25, doi:10.1016/j.biosystems.2016.07.005.
short: C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems
149 (2016) 15–25.
date_created: 2018-12-11T11:50:24Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2023-02-23T10:08:46Z
day: '01'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1016/j.biosystems.2016.07.005
ec_funded: 1
intvolume: ' 149'
language:
- iso: eng
month: '11'
oa_version: None
page: 15 - 25
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: Biosystems
publication_status: published
publisher: Elsevier
publist_id: '6210'
quality_controlled: '1'
related_material:
record:
- id: '1658'
relation: earlier_version
status: public
scopus_import: 1
status: public
title: Adaptive moment closure for parameter inference of biochemical reaction networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 149
year: '2016'
...
---
_id: '8094'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. The idea is that the controller controls the world---the body plus
its environment---as reliably as possible. This paper focuses on new lines of
self-organization for developmental robotics. We apply the recently developed
differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
system from the Myorobotics toolkit. In the experiments, we observe a vast variety
of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
sequences of different poses. By applying physical forces, the system can be entrained
into definite motion patterns like wiping a table. Most interestingly, after attaching
an object, the controller gets in a functional resonance with the object''s internal
dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
driving an attached pendulum into a circular mode. When attached to the crank
of a wheel the neural system independently discovers how to rotate it. In this
way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Rafael
full_name: Hostettler, Rafael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial
Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized
control of an tendon driven arm by differential extrinsic plasticity. In Proceedings
of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico:
MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029'
chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings
of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.
ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
of an tendon driven arm by differential extrinsic plasticity,” in Proceedings
of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp.
142–143.
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
an tendon driven arm by differential extrinsic plasticity. Proceedings of the
Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on
the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference
2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial
Life Conference 2016, MIT Press, 2016, pp. 142–143.
conference:
end_date: 2016-07-08
location: Cancun, Mexico
name: 'ALIFE 2016: 15th International Conference on the Synthesis and Simulation
of Living Systems'
start_date: 2016-07-04
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T08:16:53Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
checksum: cff63e7a4b8ac466ba51a9c84153a940
content_type: application/pdf
creator: cziletti
date_created: 2020-07-06T12:59:09Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8096'
file_name: 2016_ProcALIFE_Martius.pdf
file_size: 678670
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the Artificial Life Conference 2016
publication_identifier:
isbn:
- '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: 1
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic 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: conference
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 28
year: '2016'
...
---
_id: '1197'
abstract:
- lang: eng
text: Across the nervous system, certain population spiking patterns are observed
far more frequently than others. A hypothesis about this structure is that these
collective activity patterns function as population codewords–collective modes–carrying
information distinct from that of any single cell. We investigate this phenomenon
in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop
a novel statistical model that decomposes the population response into modes;
it predicts the distribution of spiking activity in the ganglion cell population
with high accuracy. We found that the modes represent localized features of the
visual stimulus that are distinct from the features represented by single neurons.
Modes form clusters of activity states that are readily discriminated from one
another. When we repeated the same visual stimulus, we found that the same mode
was robustly elicited. These results suggest that retinal ganglion cells’ collective
signaling is endowed with a form of error-correcting code–a principle that may
hold in brain areas beyond retina.
acknowledgement: JSP was supported by a C.V. Starr Fellowship from the Starr Foundation
(http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation
(https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National
Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science
Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler
for helpful comments on the manuscript.
article_number: e1005855
author:
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Mark
full_name: Ioffe, Mark
last_name: Ioffe
- first_name: Adrianna
full_name: Loback, Adrianna
last_name: Loback
- 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: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes
of the retinal population code. PLoS Computational Biology. 2016;12(11).
doi:10.1371/journal.pcbi.1005148
apa: Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M.
(2016). Error-robust modes of the retinal population code. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148
chicago: Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik,
and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS
Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148.
ieee: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust
modes of the retinal population code,” PLoS Computational Biology, vol.
12, no. 11. Public Library of Science, 2016.
ista: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust
modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.
mla: Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.”
PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of
Science, 2016, doi:10.1371/journal.pcbi.1005148.
short: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational
Biology 12 (2016).
date_created: 2018-12-11T11:50:40Z
date_published: 2016-11-17T00:00:00Z
date_updated: 2023-02-23T14:05:40Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005148
file:
- access_level: open_access
checksum: 47b08cbd4dbf32b25ba161f5f4b262cc
content_type: application/pdf
creator: kschuh
date_created: 2019-01-25T10:35:00Z
date_updated: 2020-07-14T12:44:38Z
file_id: '5884'
file_name: 2016_PLOS_Prentice.pdf
file_size: 4492021
relation: main_file
file_date_updated: 2020-07-14T12:44:38Z
has_accepted_license: '1'
intvolume: ' 12'
issue: '11'
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: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '6153'
quality_controlled: '1'
related_material:
record:
- id: '9709'
relation: research_data
status: public
scopus_import: 1
status: public
title: Error-robust modes of the retinal population code
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: 12
year: '2016'
...
---
_id: '948'
abstract:
- lang: eng
text: Experience constantly shapes neural circuits through a variety of plasticity
mechanisms. While the functional roles of some plasticity mechanisms are well-understood,
it remains unclear how changes in neural excitability contribute to learning.
Here, we develop a normative interpretation of intrinsic plasticity (IP) as a
key component of unsupervised learning. We introduce a novel generative mixture
model that accounts for the class-specific statistics of stimulus intensities,
and we derive a neural circuit that learns the input classes and their intensities.
We will analytically show that inference and learning for our generative model
can be achieved by a neural circuit with intensity-sensitive neurons equipped
with a specific form of IP. Numerical experiments verify our analytical derivations
and show robust behavior for artificial and natural stimuli. Our results link
IP to non-trivial input statistics, in particular the statistics of stimulus intensities
for classes to which a neuron is sensitive. More generally, our work paves the
way toward new classification algorithms that are robust to intensity variations.
acknowledgement: DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1
(JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement
no. 291734 (CS)
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Travis
full_name: Monk, Travis
last_name: Monk
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Jörg
full_name: Lücke, Jörg
last_name: Lücke
citation:
ama: 'Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn
stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems;
2016:4285-4293.'
apa: 'Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic
plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented
at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural
Information Processing Systems.'
chicago: Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic
Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information
Processing Systems, 2016.
ieee: 'T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics,” presented at the NIPS: Neural Information
Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.'
ista: 'Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 29, 4285–4293.'
mla: Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus
Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016,
pp. 4285–93.
short: T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems,
2016, pp. 4285–4293.
conference:
end_date: 2016-12-10
location: Barcelona, Spaine
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:49:21Z
date_published: 2016-01-01T00:00:00Z
date_updated: 2021-01-12T08:22:08Z
day: '01'
department:
- _id: GaTk
ec_funded: 1
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- url: https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics
month: '01'
oa_version: None
page: 4285 - 4293
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6469'
quality_controlled: '1'
scopus_import: 1
status: public
title: Neurons equipped with intrinsic plasticity learn stimulus intensity statistics
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1270'
abstract:
- lang: eng
text: A crucial step in the early development of multicellular organisms involves
the establishment of spatial patterns of gene expression which later direct proliferating
cells to take on different cell fates. These patterns enable the cells to infer
their global position within a tissue or an organism by reading out local gene
expression levels. The patterning system is thus said to encode positional information,
a concept that was formalized recently in the framework of information theory.
Here we introduce a toy model of patterning in one spatial dimension, which can
be seen as an extension of Wolpert's paradigmatic "French Flag" model,
to patterning by several interacting, spatially coupled genes subject to intrinsic
and extrinsic noise. Our model, a variant of an Ising spin system, allows us to
systematically explore expression patterns that optimally encode positional information.
