---
_id: '735'
abstract:
- lang: eng
text: Cell-cell contact formation constitutes an essential step in evolution, leading
to the differentiation of specialized cell types. However, remarkably little is
known about whether and how the interplay between contact formation and fate specification
affects development. Here, we identify a positive feedback loop between cell-cell
contact duration, morphogen signaling, and mesendoderm cell-fate specification
during zebrafish gastrulation. We show that long-lasting cell-cell contacts enhance
the competence of prechordal plate (ppl) progenitor cells to respond to Nodal
signaling, required for ppl cell-fate specification. We further show that Nodal
signaling promotes ppl cell-cell contact duration, generating a positive feedback
loop between ppl cell-cell contact duration and cell-fate specification. Finally,
by combining mathematical modeling and experimentation, we show that this feedback
determines whether anterior axial mesendoderm cells become ppl or, instead, turn
into endoderm. Thus, the interdependent activities of cell-cell signaling and
contact formation control fate diversification within the developing embryo.
article_processing_charge: No
author:
- first_name: Vanessa
full_name: Barone, Vanessa
id: 419EECCC-F248-11E8-B48F-1D18A9856A87
last_name: Barone
orcid: 0000-0003-2676-3367
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Gabriel
full_name: Krens, Gabriel
id: 2B819732-F248-11E8-B48F-1D18A9856A87
last_name: Krens
orcid: 0000-0003-4761-5996
- first_name: Saurabh
full_name: Pradhan, Saurabh
last_name: Pradhan
- first_name: Shayan
full_name: Shamipour, Shayan
id: 40B34FE2-F248-11E8-B48F-1D18A9856A87
last_name: Shamipour
- first_name: Keisuke
full_name: Sako, Keisuke
id: 3BED66BE-F248-11E8-B48F-1D18A9856A87
last_name: Sako
orcid: 0000-0002-6453-8075
- first_name: Mateusz K
full_name: Sikora, Mateusz K
id: 2F74BCDE-F248-11E8-B48F-1D18A9856A87
last_name: Sikora
- 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: Carl-Philipp J
full_name: Heisenberg, Carl-Philipp J
id: 39427864-F248-11E8-B48F-1D18A9856A87
last_name: Heisenberg
orcid: 0000-0002-0912-4566
citation:
ama: Barone V, Lang M, Krens G, et al. An effective feedback loop between cell-cell
contact duration and morphogen signaling determines cell fate. Developmental
Cell. 2017;43(2):198-211. doi:10.1016/j.devcel.2017.09.014
apa: Barone, V., Lang, M., Krens, G., Pradhan, S., Shamipour, S., Sako, K., … Heisenberg,
C.-P. J. (2017). An effective feedback loop between cell-cell contact duration
and morphogen signaling determines cell fate. Developmental Cell. Cell
Press. https://doi.org/10.1016/j.devcel.2017.09.014
chicago: Barone, Vanessa, Moritz Lang, Gabriel Krens, Saurabh Pradhan, Shayan Shamipour,
Keisuke Sako, Mateusz K Sikora, Calin C Guet, and Carl-Philipp J Heisenberg. “An
Effective Feedback Loop between Cell-Cell Contact Duration and Morphogen Signaling
Determines Cell Fate.” Developmental Cell. Cell Press, 2017. https://doi.org/10.1016/j.devcel.2017.09.014.
ieee: V. Barone et al., “An effective feedback loop between cell-cell contact
duration and morphogen signaling determines cell fate,” Developmental Cell,
vol. 43, no. 2. Cell Press, pp. 198–211, 2017.
ista: Barone V, Lang M, Krens G, Pradhan S, Shamipour S, Sako K, Sikora MK, Guet
CC, Heisenberg C-PJ. 2017. An effective feedback loop between cell-cell contact
duration and morphogen signaling determines cell fate. Developmental Cell. 43(2),
198–211.
mla: Barone, Vanessa, et al. “An Effective Feedback Loop between Cell-Cell Contact
Duration and Morphogen Signaling Determines Cell Fate.” Developmental Cell,
vol. 43, no. 2, Cell Press, 2017, pp. 198–211, doi:10.1016/j.devcel.2017.09.014.
