--- _id: '67' abstract: - lang: eng text: 'Gene regulatory networks evolve through rewiring of individual components—that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor–DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor–DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution.' article_processing_charge: No article_type: original author: - first_name: Claudia full_name: Igler, Claudia id: 46613666-F248-11E8-B48F-1D18A9856A87 last_name: Igler - first_name: Mato full_name: Lagator, Mato id: 345D25EC-F248-11E8-B48F-1D18A9856A87 last_name: Lagator - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Jonathan P full_name: Bollback, Jonathan P id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87 last_name: Bollback orcid: 0000-0002-4624-4612 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 citation: ama: Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. 2018;2(10):1633-1643. doi:10.1038/s41559-018-0651-y apa: Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., & Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. Nature Publishing Group. https://doi.org/10.1038/s41559-018-0651-y chicago: Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Nature Ecology and Evolution. Nature Publishing Group, 2018. https://doi.org/10.1038/s41559-018-0651-y. ieee: C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Evolutionary potential of transcription factors for gene regulatory rewiring,” Nature Ecology and Evolution, vol. 2, no. 10. Nature Publishing Group, pp. 1633–1643, 2018. ista: Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. 2018. Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. 2(10), 1633–1643. mla: Igler, Claudia, et al. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Nature Ecology and Evolution, vol. 2, no. 10, Nature Publishing Group, 2018, pp. 1633–43, doi:10.1038/s41559-018-0651-y. short: C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, Nature Ecology and Evolution 2 (2018) 1633–1643. date_created: 2018-12-11T11:44:27Z date_published: 2018-09-10T00:00:00Z date_updated: 2024-03-18T23:30:50Z day: '10' ddc: - '570' department: - _id: CaGu - _id: GaTk - _id: JoBo doi: 10.1038/s41559-018-0651-y ec_funded: 1 external_id: isi: - '000447947600021' file: - access_level: open_access checksum: 383a2e2c944a856e2e821ec8e7bf71b6 content_type: application/pdf creator: dernst date_created: 2020-05-14T11:28:52Z date_updated: 2020-07-14T12:47:37Z file_id: '7830' file_name: 2018_NatureEcology_Igler.pdf file_size: 1135973 relation: main_file file_date_updated: 2020-07-14T12:47:37Z has_accepted_license: '1' intvolume: ' 2' isi: 1 issue: '10' language: - iso: eng month: '09' oa: 1 oa_version: Submitted Version page: 1633 - 1643 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 2578D616-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '648440' name: Selective Barriers to Horizontal Gene Transfer - _id: 251EE76E-B435-11E9-9278-68D0E5697425 grant_number: '24573' name: Design principles underlying genetic switch architecture (DOC Fellowship) publication: Nature Ecology and Evolution publication_status: published publisher: Nature Publishing Group publist_id: '7987' quality_controlled: '1' related_material: record: - id: '5585' relation: popular_science status: public - id: '6371' relation: dissertation_contains status: public scopus_import: '1' status: public title: Evolutionary potential of transcription factors for gene regulatory rewiring type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2 year: '2018' ... --- _id: '5585' abstract: - lang: eng text: Mean repression values and standard error of the mean are given for all operator mutant libraries. article_processing_charge: No author: - first_name: Claudia full_name: Igler, Claudia id: 46613666-F248-11E8-B48F-1D18A9856A87 last_name: Igler - first_name: Mato full_name: Lagator, Mato id: 345D25EC-F248-11E8-B48F-1D18A9856A87 last_name: Lagator - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Jonathan P full_name: Bollback, Jonathan P id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87 last_name: Bollback orcid: 0000-0002-4624-4612 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 citation: ama: Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. 2018. doi:10.15479/AT:ISTA:108 apa: Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., & Guet, C. C. (2018). Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:108 chicago: Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:108. ieee: C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring.” Institute of Science and Technology Austria, 2018. ista: Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. 