We find that optimal patterning systems use positional cues, as in the French
Flag model, together with gene-gene interactions to generate combinatorial codes
for position which we call "Counter" patterns. Counter patterns can
also be stabilized against noise and variations in system size or morphogen dosage
by longer-range spatial interactions of the type invoked in the Turing model.
The simple setup proposed here qualitatively captures many of the experimentally
observed properties of biological patterning systems and allows them to be studied
in a single, theoretically consistent framework.
acknowledgement: The authors would like to thank Thomas Sokolowski and Filipe Tostevin
for helpful discussions. PH and UG were funded by the German Excellence Initiative
via the program "Nanosystems Initiative Munich" (https://www.nano-initiative-munich.de)
and the German Research Foundation via the SFB 1032 "Nanoagents for Spatiotemporal
Control of Molecular and Cellular Reactions" (http://www.sfb1032.physik.uni-muenchen.de).
GT was funded by the Austrian Science Fund (FWF P 28844) (http://www.fwf.ac.at).
article_number: e0163628
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: 'Hillenbrand P, Gerland U, Tkačik G. Beyond the French flag model: Exploiting
spatial and gene regulatory interactions for positional information. PLoS One.
2016;11(9). doi:10.1371/journal.pone.0163628'
apa: 'Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Beyond the French flag
model: Exploiting spatial and gene regulatory interactions for positional information.
PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628'
chicago: 'Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Beyond the French
Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional
Information.” PLoS One. Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.'
ieee: 'P. Hillenbrand, U. Gerland, and G. Tkačik, “Beyond the French flag model:
Exploiting spatial and gene regulatory interactions for positional information,”
PLoS One, vol. 11, no. 9. Public Library of Science, 2016.'
ista: 'Hillenbrand P, Gerland U, Tkačik G. 2016. Beyond the French flag model: Exploiting
spatial and gene regulatory interactions for positional information. PLoS One.
11(9), e0163628.'
mla: 'Hillenbrand, Patrick, et al. “Beyond the French Flag Model: Exploiting Spatial
and Gene Regulatory Interactions for Positional Information.” PLoS One,
vol. 11, no. 9, e0163628, Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.'
short: P. Hillenbrand, U. Gerland, G. Tkačik, PLoS One 11 (2016).
date_created: 2018-12-11T11:51:03Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2023-02-23T14:11:37Z
day: '27'
ddc:
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628
file:
- access_level: open_access
checksum: 3d0d55d373096a033bd9cf79288c8586
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:10:47Z
date_updated: 2020-07-14T12:44:42Z
file_id: '4837'
file_name: IST-2016-696-v1+1_journal.pone.0163628.PDF
file_size: 4950415
relation: main_file
file_date_updated: 2020-07-14T12:44:42Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '6050'
pubrep_id: '696'
quality_controlled: '1'
related_material:
record:
- id: '9869'
relation: research_data
status: public
- id: '9870'
relation: research_data
status: public
- id: '9871'
relation: research_data
status: public
scopus_import: 1
status: public
title: 'Beyond the French flag model: Exploiting spatial and gene regulatory interactions
for positional information'
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: '2016'
...
---
_id: '9870'
abstract:
- lang: eng
text: The effect of noise in the input field on an Ising model is approximated.
Furthermore, methods to compute positional information in an Ising model by transfer
matrices and Monte Carlo sampling are outlined.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in an Ising model.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in an
Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:23:45Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s002
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in an Ising model
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9869'
abstract:
- lang: eng
text: A lower bound on the error of a positional estimator with limited positional
information is derived.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position.
2016. doi:10.1371/journal.pone.0163628.s001
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator
of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on
an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of
position.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position,
Public Library of Science, 10.1371/journal.pone.0163628.s001.
mla: Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position.
Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T08:53:48Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s001
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Error bound on an estimator of position
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9871'
abstract:
- lang: eng
text: The positional information in a discrete morphogen field with Gaussian noise
is computed.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in a Discrete Morphogen Field.” Public Library of Science,
2016. https://doi.org/10.1371/journal.pone.0163628.s003.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in a discrete morphogen field.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in a
Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:27:35Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s003
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in a discrete morphogen field
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '1128'
abstract:
- lang: eng
text: "The process of gene expression is central to the modern understanding of
how cellular systems\r\nfunction. In this process, a special kind of regulatory
proteins, called transcription factors,\r\nare important to determine how much
protein is produced from a given gene. As biological\r\ninformation is transmitted
from transcription factor concentration to mRNA levels to amounts of\r\nprotein,
various sources of noise arise and pose limits to the fidelity of intracellular
signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene
expression: (i) the mathematical\r\ndescription of complex promoters responsible
for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information
processing the cell faces due to the interference from multiple\r\nfluctuating
signals, (iii) how the presence of gene expression noise influences the evolution\r\nof
regulatory sequences, (iv) and tools for the experimental study of origins and
consequences\r\nof cell-cell heterogeneity, including an application to bacterial
stress response systems."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
citation:
ama: Rieckh G. Studying the complexities of transcriptional regulation. 2016.
apa: Rieckh, G. (2016). Studying the complexities of transcriptional regulation.
Institute of Science and Technology Austria.
chicago: Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.”
Institute of Science and Technology Austria, 2016.
ieee: G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute
of Science and Technology Austria, 2016.
ista: Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute
of Science and Technology Austria.
mla: Rieckh, Georg. Studying the Complexities of Transcriptional Regulation.
Institute of Science and Technology Austria, 2016.
short: G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute
of Science and Technology Austria, 2016.
date_created: 2018-12-11T11:50:18Z
date_published: 2016-08-01T00:00:00Z
date_updated: 2023-09-07T11:44:34Z
day: '01'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GaTk
file:
- access_level: closed
checksum: ec453918c3bf8e6f460fd1156ef7b493
content_type: application/pdf
creator: dernst
date_created: 2019-08-13T11:46:25Z
date_updated: 2019-08-13T11:46:25Z
file_id: '6815'
file_name: Thesis_Georg_Rieckh_w_signature_page.pdf
file_size: 2614660
relation: main_file
- access_level: open_access
checksum: 51ae398166370d18fd22478b6365c4da
content_type: application/pdf
creator: dernst
date_created: 2020-09-21T11:30:40Z
date_updated: 2020-09-21T11:30:40Z
file_id: '8542'
file_name: Thesis_Georg_Rieckh.pdf
file_size: 6096178
relation: main_file
success: 1
file_date_updated: 2020-09-21T11:30:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: '114'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6232'
status: public
supervisor:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
title: Studying the complexities of transcriptional regulation
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '1358'
abstract:
- lang: eng
text: 'Gene regulation relies on the specificity of transcription factor (TF)–DNA
interactions. Limited specificity may lead to crosstalk: a regulatory state in
which a gene is either incorrectly activated due to noncognate TF–DNA interactions
or remains erroneously inactive. As each TF can have numerous interactions with
noncognate cis-regulatory elements, crosstalk is inherently a global problem,
yet has previously not been studied as such. We construct a theoretical framework
to analyse the effects of global crosstalk on gene regulation. We find that crosstalk
presents a significant challenge for organisms with low-specificity TFs, such
as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting
at equilibrium, including variants of cooperativity and combinatorial regulation.
Our results suggest that crosstalk imposes a previously unexplored global constraint
on the functioning and evolution of regulatory networks, which is qualitatively
distinct from the known constraints that act at the level of individual gene regulatory
elements.'
article_number: '12307'
author:
- first_name: Tamar
full_name: Friedlander, Tamar
id: 36A5845C-F248-11E8-B48F-1D18A9856A87
last_name: Friedlander
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- 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: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to
gene regulation by global crosstalk. Nature Communications. 2016;7. doi:10.1038/ncomms12307
apa: Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., & Tkačik, G. (2016).
Intrinsic limits to gene regulation by global crosstalk. Nature Communications.