short: V. Barone, M. Lang, G. Krens, S. Pradhan, S. Shamipour, K. Sako, M.K. Sikora,
C.C. Guet, C.-P.J. Heisenberg, Developmental Cell 43 (2017) 198–211.
date_created: 2018-12-11T11:48:13Z
date_published: 2017-10-23T00:00:00Z
date_updated: 2024-03-27T23:30:38Z
day: '23'
department:
- _id: CaHe
- _id: CaGu
- _id: GaTk
doi: 10.1016/j.devcel.2017.09.014
ec_funded: 1
external_id:
isi:
- '000413443700011'
intvolume: ' 43'
isi: 1
issue: '2'
language:
- iso: eng
month: '10'
oa_version: None
page: 198 - 211
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 252DD2A6-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I2058
name: 'Cell segregation in gastrulation: the role of cell fate specification'
publication: Developmental Cell
publication_identifier:
issn:
- '15345807'
publication_status: published
publisher: Cell Press
publist_id: '6934'
quality_controlled: '1'
related_material:
record:
- id: '961'
relation: dissertation_contains
status: public
- id: '8350'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: An effective feedback loop between cell-cell contact duration and morphogen
signaling determines cell fate
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 43
year: '2017'
...
---
_id: '1082'
abstract:
- lang: eng
text: In many applications, it is desirable to extract only the relevant aspects
of data. A principled way to do this is the information bottleneck (IB) method,
where one seeks a code that maximises information about a relevance variable,
Y, while constraining the information encoded about the original data, X. Unfortunately
however, the IB method is computationally demanding when data are high-dimensional
and/or non-gaussian. Here we propose an approximate variational scheme for maximising
a lower bound on the IB objective, analogous to variational EM. Using this method,
we derive an IB algorithm to recover features that are both relevant and sparse.
Finally, we demonstrate how kernelised versions of the algorithm can be used to
address a broad range of problems with non-linear relation between X and Y.
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Matthew J
full_name: Chalk, Matthew J
id: 2BAAC544-F248-11E8-B48F-1D18A9856A87
last_name: Chalk
orcid: 0000-0001-7782-4436
- 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: 'Chalk MJ, Marre O, Tkačik G. Relevant sparse codes with variational information
bottleneck. In: Vol 29. Neural Information Processing Systems; 2016:1965-1973.'
apa: 'Chalk, M. J., Marre, O., & Tkačik, G. (2016). Relevant sparse codes with
variational information bottleneck (Vol. 29, pp. 1965–1973). Presented at the
NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information
Processing Systems.'
chicago: Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Relevant Sparse Codes
with Variational Information Bottleneck,” 29:1965–73. Neural Information Processing
Systems, 2016.
ieee: 'M. J. Chalk, O. Marre, and G. Tkačik, “Relevant sparse codes with variational
information bottleneck,” presented at the NIPS: Neural Information Processing
Systems, Barcelona, Spain, 2016, vol. 29, pp. 1965–1973.'
ista: 'Chalk MJ, Marre O, Tkačik G. 2016. Relevant sparse codes with variational
information bottleneck. NIPS: Neural Information Processing Systems, Advances
in Neural Information Processing Systems, vol. 29, 1965–1973.'
mla: Chalk, Matthew J., et al. Relevant Sparse Codes with Variational Information
Bottleneck. Vol. 29, Neural Information Processing Systems, 2016, pp. 1965–73.
short: M.J. Chalk, O. Marre, G. Tkačik, in:, Neural Information Processing Systems,
2016, pp. 1965–1973.