2018. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring, Institute of Science and Technology Austria, 10.15479/AT:ISTA:108. mla: Igler, Claudia, et al. Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:108. short: C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, (2018). datarep_id: '108' date_created: 2018-12-12T12:31:40Z date_published: 2018-07-20T00:00:00Z date_updated: 2024-03-18T23:30:50Z day: '20' ddc: - '576' department: - _id: CaGu - _id: GaTk doi: 10.15479/AT:ISTA:108 ec_funded: 1 file: - access_level: open_access checksum: 1435781526c77413802adee0d4583cce content_type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet creator: system date_created: 2018-12-12T13:02:45Z date_updated: 2020-07-14T12:47:07Z file_id: '5611' file_name: IST-2018-108-v1+1_data_figures.xlsx file_size: 16507 relation: main_file file_date_updated: 2020-07-14T12:47:07Z has_accepted_license: '1' license: https://creativecommons.org/publicdomain/zero/1.0/ month: '07' 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: 2578D616-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '648440' name: Selective Barriers to Horizontal Gene Transfer - _id: 251EE76E-B435-11E9-9278-68D0E5697425 grant_number: '24573' name: Design principles underlying genetic switch architecture (DOC Fellowship) publisher: Institute of Science and Technology Austria related_material: record: - id: '67' relation: research_paper status: public - id: '6371' relation: research_paper status: public status: public title: Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ... --- _id: '613' abstract: - lang: eng text: 'Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.' acknowledgement: We are grateful to M. Lang, H. Janovjak, M. Khammash, A. Milias-Argeitis, M. Rullan, G. Batt, A. Bosma-Moody, Aryan, S. Leibler, and members of the Guet and Tkačik groups for helpful discussion, comments, and suggestions. We thank A. Moglich, T. Mathes, J. Tabor, and S. Schmidl for kind gifts of strains, and R. Hauschild, B. Knep, M. Lang, T. Asenov, E. Papusheva, T. Menner, T. Adletzberger, and J. Merrin for technical assistance. 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 no. [291734]. (to R.C. and J.R.), Austrian Science Fund grant FWF P28844 (to G.T.), and internal IST Austria Interdisciplinary Project Support. J.R. acknowledges support from the Agence Nationale de la Recherche (ANR) under Grant Nos. ANR-16-CE33-0018 (MEMIP), ANR-16-CE12-0025 (COGEX) and ANR-10-BINF-06-01 (ICEBERG). article_number: '1535' article_processing_charge: Yes (in subscription journal) 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: Jakob full_name: Ruess, Jakob id: 4A245D00-F248-11E8-B48F-1D18A9856A87 last_name: Ruess orcid: 0000-0003-1615-3282 - first_name: Tobias full_name: Bergmiller, Tobias id: 2C471CFA-F248-11E8-B48F-1D18A9856A87 last_name: Bergmiller orcid: 0000-0001-5396-4346 - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 citation: ama: Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-01683-1 apa: Chait, R. P., Ruess, J., Bergmiller, T., Tkačik, G., & Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-01683-1 chicago: Chait, Remy P, Jakob Ruess, Tobias Bergmiller, Gašper Tkačik, and Calin C Guet. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/s41467-017-01683-1. ieee: R. P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, and C. C. Guet, “Shaping bacterial population behavior through computer interfaced control of individual cells,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017. ista: Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. 2017. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 8(1), 1535. mla: Chait, Remy P., et al. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” Nature Communications, vol. 8, no. 1, 1535, Nature Publishing Group, 2017, doi:10.1038/s41467-017-01683-1. short: R.P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, C.C. Guet, Nature Communications 8 (2017). date_created: 2018-12-11T11:47:30Z date_published: 2017-12-01T00:00:00Z date_updated: 2021-01-12T08:06:15Z day: '01' ddc: - '576' - '579' department: - _id: CaGu - _id: GaTk doi: 10.1038/s41467-017-01683-1 ec_funded: 1 file: - access_level: open_access checksum: 44bb5d0229926c23a9955d9fe0f9723f content_type: application/pdf creator: system date_created: 2018-12-12T10:16:05Z date_updated: 2020-07-14T12:47:20Z file_id: '5190' file_name: IST-2017-911-v1+1_s41467-017-01683-1.pdf file_size: 1951699 relation: main_file file_date_updated: 2020-07-14T12:47:20Z has_accepted_license: '1' intvolume: ' 8' issue: '1' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '12' 