Nature Publishing Group. https://doi.org/10.1038/ncomms12307
chicago: Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and
Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature
Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms12307.
ieee: T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkačik, “Intrinsic
limits to gene regulation by global crosstalk,” Nature Communications,
vol. 7. Nature Publishing Group, 2016.
ista: Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits
to gene regulation by global crosstalk. Nature Communications. 7, 12307.
mla: Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.”
Nature Communications, vol. 7, 12307, Nature Publishing Group, 2016, doi:10.1038/ncomms12307.
short: T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications
7 (2016).
date_created: 2018-12-11T11:51:34Z
date_published: 2016-08-04T00:00:00Z
date_updated: 2023-09-07T12:53:49Z
day: '04'
ddc:
- '576'
department:
- _id: GaTk
- _id: NiBa
- _id: CaGu
doi: 10.1038/ncomms12307
ec_funded: 1
file:
- access_level: open_access
checksum: fe3f3a1526d180b29fe691ab11435b78
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:01Z
date_updated: 2020-07-14T12:44:46Z
file_id: '4919'
file_name: IST-2016-627-v1+1_ncomms12307.pdf
file_size: 861805
relation: main_file
- access_level: open_access
checksum: 164864a1a675f3ad80e9917c27aba07f
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:02Z
date_updated: 2020-07-14T12:44:46Z
file_id: '4920'
file_name: IST-2016-627-v1+2_ncomms12307-s1.pdf
file_size: 1084703
relation: main_file
file_date_updated: 2020-07-14T12:44:46Z
has_accepted_license: '1'
intvolume: ' 7'
language:
- iso: eng
month: '08'
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
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Nature Communications
publication_status: published
publisher: Nature Publishing Group
publist_id: '5887'
pubrep_id: '627'
quality_controlled: '1'
related_material:
record:
- id: '6071'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Intrinsic limits to gene regulation by global crosstalk
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: 7
year: '2016'
...
---
_id: '10794'
abstract:
- lang: eng
text: Mathematical models are of fundamental importance in the understanding of
complex population dynamics. For instance, they can be used to predict the population
evolution starting from different initial conditions or to test how a system responds
to external perturbations. For this analysis to be meaningful in real applications,
however, it is of paramount importance to choose an appropriate model structure
and to infer the model parameters from measured data. While many parameter inference
methods are available for models based on deterministic ordinary differential
equations, the same does not hold for more detailed individual-based models. Here
we consider, in particular, stochastic models in which the time evolution of the
species abundances is described by a continuous-time Markov chain. These models
are governed by a master equation that is typically difficult to solve. Consequently,
traditional inference methods that rely on iterative evaluation of parameter likelihoods
are computationally intractable. The aim of this paper is to present recent advances
in parameter inference for continuous-time Markov chain models, based on a moment
closure approximation of the parameter likelihood, and to investigate how these
results can help in understanding, and ultimately controlling, complex systems
in ecology. Specifically, we illustrate through an agricultural pest case study
how parameters of a stochastic individual-based model can be identified from measured
data and how the resulting model can be used to solve an optimal control problem
in a stochastic setting. In particular, we show how the matter of determining
the optimal combination of two different pest control methods can be formulated
as a chance constrained optimization problem where the control action is modeled
as a state reset, leading to a hybrid system formulation.
acknowledgement: "The authors would like to acknowledge contributions from Baptiste
Mottet who performed preliminary analysis regarding parameter inference for the
considered case study in a student project (Mottet, 2014/2015).\r\nThe research
leading to these results has received funding from the People Programme (Marie Curie
Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under
REA grant agreement No. [291734] and from SystemsX under the project SignalX."
article_number: '42'
article_processing_charge: No
article_type: original
author:
- first_name: Francesca
full_name: Parise, Francesca
last_name: Parise
- first_name: John
full_name: Lygeros, John
last_name: Lygeros
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: 'Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based
models of ecological systems: a pest control simulation study. Frontiers in
Environmental Science. 2015;3. doi:10.3389/fenvs.2015.00042'
apa: 'Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic
individual-based models of ecological systems: a pest control simulation study.
Frontiers in Environmental Science. Frontiers. https://doi.org/10.3389/fenvs.2015.00042'
chicago: 'Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference
for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation
Study.” Frontiers in Environmental Science. Frontiers, 2015. https://doi.org/10.3389/fenvs.2015.00042.'
ieee: 'F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based
models of ecological systems: a pest control simulation study,” Frontiers in
Environmental Science, vol. 3. Frontiers, 2015.'
ista: 'Parise F, Lygeros J, Ruess J. 2015. Bayesian inference for stochastic individual-based
models of ecological systems: a pest control simulation study. Frontiers in Environmental
Science. 3, 42.'
mla: 'Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based
Models of Ecological Systems: A Pest Control Simulation Study.” Frontiers in
Environmental Science, vol. 3, 42, Frontiers, 2015, doi:10.3389/fenvs.2015.00042.'
short: F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).
date_created: 2022-02-25T11:42:25Z
date_published: 2015-06-10T00:00:00Z
date_updated: 2022-02-25T11:59:23Z
day: '10'
ddc:
- '000'
- '570'
department:
- _id: ToHe
- _id: GaTk
doi: 10.3389/fenvs.2015.00042
ec_funded: 1
file:
- access_level: open_access
checksum: 26c222487564e1be02a11d688d6f769d
content_type: application/pdf
creator: dernst
date_created: 2022-02-25T11:55:26Z
date_updated: 2022-02-25T11:55:26Z
file_id: '10795'
file_name: 2015_FrontiersEnvironmScience_Parise.pdf
file_size: 1371201
relation: main_file
success: 1
file_date_updated: 2022-02-25T11:55:26Z
has_accepted_license: '1'
intvolume: ' 3'
keyword:
- General Environmental Science
language:
- iso: eng
month: '06'
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 Environmental Science
publication_identifier:
issn:
- 2296-665X
publication_status: published
publisher: Frontiers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Bayesian inference for stochastic individual-based models of ecological systems:
a pest control simulation study'
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: 3
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: '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: '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: '1658'
abstract:
- lang: eng
text: Continuous-time Markov chain (CTMC) models have become a central tool for
understanding the dynamics of complex reaction networks and the importance of
stochasticity in the underlying biochemical processes. When such models are employed
to answer questions in applications, in order to ensure that the model provides
a sufficiently accurate representation of the real system, it is of vital importance
that the model parameters are inferred from real measured data. This, however,
is often a formidable task and all of the existing methods fail in one case or
the other, usually because the underlying CTMC model is high-dimensional and computationally
difficult to analyze. The parameter inference methods that tend to scale best
in the dimension of the CTMC are based on so-called moment closure approximations.
However, there exists a large number of different moment closure approximations
and it is typically hard to say a priori which of the approximations is the most
suitable for the inference procedure. Here, we propose a moment-based parameter
inference method that automatically chooses the most appropriate moment closure
method. Accordingly, contrary to existing methods, the user is not required to
be experienced in moment closure techniques. In addition to that, our method adaptively
changes the approximation during the parameter inference to ensure that always
the best approximation is used, even in cases where different approximations are
best in different regions of the parameter space.
alternative_title:
- LNCS
author:
- first_name: Sergiy
full_name: Bogomolov, Sergiy
id: 369D9A44-F248-11E8-B48F-1D18A9856A87
last_name: Bogomolov
orcid: 0000-0002-0686-0365
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Andreas
full_name: Podelski, Andreas
last_name: Podelski
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Christian
full_name: Schilling, Christian
last_name: Schilling
citation:
ama: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment
closure for parameter inference of biochemical reaction networks. 2015;9308:77-89.
doi:10.1007/978-3-319-23401-4_8
apa: 'Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling,
C. (2015). Adaptive moment closure for parameter inference of biochemical reaction
networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes,
France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8'
chicago: Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and
Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical
Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8.
ieee: S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive
moment closure for parameter inference of biochemical reaction networks,” vol.