conference:
end_date: 2016-12-10
location: Barcelona, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:03Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:09Z
day: '01'
department:
- _id: GaTk
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1605.07332
month: '12'
oa: 1
oa_version: Preprint
page: 1965-1973
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6298'
quality_controlled: '1'
related_material:
link:
- relation: other
url: https://papers.nips.cc/paper/6101-relevant-sparse-codes-with-variational-information-bottleneck
scopus_import: 1
status: public
title: Relevant sparse codes with variational information bottleneck
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1105'
abstract:
- lang: eng
text: Jointly characterizing neural responses in terms of several external variables
promises novel insights into circuit function, but remains computationally prohibitive
in practice. Here we use gaussian process (GP) priors and exploit recent advances
in fast GP inference and learning based on Kronecker methods, to efficiently estimate
multidimensional nonlinear tuning functions. Our estimator require considerably
less data than traditional methods and further provides principled uncertainty
estimates. We apply these tools to hippocampal recordings during open field exploration
and use them to characterize the joint dependence of CA1 responses on the position
of the animal and several other variables, including the animal\'s speed, direction
of motion, and network oscillations.Our results provide an unprecedentedly detailed
quantification of the tuning of hippocampal neurons. The model\'s generality suggests
that our approach can be used to estimate neural response properties in other
brain regions.
acknowledgement: "We thank Jozsef Csicsvari for kindly sharing the CA1 data.\r\nThis
work was supported by the People Programme (Marie Curie Actions) of the European
Union’s Seventh Framework Programme(FP7/2007-2013) under REA grant agreement no.
291734."
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: 'Savin C, Tkačik G. Estimating nonlinear neural response functions using GP
priors and Kronecker methods. In: Vol 29. Neural Information Processing Systems;
2016:3610-3618.'
apa: 'Savin, C., & Tkačik, G. (2016). Estimating nonlinear neural response functions
using GP priors and Kronecker methods (Vol. 29, pp. 3610–3618). Presented at the
NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information
Processing Systems.'
chicago: Savin, Cristina, and Gašper Tkačik. “Estimating Nonlinear Neural Response
Functions Using GP Priors and Kronecker Methods,” 29:3610–18. Neural Information
Processing Systems, 2016.
ieee: 'C. Savin and G. Tkačik, “Estimating nonlinear neural response functions using
GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing
Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.'
ista: 'Savin C, Tkačik G. 2016. Estimating nonlinear neural response functions using
GP priors and Kronecker methods. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 29, 3610–3618.'
mla: Savin, Cristina, and Gašper Tkačik. Estimating Nonlinear Neural Response
Functions Using GP Priors and Kronecker Methods. Vol. 29, Neural Information
Processing Systems, 2016, pp. 3610–18.
short: C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp.
3610–3618.
conference:
end_date: 2016-12-10
location: Barcelona; Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:10Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:19Z
day: '01'
department:
- _id: GaTk
ec_funded: 1
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- url: http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods
month: '12'
oa_version: None
page: 3610-3618
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: '6265'
quality_controlled: '1'
scopus_import: 1
status: public
title: Estimating nonlinear neural response functions using GP priors and Kronecker
methods
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1170'
abstract:
- lang: eng
text: The increasing complexity of dynamic models in systems and synthetic biology
poses computational challenges especially for the identification of model parameters.
While modularization of the corresponding optimization problems could help reduce
the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit
a simple decomposition of most biomolecular networks into subnetworks, or modules.
Drawing on ideas from network modularization and multiple-shooting optimization,
we present here a modular parameter identification approach that explicitly allows
for such interdependencies. Interfaces between our modules are given by the experimentally
measured molecular species. This definition allows deriving good (initial) estimates
for the inter-module communication directly from the experimental data. Given
these estimates, the states and parameter sensitivities of different modules can
be integrated independently. To achieve consistency between modules, we iteratively
adjust the estimates for inter-module communication while optimizing the parameters.
After convergence to an optimal parameter set---but not during earlier iterations---the
intermodule communication as well as the individual modules\' state dynamics agree
with the dynamics of the nonmodularized network. Our modular parameter identification
approach allows for easy parallelization; it can reduce the computational complexity
for larger networks and decrease the probability to converge to suboptimal local
minima. We demonstrate the algorithm\'s performance in parameter estimation for
two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling
pathway.
author:
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Jörg
full_name: Stelling, Jörg
last_name: Stelling
citation:
ama: Lang M, Stelling J. Modular parameter identification of biomolecular networks.