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: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: Nature Communications publication_identifier: issn: - '20411723' publication_status: published publisher: Nature Publishing Group publist_id: '7191' pubrep_id: '911' quality_controlled: '1' scopus_import: 1 status: public title: Shaping bacterial population behavior through computer interfaced control of individual cells 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: 8 year: '2017' ... --- _id: '652' abstract: - lang: eng text: 'We present an approach that enables robots to self-organize their sensorimotor behavior from scratch without providing specific information about neither the robot nor its environment. This is achieved by a simple neural control law that increases the consistency between external sensor dynamics and internal neural dynamics of the utterly simple controller. In this way, the embodiment and the agent-environment coupling are the only source of individual development. We show how an anthropomorphic tendon driven arm-shoulder system develops different behaviors depending on that coupling. For instance: Given a bottle half-filled with water, the arm starts to shake it, driven by the physical response of the water. When attaching a brush, the arm can be manipulated into wiping a table, and when connected to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said to discover the affordances of the world. When allowing two (simulated) humanoid robots to interact physically, they engage into a joint behavior development leading to, for instance, spontaneous cooperation. More social effects are observed if the robots can visually perceive each other. Although, as an observer, it is tempting to attribute an apparent intentionality, there is nothing of the kind put in. As a conclusion, we argue that emergent behavior may be much less rooted in explicit intentions, internal motivations, or specific reward systems than is commonly believed.' article_number: '7846789' 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. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789' apa: 'Der, R., & Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789' chicago: Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots.” IEEE, 2017. https://doi.org/10.1109/DEVLRN.2016.7846789. ieee: 'R. Der and G. S. Martius, “Dynamical self consistency leads to behavioral development and emergent social interactions in robots,” presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France, 2017.' ista: 'Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , 7846789.' mla: Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017, doi:10.1109/DEVLRN.2016.7846789. short: R. Der, G.S. Martius, in:, IEEE, 2017. conference: end_date: 2016-09-22 location: Cergy-Pontoise, France name: 'ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics ' start_date: 2016-09-19 date_created: 2018-12-11T11:47:43Z date_published: 2017-02-07T00:00:00Z date_updated: 2021-01-12T08:07:51Z day: '07' department: - _id: ChLa - _id: GaTk doi: 10.1109/DEVLRN.2016.7846789 language: - iso: eng month: '02' oa_version: None publication_identifier: isbn: - 978-150905069-7 publication_status: published publisher: IEEE publist_id: '7100' quality_controlled: '1' scopus_import: 1 status: public title: Dynamical self consistency leads to behavioral development and emergent social interactions in robots type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '658' abstract: - lang: eng text: 'With the accelerated development of robot technologies, 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 specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object''s identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics.' article_number: '00008' article_processing_charge: Yes 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. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008 apa: Der, R., & Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation. https://doi.org/10.3389/fnbot.2017.00008 chicago: Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics. Frontiers Research Foundation, 2017. https://doi.org/10.3389/fnbot.2017.00008. ieee: R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017. ista: Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 11(MAR), 00008. mla: Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers Research Foundation, 2017, doi:10.3389/fnbot.2017.00008. short: R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017). date_created: 2018-12-11T11:47:45Z date_published: 2017-03-16T00:00:00Z date_updated: 2021-01-12T08:08:04Z day: '16' ddc: - '006' department: - _id: ChLa - _id: GaTk doi: 10.3389/fnbot.2017.00008 ec_funded: 1 file: - access_level: open_access checksum: b1bc43f96d1df3313c03032c2a46388d content_type: application/pdf creator: system date_created: 2018-12-12T10:18:49Z date_updated: 2020-07-14T12:47:33Z file_id: '5371' file_name: IST-2017-903-v1+1_fnbot-11-00008.pdf file_size: 8439566 relation: main_file file_date_updated: 2020-07-14T12:47:33Z has_accepted_license: '1' intvolume: ' 11' issue: MAR 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: Frontiers in Neurorobotics publication_identifier: issn: - '16625218' publication_status: published publisher: Frontiers Research Foundation publist_id: '7078' pubrep_id: '903' quality_controlled: '1' scopus_import: 1 status: public title: Self organized behavior generation for musculoskeletal 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: 2EBD1598-F248-11E8-B48F-1D18A9856A87 volume: 11 year: '2017' ... --- _id: '720' abstract: - lang: eng text: 'Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.' article_number: e1005763 article_processing_charge: Yes author: - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 2017;13(9). doi:10.1371/journal.pcbi.1005763 apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763 chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763. ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017. ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763. mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763. short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:48:08Z date_published: 2017-09-19T00:00:00Z date_updated: 2021-01-12T08:12:21Z day: '19' ddc: - '530' - '571' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005763 file: - access_level: open_access checksum: 81107096c19771c36ddbe6f0282a3acb content_type: application/pdf creator: system date_created: 2018-12-12T10:18:30Z date_updated: 2020-07-14T12:47:53Z file_id: '5352' file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf file_size: 14167050 relation: main_file file_date_updated: 2020-07-14T12:47:53Z has_accepted_license: '1' intvolume: ' 13' issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 255008E4-B435-11E9-9278-68D0E5697425 grant_number: RGP0065/2012 name: Information processing and computation in fish groups - _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_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '6960' pubrep_id: '884' quality_controlled: '1' scopus_import: 1 status: public title: Probabilistic models for neural populations that naturally capture global coupling and criticality 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: 13 year: '2017' ... --- _id: '725' abstract: - lang: eng text: Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an “active” mode, in which they are sensitive to the swimming patterns of conspecifics, and a “passive” mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors’ behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species. author: - first_name: Roy full_name: Harpaz, Roy last_name: Harpaz - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Harpaz R, Tkačik G, Schneidman E. Discrete modes of social information processing predict individual behavior of fish in a group. PNAS. 2017;114(38):10149-10154. doi:10.1073/pnas.1703817114 apa: Harpaz, R., Tkačik, G., & Schneidman, E. (2017). Discrete modes of social information processing predict individual behavior of fish in a group. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1703817114 chicago: Harpaz, Roy, Gašper Tkačik, and Elad Schneidman. “Discrete Modes of Social Information Processing Predict Individual Behavior of Fish in a Group.” PNAS. National Academy of Sciences, 2017. https://doi.org/10.1073/pnas.1703817114. ieee: R. Harpaz, G. Tkačik, and E. Schneidman, “Discrete modes of social information processing predict individual behavior of fish in a group,” PNAS, vol. 114, no. 38. National Academy of Sciences, pp. 10149–10154, 2017. ista: Harpaz R, Tkačik G, Schneidman E. 2017. Discrete modes of social information processing predict individual behavior of fish in a group. PNAS. 114(38), 10149–10154. mla: Harpaz, Roy, et al. “Discrete Modes of Social Information Processing Predict Individual Behavior of Fish in a Group.” PNAS, vol. 114, no. 38, National Academy of Sciences, 2017, pp. 10149–54, doi:10.1073/pnas.1703817114. short: R. Harpaz, G. Tkačik, E. Schneidman, PNAS 114 (2017) 10149–10154. date_created: 2018-12-11T11:48:10Z date_published: 2017-09-19T00:00:00Z date_updated: 2021-01-12T08:12:36Z day: '19' department: - _id: GaTk doi: 10.1073/pnas.1703817114 external_id: pmid: - '28874581' intvolume: ' 114' issue: '38' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5617265/ month: '09' oa: 1 oa_version: Submitted Version page: 10149 - 10154 pmid: 1 publication: PNAS publication_identifier: issn: - '00278424' publication_status: published publisher: National Academy of Sciences publist_id: '6953' quality_controlled: '1' scopus_import: 1 status: public title: Discrete modes of social information processing predict individual behavior of fish in a group type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 114 year: '2017' ... --- _id: '9709' 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. article_processing_charge: No 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: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik 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. Data from: Error-robust modes of the retinal population code. 2017. doi:10.5061/dryad.1f1rc' apa: 'Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M. (2017). Data from: Error-robust modes of the retinal population code. Dryad. https://doi.org/10.5061/dryad.1f1rc' chicago: 'Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Data from: Error-Robust Modes of the Retinal Population Code.” Dryad, 2017. https://doi.org/10.5061/dryad.1f1rc.' ieee: 'J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Data from: Error-robust modes of the retinal population code.” Dryad, 2017.' ista: 'Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2017. Data from: Error-robust modes of the retinal population code, Dryad, 10.5061/dryad.1f1rc.' mla: 'Prentice, Jason, et al. Data from: Error-Robust Modes of the Retinal Population Code. Dryad, 2017, doi:10.5061/dryad.1f1rc.' short: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, (2017). date_created: 2021-07-23T11:34:34Z date_published: 2017-10-18T00:00:00Z date_updated: 2023-02-21T16:34:41Z day: '18' department: - _id: GaTk doi: 10.5061/dryad.1f1rc main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.1f1rc month: '10' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '1197' relation: used_in_publication status: public status: public title: 'Data from: Error-robust modes of the retinal population code' type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2017' ... --- _id: '680' abstract: - lang: eng text: In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics. article_number: e1005582 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: Paul full_name: Masset, Paul last_name: Masset - first_name: Boris full_name: Gutkin, Boris last_name: Gutkin - first_name: Sophie full_name: Denève, Sophie last_name: Denève citation: ama: Chalk MJ, Masset P, Gutkin B, Denève S. Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 2017;13(6). doi:10.1371/journal.pcbi.1005582 apa: Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582 chicago: Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005582. ieee: M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts divisive reshaping of receptive fields,” PLoS Computational Biology, vol. 13, no. 6. Public Library of Science, 2017. ista: Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 13(6), e1005582. mla: Chalk, Matthew J., et al. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” PLoS Computational Biology, vol. 13, no. 6, e1005582, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005582. short: M.J. Chalk, P. Masset, B. Gutkin, S. Denève, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:47:53Z date_published: 2017-06-01T00:00:00Z date_updated: 2023-02-23T14:10:54Z day: '01' ddc: - '571' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005582 file: - access_level: open_access checksum: 796a1026076af6f4405a47d985bc7b68 content_type: application/pdf creator: system date_created: 2018-12-12T10:07:47Z date_updated: 2020-07-14T12:47:40Z file_id: '4645' file_name: IST-2017-898-v1+1_journal.pcbi.1005582.pdf file_size: 14555676 relation: main_file file_date_updated: 2020-07-14T12:47:40Z has_accepted_license: '1' intvolume: ' 13' issue: '6' language: - iso: eng month: '06' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '7035' pubrep_id: '898' quality_controlled: '1' related_material: record: - id: '9855' relation: research_data status: public scopus_import: 1 status: public title: Sensory noise predicts divisive reshaping of receptive fields 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: 13 year: '2017' ... --- _id: '9855' abstract: - lang: eng text: Includes derivation of optimal estimation algorithm, generalisation to non-poisson noise statistics, correlated input noise, and implementation of in a multi-layer neural network. article_processing_charge: No 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: Paul full_name: Masset, Paul last_name: Masset - first_name: Boris full_name: Gutkin, Boris last_name: Gutkin - first_name: Sophie full_name: Denève, Sophie last_name: Denève citation: ama: Chalk MJ, Masset P, Gutkin B, Denève S. Supplementary appendix. 