9308. Springer, pp. 77–89, 2015.
ista: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive
moment closure for parameter inference of biochemical reaction networks. 9308,
77–89.
mla: Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference
of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8.
short: S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015)
77–89.
conference:
end_date: 2015-09-18
location: Nantes, France
name: 'CMSB: Computational Methods in Systems Biology'
start_date: 2015-09-16
date_created: 2018-12-11T11:53:18Z
date_published: 2015-09-01T00:00:00Z
date_updated: 2023-02-21T16:17:24Z
day: '01'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1007/978-3-319-23401-4_8
ec_funded: 1
intvolume: ' 9308'
language:
- iso: eng
month: '09'
oa_version: None
page: 77 - 89
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Springer
publist_id: '5492'
quality_controlled: '1'
related_material:
record:
- id: '1148'
relation: later_version
status: public
scopus_import: 1
series_title: Lecture Notes in Computer Science
status: public
title: Adaptive moment closure for parameter inference of biochemical reaction networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9308
year: '2015'
...
---
_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: '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: '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: '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: '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: '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: 2023-02-23T10:09:08Z
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: '1666'
abstract:
- lang: eng
text: Evolution of gene regulation is crucial for our understanding of the phenotypic
differences between species, populations and individuals. Sequence-specific binding
of transcription factors to the regulatory regions on the DNA is a key regulatory
mechanism that determines gene expression and hence heritable phenotypic variation.
We use a biophysical model for directional selection on gene expression to estimate
the rates of gain and loss of transcription factor binding sites (TFBS) in finite
populations under both point and insertion/deletion mutations. Our results show
that these rates are typically slow for a single TFBS in an isolated DNA region,
unless the selection is extremely strong. These rates decrease drastically with
increasing TFBS length or increasingly specific protein-DNA interactions, making
the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation
timescales. Similarly, evolution converges to the stationary distribution of binding
sequences very slowly, making the equilibrium assumption questionable. The availability
of longer regulatory sequences in which multiple binding sites can evolve simultaneously,
the presence of “pre-sites” or partially decayed old sites in the initial sequence,
and biophysical cooperativity between transcription factors, can all facilitate
gain of TFBS and reconcile theoretical calculations with timescales inferred from
comparative genomics.
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: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Tugrul M, Paixao T, Barton NH, Tkačik G. Dynamics of transcription factor binding
site evolution. PLoS Genetics. 2015;11(11). doi:10.1371/journal.pgen.1005639
apa: Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Dynamics of
transcription factor binding site evolution. PLoS Genetics. Public Library
of Science. https://doi.org/10.1371/journal.pgen.1005639
chicago: Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Dynamics
of Transcription Factor Binding Site Evolution.” PLoS Genetics. Public
Library of Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.
ieee: M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Dynamics of transcription
factor binding site evolution,” PLoS Genetics, vol. 11, no. 11. Public
Library of Science, 2015.
ista: Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Dynamics of transcription factor
binding site evolution. PLoS Genetics. 11(11).
mla: Tugrul, Murat, et al. “Dynamics of Transcription Factor Binding Site Evolution.”
PLoS Genetics, vol. 11, no. 11, Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.
short: M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, PLoS Genetics 11 (2015).
date_created: 2018-12-11T11:53:21Z
date_published: 2015-11-06T00:00:00Z
date_updated: 2023-09-07T11:53:49Z
day: '06'
ddc:
- '576'
department:
- _id: NiBa
- _id: CaGu
- _id: GaTk
doi: 10.1371/journal.pgen.1005639
ec_funded: 1
file:
- access_level: open_access
checksum: a4e72fca5ccf40ddacf4d08c8e46b554
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:07:58Z
date_updated: 2020-07-14T12:45:10Z
file_id: '4657'
file_name: IST-2016-463-v1+1_journal.pgen.1005639.pdf
file_size: 2580778
relation: main_file
file_date_updated: 2020-07-14T12:45:10Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: PLoS Genetics
publication_status: published
publisher: Public Library of Science
publist_id: '5483'
pubrep_id: '463'
quality_controlled: '1'
related_material:
record:
- id: '9712'
relation: research_data
status: public
- id: '1131'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Dynamics of transcription factor binding site evolution
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
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: '1655'
abstract:
- lang: eng
text: Quantifying behaviors of robots which were generated autonomously from task-independent
objective functions is an important prerequisite for objective comparisons of
algorithms and movements of animals. The temporal sequence of such a behavior
can be considered as a time series and hence complexity measures developed for
time series are natural candidates for its quantification. The predictive information
and the excess entropy are such complexity measures. They measure the amount of
information the past contains about the future and thus quantify the nonrandom
structure in the temporal sequence. However, when using these measures for systems
with continuous states one has to deal with the fact that their values will depend
on the resolution with which the systems states are observed. For deterministic
systems both measures will diverge with increasing resolution. We therefore propose
a new decomposition of the excess entropy in resolution dependent and resolution
independent parts and discuss how they depend on the dimensionality of the dynamics,
correlations and the noise level. For the practical estimation we propose to use
estimates based on the correlation integral instead of the direct estimation of
the mutual information based on next neighbor statistics because the latter allows
less control of the scale dependencies. Using our algorithm we are able to show
how autonomous learning generates behavior of increasing complexity with increasing
learning duration.
acknowledgement: This work was supported by the DFG priority program 1527 (Autonomous
Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013)
under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie
Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013)
under REA grant agreement no. 291734.
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Eckehard
full_name: Olbrich, Eckehard
last_name: Olbrich
citation:
ama: Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots.
Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266
apa: Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of
autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266
chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior
of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266.
ieee: G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous
robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
ista: Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots.
Entropy. 17(10), 7266–7297.
mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of
Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97,
doi:10.3390/e17107266.
short: G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.
date_created: 2018-12-11T11:53:17Z
date_published: 2015-10-23T00:00:00Z
date_updated: 2023-10-17T11:42:00Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3390/e17107266
ec_funded: 1
file:
- access_level: open_access
checksum: 945d99631a96e0315acb26dc8541dcf9
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:25Z
date_updated: 2020-07-14T12:45:08Z
file_id: '4943'
file_name: IST-2016-464-v1+1_entropy-17-07266.pdf
file_size: 6455007
relation: main_file
file_date_updated: 2020-07-14T12:45:08Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '10'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 7266 - 7297
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Entropy
publication_status: published
publisher: MDPI
publist_id: '5495'
pubrep_id: '464'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying emergent behavior of autonomous robots
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: '2015'
...
---
_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'
...
---
_id: '1931'
abstract:
- lang: eng
text: A wealth of experimental evidence suggests that working memory circuits preferentially
represent information that is behaviorally relevant. Still, we are missing a mechanistic
account of how these representations come about. Here we provide a simple explanation
for a range of experimental findings, in light of prefrontal circuits adapting
to task constraints by reward-dependent learning. In particular, we model a neural
network shaped by reward-modulated spike-timing dependent plasticity (r-STDP)
and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show
that the experimentally-observed neural representations naturally emerge in an
initially unstructured circuit as it learns to solve several working memory tasks.
These results point to a critical, and previously unappreciated, role for reward-dependent
learning in shaping prefrontal cortex activity.
acknowledgement: Supported in part by EC MEXT project PLICON and the LOEWE-Program
“Neuronal Coordination Research Focus Frankfurt” (NeFF). Jochen Triesch was supported
by the Quandt foundation.
article_number: '57'
author:
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Jochen
full_name: Triesch, Jochen
last_name: Triesch
citation:
ama: Savin C, Triesch J. Emergence of task-dependent representations in working
memory circuits. Frontiers in Computational Neuroscience. 2014;8(MAY).
doi:10.3389/fncom.2014.00057
apa: Savin, C., & Triesch, J. (2014). Emergence of task-dependent representations
in working memory circuits. Frontiers in Computational Neuroscience. Frontiers
Research Foundation. https://doi.org/10.3389/fncom.2014.00057
chicago: Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations
in Working Memory Circuits.” Frontiers in Computational Neuroscience. Frontiers
Research Foundation, 2014. https://doi.org/10.3389/fncom.2014.00057.
ieee: C. Savin and J. Triesch, “Emergence of task-dependent representations in working
memory circuits,” Frontiers in Computational Neuroscience, vol. 8, no.
