---
_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
license: https://creativecommons.org/licenses/by/4.0/
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'
...