--- _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' ...