MAY. Frontiers Research Foundation, 2014.
ista: Savin C, Triesch J. 2014. Emergence of task-dependent representations in working
memory circuits. Frontiers in Computational Neuroscience. 8(MAY), 57.
mla: Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations
in Working Memory Circuits.” Frontiers in Computational Neuroscience, vol.
8, no. MAY, 57, Frontiers Research Foundation, 2014, doi:10.3389/fncom.2014.00057.
short: C. Savin, J. Triesch, Frontiers in Computational Neuroscience 8 (2014).
date_created: 2018-12-11T11:54:46Z
date_published: 2014-05-28T00:00:00Z
date_updated: 2021-01-12T06:54:09Z
day: '28'
department:
- _id: GaTk
doi: 10.3389/fncom.2014.00057
intvolume: ' 8'
issue: MAY
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035833/
month: '05'
oa: 1
oa_version: Submitted Version
publication: Frontiers in Computational Neuroscience
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '5163'
quality_controlled: '1'
scopus_import: 1
status: public
title: Emergence of task-dependent representations in working memory circuits
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2014'
...
---
_id: '2028'
abstract:
- lang: eng
text: 'Understanding the dynamics of noisy neurons remains an important challenge
in neuroscience. Here, we describe a simple probabilistic model that accurately
describes the firing behavior in a large class (type II) of neurons. To demonstrate
the usefulness of this model, we show how it accurately predicts the interspike
interval (ISI) distributions, bursting patterns and mean firing rates found by:
(1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental
data from squid giant axon with a noisy input current and (3) experimental data
on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple
model has 6 parameters, however, in some cases, two of these parameters are coupled
and only 5 parameters account for much of the known behavior. From these parameters,
many properties of spiking can be found through simple calculation. Thus, we show
how the complex effects of noise can be understood through a simple and general
probabilistic model.'
acknowledgement: 'This work is supported by AFOSR grant FA 9550-11-1-0165, program
grant RPG 24/2012 from the Human Frontiers of Science (DBF) and travel support from
the European Commission Marie Curie International Reintegration Grant PIRG04-GA-2008-239429
(KB). DP was supported by NIHR01 GM104987 and the Wyss Institute of Biologically
Inspired Engineering. '
article_processing_charge: No
author:
- first_name: Katarina
full_name: Bodova, Katarina
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bodova
orcid: 0000-0002-7214-0171
- first_name: David
full_name: Paydarfar, David
last_name: Paydarfar
- first_name: Daniel
full_name: Forger, Daniel
last_name: Forger
citation:
ama: Bodova K, Paydarfar D, Forger D. Characterizing spiking in noisy type II neurons.
Journal of Theoretical Biology. 2014;365:40-54. doi:10.1016/j.jtbi.2014.09.041
apa: Bodova, K., Paydarfar, D., & Forger, D. (2014). Characterizing spiking
in noisy type II neurons. Journal of Theoretical Biology. Academic Press.
https://doi.org/10.1016/j.jtbi.2014.09.041
chicago: Bodova, Katarina, David Paydarfar, and Daniel Forger. “Characterizing Spiking
in Noisy Type II Neurons.” Journal of Theoretical Biology. Academic Press,
2014. https://doi.org/10.1016/j.jtbi.2014.09.041.
ieee: K. Bodova, D. Paydarfar, and D. Forger, “Characterizing spiking in noisy type
II neurons,” Journal of Theoretical Biology, vol. 365. Academic Press,
pp. 40–54, 2014.
ista: Bodova K, Paydarfar D, Forger D. 2014. Characterizing spiking in noisy type
II neurons. Journal of Theoretical Biology. 365, 40–54.
mla: Bodova, Katarina, et al. “Characterizing Spiking in Noisy Type II Neurons.”
Journal of Theoretical Biology, vol. 365, Academic Press, 2014, pp. 40–54,
doi:10.1016/j.jtbi.2014.09.041.
short: K. Bodova, D. Paydarfar, D. Forger, Journal of Theoretical Biology 365 (2014)
40–54.
date_created: 2018-12-11T11:55:18Z
date_published: 2014-10-12T00:00:00Z
date_updated: 2022-08-25T14:00:47Z
day: '12'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1016/j.jtbi.2014.09.041
file:
- access_level: open_access
checksum: a9dbae18d3233b3dab6944fd3f2cd49e
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:17:58Z
date_updated: 2020-07-14T12:45:25Z
file_id: '5316'
file_name: IST-2016-444-v1+1_1-s2.0-S0022519314005888-main.pdf
file_size: 2679222
relation: main_file
file_date_updated: 2020-07-14T12:45:25Z
has_accepted_license: '1'
intvolume: ' 365'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '10'
oa: 1
oa_version: Published Version
page: 40 - 54
publication: ' Journal of Theoretical Biology'
publication_status: published
publisher: Academic Press
publist_id: '5043'
pubrep_id: '444'
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1016/j.jtbi.2015.03.013
scopus_import: '1'
status: public
title: Characterizing spiking in noisy type II neurons
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 365
year: '2014'
...
---
_id: '2183'
abstract:
- lang: eng
text: 'We describe a simple adaptive network of coupled chaotic maps. The network
reaches a stationary state (frozen topology) for all values of the coupling parameter,
although the dynamics of the maps at the nodes of the network can be nontrivial.
The structure of the network shows interesting hierarchical properties and in
certain parameter regions the dynamics is polysynchronous: Nodes can be divided
in differently synchronized classes but, contrary to cluster synchronization,
nodes in the same class need not be connected to each other. These complicated
synchrony patterns have been conjectured to play roles in systems biology and
circuits. The adaptive system we study describes ways whereby this behavior can
evolve from undifferentiated nodes.'
acknowledgement: "V.B.S. is partially supported by contract MEC (Grant No. AYA2010-22111-C03-02).\r\n"
article_number: '062809'
article_processing_charge: No
author:
- 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: Paul
full_name: Glendinning, Paul
last_name: Glendinning
citation:
ama: Botella Soler V, Glendinning P. Hierarchy and polysynchrony in an adaptive
network . Physical Review E Statistical Nonlinear and Soft Matter Physics.
2014;89(6). doi:10.1103/PhysRevE.89.062809
apa: Botella Soler, V., & Glendinning, P. (2014). Hierarchy and polysynchrony
in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter
Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.89.062809
chicago: Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony
in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft
Matter Physics. American Institute of Physics, 2014. https://doi.org/10.1103/PhysRevE.89.062809.
ieee: V. Botella Soler and P. Glendinning, “Hierarchy and polysynchrony in an adaptive
network ,” Physical Review E Statistical Nonlinear and Soft Matter Physics,
vol. 89, no. 6. American Institute of Physics, 2014.
ista: Botella Soler V, Glendinning P. 2014. Hierarchy and polysynchrony in an adaptive
network . Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(6),
062809.
mla: Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony
in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft
Matter Physics, vol. 89, no. 6, 062809, American Institute of Physics, 2014,
doi:10.1103/PhysRevE.89.062809.
short: V. Botella Soler, P. Glendinning, Physical Review E Statistical Nonlinear
and Soft Matter Physics 89 (2014).
date_created: 2018-12-11T11:56:11Z
date_published: 2014-06-16T00:00:00Z
date_updated: 2022-08-25T14:04:45Z
day: '16'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.89.062809
ec_funded: 1
intvolume: ' 89'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1403.3209
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '4798'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Hierarchy and polysynchrony in an adaptive network '
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2014'
...