SIAM Journal on Scientific Computing. 2016;38(6):B988-B1008. doi:10.1137/15M103306X
apa: Lang, M., & Stelling, J. (2016). Modular parameter identification of biomolecular
networks. SIAM Journal on Scientific Computing. Society for Industrial
and Applied Mathematics . https://doi.org/10.1137/15M103306X
chicago: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing. Society for Industrial
and Applied Mathematics , 2016. https://doi.org/10.1137/15M103306X.
ieee: M. Lang and J. Stelling, “Modular parameter identification of biomolecular
networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society
for Industrial and Applied Mathematics , pp. B988–B1008, 2016.
ista: Lang M, Stelling J. 2016. Modular parameter identification of biomolecular
networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008.
mla: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing, vol. 38, no. 6, Society
for Industrial and Applied Mathematics , 2016, pp. B988–1008, doi:10.1137/15M103306X.
short: M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.
date_created: 2018-12-11T11:50:31Z
date_published: 2016-11-15T00:00:00Z
date_updated: 2021-01-12T06:48:49Z
day: '15'
ddc:
- '003'
- '518'
- '570'
- '621'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1137/15M103306X
file:
- access_level: local
checksum: 781bc3ffd30b2dd65b7727c5a285fc78
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:41Z
date_updated: 2020-07-14T12:44:37Z
file_id: '5095'
file_name: IST-2017-811-v1+1_modular_parameter_identification.pdf
file_size: 871964
relation: main_file
file_date_updated: 2020-07-14T12:44:37Z
has_accepted_license: '1'
intvolume: ' 38'
issue: '6'
language:
- iso: eng
month: '11'
oa_version: Submitted Version
page: B988 - B1008
publication: SIAM Journal on Scientific Computing
publication_status: published
publisher: 'Society for Industrial and Applied Mathematics '
publist_id: '6186'
pubrep_id: '811'
quality_controlled: '1'
scopus_import: 1
status: public
title: Modular parameter identification of biomolecular networks
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2016'
...
---
_id: '1171'
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: 'Tkačik G. Understanding regulatory networks requires more than computing a
multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. 2016;17:166-167. doi:10.1016/j.plrev.2016.06.005'
apa: 'Tkačik, G. (2016). Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. Elsevier. https://doi.org/10.1016/j.plrev.2016.06.005'
chicago: 'Tkačik, Gašper. “Understanding Regulatory Networks Requires More than
Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural
Features in Gene Regulatory Networks: From Biophysical Constraints to Biological
Function" by O. C. Martin et Al.” Physics of Life Reviews. Elsevier,
2016. https://doi.org/10.1016/j.plrev.2016.06.005.'
ieee: 'G. Tkačik, “Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al.,” Physics of Life Reviews, vol. 17. Elsevier, pp.
166–167, 2016.'
ista: 'Tkačik G. 2016. Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. 17, 166–167.'
mla: 'Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing
a Multitude of Graph Statistics: Comment on "Drivers of Structural Features
in Gene Regulatory Networks: From Biophysical Constraints to Biological Function"
by O. C. Martin et Al.” Physics of Life Reviews, vol. 17, Elsevier, 2016,
pp. 166–67, doi:10.1016/j.plrev.2016.06.005.'
short: G. Tkačik, Physics of Life Reviews 17 (2016) 166–167.
date_created: 2018-12-11T11:50:32Z
date_published: 2016-07-01T00:00:00Z
date_updated: 2021-01-12T06:48:50Z
day: '01'
department:
- _id: GaTk
doi: 10.1016/j.plrev.2016.06.005
intvolume: ' 17'
language:
- iso: eng
month: '07'
oa_version: None
page: 166 - 167
publication: Physics of Life Reviews
publication_status: published
publisher: Elsevier
publist_id: '6185'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Understanding regulatory networks requires more than computing a multitude
of graph statistics: Comment on "Drivers of structural features in gene regulatory
networks: From biophysical constraints to biological function" by O. C. Martin
et al.'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2016'
...