---
_id: '2231'
abstract:
- lang: eng
text: Based on the measurements of noise in gene expression performed during the
past decade, it has become customary to think of gene regulation in terms of a
two-state model, where the promoter of a gene can stochastically switch between
an ON and an OFF state. As experiments are becoming increasingly precise and the
deviations from the two-state model start to be observable, we ask about the experimental
signatures of complex multistate promoters, as well as the functional consequences
of this additional complexity. In detail, we i), extend the calculations for noise
in gene expression to promoters described by state transition diagrams with multiple
states, ii), systematically compute the experimentally accessible noise characteristics
for these complex promoters, and iii), use information theory to evaluate the
channel capacities of complex promoter architectures and compare them with the
baseline provided by the two-state model. We find that adding internal states
to the promoter generically decreases channel capacity, except in certain cases,
three of which (cooperativity, dual-role regulation, promoter cycling) we analyze
in detail.
author:
- 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: Rieckh G, Tkačik G. Noise and information transmission in promoters with multiple
internal states. Biophysical Journal. 2014;106(5):1194-1204. doi:10.1016/j.bpj.2014.01.014
apa: Rieckh, G., & Tkačik, G. (2014). Noise and information transmission in
promoters with multiple internal states. Biophysical Journal. Biophysical
Society. https://doi.org/10.1016/j.bpj.2014.01.014
chicago: Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in
Promoters with Multiple Internal States.” Biophysical Journal. Biophysical
Society, 2014. https://doi.org/10.1016/j.bpj.2014.01.014.
ieee: G. Rieckh and G. Tkačik, “Noise and information transmission in promoters
with multiple internal states,” Biophysical Journal, vol. 106, no. 5. Biophysical
Society, pp. 1194–1204, 2014.
ista: Rieckh G, Tkačik G. 2014. Noise and information transmission in promoters
with multiple internal states. Biophysical Journal. 106(5), 1194–1204.
mla: Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters
with Multiple Internal States.” Biophysical Journal, vol. 106, no. 5, Biophysical
Society, 2014, pp. 1194–204, doi:10.1016/j.bpj.2014.01.014.
short: G. Rieckh, G. Tkačik, Biophysical Journal 106 (2014) 1194–1204.
date_created: 2018-12-11T11:56:28Z
date_published: 2014-03-04T00:00:00Z
date_updated: 2021-01-12T06:56:10Z
day: '04'
department:
- _id: GaTk
doi: 10.1016/j.bpj.2014.01.014
external_id:
pmid:
- '24606943'
intvolume: ' 106'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4026790/
month: '03'
oa: 1
oa_version: Submitted Version
page: 1194 - 1204
pmid: 1
publication: Biophysical Journal
publication_identifier:
issn:
- '00063495'
publication_status: published
publisher: Biophysical Society
publist_id: '4730'
quality_controlled: '1'
scopus_import: 1
status: public
title: Noise and information transmission in promoters with multiple internal states
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 106
year: '2014'
...
---
_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: '537'
abstract:
- lang: eng
text: Transgenerational effects are broader than only parental relationships. Despite
mounting evidence that multigenerational effects alter phenotypic and life-history
traits, our understanding of how they combine to determine fitness is not well
developed because of the added complexity necessary to study them. Here, we derive
a quantitative genetic model of adaptation to an extraordinary new environment
by an additive genetic component, phenotypic plasticity, maternal and grandmaternal
effects. We show how, at equilibrium, negative maternal and negative grandmaternal
effects maximize expected population mean fitness. We define negative transgenerational
effects as those that have a negative effect on trait expression in the subsequent
generation, that is, they slow, or potentially reverse, the expected evolutionary
dynamic. When maternal effects are positive, negative grandmaternal effects are
preferred. As expected under Mendelian inheritance, the grandmaternal effects
have a lower impact on fitness than the maternal effects, but this dual inheritance
model predicts a more complex relationship between maternal and grandmaternal
effects to constrain phenotypic variance and so maximize expected population mean
fitness in the offspring.
author:
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Thomas
full_name: Ezard, Thomas
last_name: Ezard
- first_name: Rebecca
full_name: Hoyle, Rebecca
last_name: Hoyle
citation:
ama: Prizak R, Ezard T, Hoyle R. Fitness consequences of maternal and grandmaternal
effects. Ecology and Evolution. 2014;4(15):3139-3145. doi:10.1002/ece3.1150
apa: Prizak, R., Ezard, T., & Hoyle, R. (2014). Fitness consequences of maternal
and grandmaternal effects. Ecology and Evolution. Wiley-Blackwell. https://doi.org/10.1002/ece3.1150
chicago: Prizak, Roshan, Thomas Ezard, and Rebecca Hoyle. “Fitness Consequences
of Maternal and Grandmaternal Effects.” Ecology and Evolution. Wiley-Blackwell,
2014. https://doi.org/10.1002/ece3.1150.
ieee: R. Prizak, T. Ezard, and R. Hoyle, “Fitness consequences of maternal and grandmaternal
effects,” Ecology and Evolution, vol. 4, no. 15. Wiley-Blackwell, pp. 3139–3145,
2014.
ista: Prizak R, Ezard T, Hoyle R. 2014. Fitness consequences of maternal and grandmaternal
effects. Ecology and Evolution. 4(15), 3139–3145.
mla: Prizak, Roshan, et al. “Fitness Consequences of Maternal and Grandmaternal
Effects.” Ecology and Evolution, vol. 4, no. 15, Wiley-Blackwell, 2014,
pp. 3139–45, doi:10.1002/ece3.1150.
short: R. Prizak, T. Ezard, R. Hoyle, Ecology and Evolution 4 (2014) 3139–3145.
date_created: 2018-12-11T11:47:02Z
date_published: 2014-07-19T00:00:00Z
date_updated: 2021-01-12T08:01:30Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1002/ece3.1150
file:
- access_level: open_access
checksum: e32abf75a248e7a11811fd7f60858769
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:11:31Z
date_updated: 2020-07-14T12:46:38Z
file_id: '4886'
file_name: IST-2018-934-v1+1_Prizak_et_al-2014-Ecology_and_Evolution.pdf
file_size: 621582
relation: main_file
file_date_updated: 2020-07-14T12:46:38Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '15'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 3139 - 3145
publication: Ecology and Evolution
publication_status: published
publisher: Wiley-Blackwell
publist_id: '7280'
pubrep_id: '934'
scopus_import: 1
status: public
title: Fitness consequences of maternal and grandmaternal 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2014'
...
---
_id: '9752'
abstract:
- lang: eng
text: Redundancies and correlations in the responses of sensory neurons may seem
to waste neural resources, but they can also carry cues about structured stimuli
and may help the brain to correct for response errors. To investigate the effect
of stimulus structure on redundancy in retina, we measured simultaneous responses
from populations of retinal ganglion cells presented with natural and artificial
stimuli that varied greatly in correlation structure; these stimuli and recordings
are publicly available online. Responding to spatio-temporally structured stimuli
such as natural movies, pairs of ganglion cells were modestly more correlated
than in response to white noise checkerboards, but they were much less correlated
than predicted by a non-adapting functional model of retinal response. Meanwhile,
responding to stimuli with purely spatial correlations, pairs of ganglion cells
showed increased correlations consistent with a static, non-adapting receptive
field and nonlinearity. We found that in response to spatio-temporally correlated
stimuli, ganglion cells had faster temporal kernels and tended to have stronger
surrounds. These properties of individual cells, along with gain changes that
opposed changes in effective contrast at the ganglion cell input, largely explained
the pattern of pairwise correlations across stimuli where receptive field measurements
were possible.
article_processing_charge: No
author:
- first_name: Kristina
full_name: Simmons, Kristina
last_name: Simmons
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Jan
full_name: Homann, Jan
last_name: Homann
- first_name: Heather
full_name: Yee, Heather
last_name: Yee
- first_name: Stephanie
full_name: Palmer, Stephanie
last_name: Palmer
- first_name: Philip
full_name: Nelson, Philip
last_name: Nelson
- first_name: Vijay
full_name: Balasubramanian, Vijay
last_name: Balasubramanian
citation:
ama: 'Simmons K, Prentice J, Tkačik G, et al. Data from: Transformation of stimulus
correlations by the retina. 2014. doi:10.5061/dryad.246qg'
apa: 'Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., …
Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations
by the retina. Dryad. https://doi.org/10.5061/dryad.246qg'
chicago: 'Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather
Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Data from: Transformation
of Stimulus Correlations by the Retina.” Dryad, 2014. https://doi.org/10.5061/dryad.246qg.'
ieee: 'K. Simmons et al., “Data from: Transformation of stimulus correlations
by the retina.” Dryad, 2014.'
ista: 'Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian
V. 2014. Data from: Transformation of stimulus correlations by the retina, Dryad,
10.5061/dryad.246qg.'
mla: 'Simmons, Kristina, et al. Data from: Transformation of Stimulus Correlations
by the Retina. Dryad, 2014, doi:10.5061/dryad.246qg.'
short: K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson,
V. Balasubramanian, (2014).
date_created: 2021-07-30T08:13:52Z
date_published: 2014-11-07T00:00:00Z
date_updated: 2023-02-23T10:35:57Z
day: '07'
department:
- _id: GaTk
doi: 10.5061/dryad.246qg
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.246qg
month: '11'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '2277'
relation: used_in_publication
status: public
status: public
title: 'Data from: Transformation of stimulus correlations by the retina'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2014'
...
---
_id: '2257'
abstract:
- lang: eng
text: 'Maximum entropy models are the least structured probability distributions
that exactly reproduce a chosen set of statistics measured in an interacting network.
Here we use this principle to construct probabilistic models which describe the
correlated spiking activity of populations of up to 120 neurons in the salamander
retina as it responds to natural movies. Already in groups as small as 10 neurons,
interactions between spikes can no longer be regarded as small perturbations in
an otherwise independent system; for 40 or more neurons pairwise interactions
need to be supplemented by a global interaction that controls the distribution
of synchrony in the population. Here we show that such “K-pairwise” models—being
systematic extensions of the previously used pairwise Ising models—provide an
excellent account of the data. We explore the properties of the neural vocabulary
by: 1) estimating its entropy, which constrains the population''s capacity to
represent visual information; 2) classifying activity patterns into a small set
of metastable collective modes; 3) showing that the neural codeword ensembles
are extremely inhomogenous; 4) demonstrating that the state of individual neurons
is highly predictable from the rest of the population, allowing the capacity for
error correction.'
acknowledgement: "\r\n\r\n\r\n\r\nThis work was funded by NSF grant IIS-0613435, NSF
grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508,
the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation,
ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES:
ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651."
article_number: e1003408
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
- first_name: William
full_name: Bialek, William
last_name: Bialek
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for
collective behavior in a large network of sensory neurons. PLoS Computational
Biology. 2014;10(1). doi:10.1371/journal.pcbi.1003408
apa: Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., & Berry,
M. (2014). Searching for collective behavior in a large network of sensory neurons.
PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003408
chicago: Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek,
and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory
Neurons.” PLoS Computational Biology. Public Library of Science, 2014.
https://doi.org/10.1371/journal.pcbi.1003408.
ieee: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching
for collective behavior in a large network of sensory neurons,” PLoS Computational
Biology, vol. 10, no. 1. Public Library of Science, 2014.
ista: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching
for collective behavior in a large network of sensory neurons. PLoS Computational
Biology. 10(1), e1003408.
mla: Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network
of Sensory Neurons.” PLoS Computational Biology, vol. 10, no. 1, e1003408,
Public Library of Science, 2014, doi:10.1371/journal.pcbi.1003408.
short: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS
Computational Biology 10 (2014).
date_created: 2018-12-11T11:56:36Z
date_published: 2014-01-02T00:00:00Z
date_updated: 2024-02-21T13:46:14Z
day: '02'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1003408
file:
- access_level: open_access
checksum: c720222c5e924a4acb17f23b9381a6ca
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:46Z
date_updated: 2020-07-14T12:45:35Z
file_id: '4965'
file_name: IST-2016-436-v1+1_journal.pcbi.1003408.pdf
file_size: 2194790
relation: main_file
file_date_updated: 2020-07-14T12:45:35Z
has_accepted_license: '1'
intvolume: ' 10'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://repository.ist.ac.at/id/eprint/436
month: '01'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '4689'
pubrep_id: '436'
quality_controlled: '1'
related_material:
record:
- id: '5562'
relation: popular_science
status: public
scopus_import: 1
status: public
title: Searching for collective behavior in a large network of sensory neurons
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: 10
year: '2014'
...
---
_id: '2413'
abstract:
- lang: eng
text: 'Progress in understanding the global brain dynamics has remained slow to
date in large part because of the highly multiscale nature of brain activity.
Indeed, normal brain dynamics is characterized by complex interactions between
multiple levels: from the microscopic scale of single neurons to the mesoscopic
level of local groups of neurons, and finally to the macroscopic level of the
whole brain. Among the most difficult tasks are those of identifying which scales
are significant for a given particular function and describing how the scales
affect each other. It is important to realize that the scales of time and space
are linked together, or even intertwined, and that causal inference is far more
ambiguous between than within levels. We approach this problem from the perspective
of our recent work on simultaneous recording from micro- and macroelectrodes in
the human brain. We propose a physiological description of these multilevel interactions,
based on phase–amplitude coupling of neuronal oscillations that operate at multiple
frequencies and on different spatial scales. Specifically, the amplitude of the
oscillations on a particular spatial scale is modulated by phasic variations in
neuronal excitability induced by lower frequency oscillations that emerge on a
larger spatial scale. Following this general principle, it is possible to scale
up or scale down the multiscale brain dynamics. It is expected that large-scale
network oscillations in the low-frequency range, mediating downward effects, may
play an important role in attention and consciousness.'
alternative_title:
- Reviews of Nonlinear Dynamics and Complexity
author:
- first_name: Mario
full_name: Valderrama, Mario
last_name: Valderrama
- 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: Michel
full_name: Le Van Quyen, Michel
last_name: Le Van Quyen
citation:
ama: 'Valderrama M, Botella Soler V, Le Van Quyen M. Neuronal oscillations scale
up and scale down the brain dynamics . In: Meyer M, Pesenson Z, eds. Multiscale
Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH; 2013.
doi:10.1002/9783527671632.ch08'
apa: 'Valderrama, M., Botella Soler, V., & Le Van Quyen, M. (2013). Neuronal
oscillations scale up and scale down the brain dynamics . In M. Meyer & Z.
Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to
the Brain. Wiley-VCH. https://doi.org/10.1002/9783527671632.ch08'
chicago: 'Valderrama, Mario, Vicente Botella Soler, and Michel Le Van Quyen. “Neuronal
Oscillations Scale up and Scale down the Brain Dynamics .” In Multiscale Analysis
and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and
Z. Pesenson. Wiley-VCH, 2013. https://doi.org/10.1002/9783527671632.ch08.'
ieee: 'M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations
scale up and scale down the brain dynamics ,” in Multiscale Analysis and Nonlinear
Dynamics: From Genes to the Brain, M. Meyer and Z. Pesenson, Eds. Wiley-VCH,
2013.'
ista: 'Valderrama M, Botella Soler V, Le Van Quyen M. 2013.Neuronal oscillations
scale up and scale down the brain dynamics . In: Multiscale Analysis and Nonlinear
Dynamics: From Genes to the Brain. Reviews of Nonlinear Dynamics and Complexity,
.'
mla: 'Valderrama, Mario, et al. “Neuronal Oscillations Scale up and Scale down the
Brain Dynamics .” Multiscale Analysis and Nonlinear Dynamics: From Genes to
the Brain, edited by Misha Meyer and Z. Pesenson, Wiley-VCH, 2013, doi:10.1002/9783527671632.ch08.'
short: 'M. Valderrama, V. Botella Soler, M. Le Van Quyen, in:, M. Meyer, Z. Pesenson
(Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley-VCH,
2013.'
date_created: 2018-12-11T11:57:31Z
date_published: 2013-08-01T00:00:00Z
date_updated: 2021-01-12T06:57:20Z
day: '01'
department:
- _id: GaTk
doi: 10.1002/9783527671632.ch08
editor:
- first_name: Misha
full_name: Meyer, Misha
last_name: Meyer
- first_name: Z.
full_name: Pesenson, Z.
last_name: Pesenson
language:
- iso: eng
month: '08'
oa_version: None
publication: 'Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain'
publication_identifier:
eisbn:
- '9783527671632'
isbn:
- '9783527411986 '
publication_status: published
publisher: Wiley-VCH
publist_id: '4513'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Neuronal oscillations scale up and scale down the brain dynamics '
type: book_chapter
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2818'
abstract:
- lang: eng
text: Models of neural responses to stimuli with complex spatiotemporal correlation
structure often assume that neurons are selective for only a small number of linear
projections of a potentially high-dimensional input. In this review, we explore
recent modeling approaches where the neural response depends on the quadratic
form of the input rather than on its linear projection, that is, the neuron is
sensitive to the local covariance structure of the signal preceding the spike.
To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic)
stimulus distribution, we review several inference methods, focusing in particular
on two information theory–based approaches (maximization of stimulus energy and
of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered
covariance and extensions of generalized linear models). We analyze the formal
relationship between the likelihood-based and information-based approaches to
demonstrate how they lead to consistent inference. We demonstrate the practical
feasibility of these procedures by using model neurons responding to a flickering
variance stimulus.
author:
- first_name: Kanaka
full_name: Rajan, Kanaka
last_name: Rajan
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Rajan K, Marre O, Tkačik G. Learning quadratic receptive fields from neural
responses to natural stimuli. Neural Computation. 2013;25(7):1661-1692.
doi:10.1162/NECO_a_00463
apa: Rajan, K., Marre, O., & Tkačik, G. (2013). Learning quadratic receptive
fields from neural responses to natural stimuli. Neural Computation. MIT
Press . https://doi.org/10.1162/NECO_a_00463
chicago: Rajan, Kanaka, Olivier Marre, and Gašper Tkačik. “Learning Quadratic Receptive
Fields from Neural Responses to Natural Stimuli.” Neural Computation. MIT
Press , 2013. https://doi.org/10.1162/NECO_a_00463.
ieee: K. Rajan, O. Marre, and G. Tkačik, “Learning quadratic receptive fields from
neural responses to natural stimuli,” Neural Computation, vol. 25, no.
7. MIT Press , pp. 1661–1692, 2013.
ista: Rajan K, Marre O, Tkačik G. 2013. Learning quadratic receptive fields from
neural responses to natural stimuli. Neural Computation. 25(7), 1661–1692.
mla: Rajan, Kanaka, et al. “Learning Quadratic Receptive Fields from Neural Responses
to Natural Stimuli.” Neural Computation, vol. 25, no. 7, MIT Press , 2013,
pp. 1661–92, doi:10.1162/NECO_a_00463.
short: K. Rajan, O. Marre, G. Tkačik, Neural Computation 25 (2013) 1661–1692.
date_created: 2018-12-11T11:59:45Z
date_published: 2013-07-01T00:00:00Z
date_updated: 2021-01-12T06:59:56Z
day: '01'
department:
- _id: GaTk
doi: 10.1162/NECO_a_00463
external_id:
arxiv:
- '1209.0121'
intvolume: ' 25'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1209.0121
month: '07'
oa: 1
oa_version: Preprint
page: 1661 - 1692
publication: Neural Computation
publication_status: published
publisher: 'MIT Press '
publist_id: '3983'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning quadratic receptive fields from neural responses to natural stimuli
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2013'
...
---
_id: '2850'
abstract:
- lang: eng
text: "Recent work emphasizes that the maximum entropy principle provides a bridge
between statistical mechanics models for collective behavior in neural networks
and experiments on networks of real neurons. Most of this work has focused on
capturing the measured correlations among pairs of neurons. Here we suggest an
alternative, constructing models that are consistent with the distribution of
global network activity, i.e. the probability that K out of N cells in the network
generate action potentials in the same small time bin. The inverse problem that
we need to solve in constructing the model is analytically tractable, and provides
a natural 'thermodynamics' for the network in the limit of large N. We analyze
the responses of neurons in a small patch of the retina to naturalistic stimuli,
and find that the implied thermodynamics is very close to an unusual critical
point, in which the entropy (in proper units) is exactly equal to the energy.
© 2013 IOP Publishing Ltd and SISSA Medialab srl.\r\n"
acknowledgement: "his work was supported in part by NSF Grants IIS-0613435 and PHY-0957573,
by NIH Grants R01 EY14196 and P50 GM071508, by the Fannie and John Hertz Foundation,
by the Human Frontiers Science Program, by the Swartz Foundation, and by the WM
Keck Foundation.\r\n"
article_number: P03011
article_processing_charge: No
article_type: original
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. The simplest maximum
entropy model for collective behavior in a neural network. Journal of Statistical
Mechanics Theory and Experiment. 2013;2013(3). doi:10.1088/1742-5468/2013/03/P03011
apa: Tkačik, G., Marre, O., Mora, T., Amodei, D., Berry, M., & Bialek, W. (2013).
The simplest maximum entropy model for collective behavior in a neural network.
Journal of Statistical Mechanics Theory and Experiment. IOP Publishing
Ltd. https://doi.org/10.1088/1742-5468/2013/03/P03011
chicago: Tkačik, Gašper, Olivier Marre, Thierry Mora, Dario Amodei, Michael Berry,
and William Bialek. “The Simplest Maximum Entropy Model for Collective Behavior
in a Neural Network.” Journal of Statistical Mechanics Theory and Experiment.
IOP Publishing Ltd., 2013. https://doi.org/10.1088/1742-5468/2013/03/P03011.
ieee: G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, and W. Bialek, “The simplest
maximum entropy model for collective behavior in a neural network,” Journal
of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing
Ltd., 2013.
ista: Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. 2013. The simplest
maximum entropy model for collective behavior in a neural network. Journal of
Statistical Mechanics Theory and Experiment. 2013(3), P03011.
mla: Tkačik, Gašper, et al. “The Simplest Maximum Entropy Model for Collective Behavior
in a Neural Network.” Journal of Statistical Mechanics Theory and Experiment,
vol. 2013, no. 3, P03011, IOP Publishing Ltd., 2013, doi:10.1088/1742-5468/2013/03/P03011.
short: G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek, Journal of
Statistical Mechanics Theory and Experiment 2013 (2013).
date_created: 2018-12-11T11:59:55Z
date_published: 2013-03-12T00:00:00Z
date_updated: 2021-01-12T07:00:14Z
day: '12'
department:
- _id: GaTk
doi: 10.1088/1742-5468/2013/03/P03011
external_id:
arxiv:
- '1207.6319'
intvolume: ' 2013'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1207.6319
month: '03'
oa: 1
oa_version: Preprint
publication: Journal of Statistical Mechanics Theory and Experiment
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '3942'
quality_controlled: '1'
scopus_import: 1
status: public
title: The simplest maximum entropy model for collective behavior in a neural network
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2013
year: '2013'
...