2017. doi:10.1371/journal.pcbi.1005582.s001 apa: Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Supplementary appendix. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582.s001 chicago: Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Supplementary Appendix.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005582.s001. ieee: M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Supplementary appendix.” Public Library of Science, 2017. ista: Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Supplementary appendix, Public Library of Science, 10.1371/journal.pcbi.1005582.s001. mla: Chalk, Matthew J., et al. Supplementary Appendix. Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005582.s001. short: M.J. Chalk, P. Masset, B. Gutkin, S. Denève, (2017). date_created: 2021-08-10T07:05:10Z date_published: 2017-06-01T00:00:00Z date_updated: 2023-02-23T12:52:17Z day: '01' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005582.s001 month: '06' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '680' relation: used_in_publication status: public status: public title: Supplementary appendix type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2017' ... --- _id: '666' abstract: - lang: eng text: Antibiotics elicit drastic changes in microbial gene expression, including the induction of stress response genes. While certain stress responses are known to “cross-protect” bacteria from other stressors, it is unclear whether cellular responses to antibiotics have a similar protective role. By measuring the genome-wide transcriptional response dynamics of Escherichia coli to four antibiotics, we found that trimethoprim induces a rapid acid stress response that protects bacteria from subsequent exposure to acid. Combining microfluidics with time-lapse imaging to monitor survival and acid stress response in single cells revealed that the noisy expression of the acid resistance operon gadBC correlates with single-cell survival. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. The seemingly random single-cell survival under acid stress can therefore be predicted from gadBC expression and rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap for identifying the molecular mechanisms of single-cell cross-protection between antibiotics and other stressors. article_processing_charge: Yes (in subscription journal) author: - first_name: Karin full_name: Mitosch, Karin id: 39B66846-F248-11E8-B48F-1D18A9856A87 last_name: Mitosch - first_name: Georg full_name: Rieckh, Georg id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87 last_name: Rieckh - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Mitosch K, Rieckh G, Bollenbach MT. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 2017;4(4):393-403. doi:10.1016/j.cels.2017.03.001 apa: Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2017.03.001 chicago: Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems. Cell Press, 2017. https://doi.org/10.1016/j.cels.2017.03.001. ieee: K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” Cell Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017. ista: Mitosch K, Rieckh G, Bollenbach MT. 2017. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 4(4), 393–403. mla: Mitosch, Karin, et al. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems, vol. 4, no. 4, Cell Press, 2017, pp. 393–403, doi:10.1016/j.cels.2017.03.001. short: K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403. date_created: 2018-12-11T11:47:48Z date_published: 2017-04-26T00:00:00Z date_updated: 2023-09-07T12:00:25Z day: '26' ddc: - '576' - '610' department: - _id: ToBo - _id: GaTk doi: 10.1016/j.cels.2017.03.001 ec_funded: 1 file: - access_level: open_access checksum: 04ff20011c3d9a601c514aa999a5fe1a content_type: application/pdf creator: system date_created: 2018-12-12T10:13:54Z date_updated: 2020-07-14T12:47:35Z file_id: '5041' file_name: IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf file_size: 2438660 relation: main_file file_date_updated: 2020-07-14T12:47:35Z has_accepted_license: '1' intvolume: ' 4' issue: '4' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '04' oa: 1 oa_version: Published Version page: 393 - 403 project: - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Cell Systems publication_identifier: issn: - '24054712' publication_status: published publisher: Cell Press publist_id: '7061' pubrep_id: '901' quality_controlled: '1' related_material: record: - id: '818' relation: dissertation_contains status: public scopus_import: 1 status: public title: Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment 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: 4 year: '2017' ... --- _id: '2016' abstract: - lang: eng text: The Ising model is one of the simplest and most famous models of interacting systems. It was originally proposed to model ferromagnetic interactions in statistical physics and is now widely used to model spatial processes in many areas such as ecology, sociology, and genetics, usually without testing its goodness-of-fit. Here, we propose an exact goodness-of-fit test for the finite-lattice Ising model. The theory of Markov bases has been developed in algebraic statistics for exact goodness-of-fit testing using a Monte Carlo approach. However, this beautiful theory has fallen short of its promise for applications, because finding a Markov basis is usually computationally intractable. We develop a Monte Carlo method for exact goodness-of-fit testing for the Ising model which avoids computing a Markov basis and also leads to a better connectivity of the Markov chain and hence to a faster convergence. We show how this method can be applied to analyze the spatial organization of receptors on the cell membrane. article_processing_charge: No author: - first_name: Abraham full_name: Martin Del Campo Sanchez, Abraham last_name: Martin Del Campo Sanchez - first_name: Sarah A full_name: Cepeda Humerez, Sarah A id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87 last_name: Cepeda Humerez - first_name: Caroline full_name: Uhler, Caroline id: 49ADD78E-F248-11E8-B48F-1D18A9856A87 last_name: Uhler orcid: 0000-0002-7008-0216 citation: ama: Martin Del Campo Sanchez A, Cepeda Humerez SA, Uhler C. Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. 2017;44(2):285-306. doi:10.1111/sjos.12251 apa: Martin Del Campo Sanchez, A., Cepeda Humerez, S. A., & Uhler, C. (2017). Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. Wiley-Blackwell. https://doi.org/10.1111/sjos.12251 chicago: Martin Del Campo Sanchez, Abraham, Sarah A Cepeda Humerez, and Caroline Uhler. “Exact Goodness-of-Fit Testing for the Ising Model.” Scandinavian Journal of Statistics. Wiley-Blackwell, 2017. https://doi.org/10.1111/sjos.12251. ieee: A. Martin Del Campo Sanchez, S. A. Cepeda Humerez, and C. Uhler, “Exact goodness-of-fit testing for the Ising model,” Scandinavian Journal of Statistics, vol. 44, no. 2. Wiley-Blackwell, pp. 285–306, 2017. ista: Martin Del Campo Sanchez A, Cepeda Humerez SA, Uhler C. 2017. Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. 44(2), 285–306. mla: Martin Del Campo Sanchez, Abraham, et al. “Exact Goodness-of-Fit Testing for the Ising Model.” Scandinavian Journal of Statistics, vol. 44, no. 2, Wiley-Blackwell, 2017, pp. 285–306, doi:10.1111/sjos.12251. short: A. Martin Del Campo Sanchez, S.A. Cepeda Humerez, C. Uhler, Scandinavian Journal of Statistics 44 (2017) 285–306. date_created: 2018-12-11T11:55:13Z date_published: 2017-06-01T00:00:00Z date_updated: 2023-09-19T15:13:27Z day: '01' department: - _id: GaTk doi: 10.1111/sjos.12251 external_id: arxiv: - '1410.1242' isi: - '000400985000001' intvolume: ' 44' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1410.1242 month: '06' oa: 1 oa_version: Preprint page: 285 - 306 publication: Scandinavian Journal of Statistics publication_identifier: issn: - '03036898' publication_status: published publisher: Wiley-Blackwell publist_id: '5060' quality_controlled: '1' related_material: record: - id: '6473' relation: part_of_dissertation status: public scopus_import: '1' status: public title: Exact goodness-of-fit testing for the Ising model type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 44 year: '2017' ... --- _id: '1104' abstract: - lang: eng text: In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems. article_number: '1964' article_processing_charge: No author: - first_name: Stephane full_name: Deny, Stephane last_name: Deny - first_name: Ulisse full_name: Ferrari, Ulisse last_name: Ferrari - first_name: Emilie full_name: Mace, Emilie last_name: Mace - first_name: Pierre full_name: Yger, Pierre last_name: Yger - first_name: Romain full_name: Caplette, Romain last_name: Caplette - first_name: Serge full_name: Picaud, Serge last_name: Picaud - 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 citation: ama: Deny S, Ferrari U, Mace E, et al. Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-02159-y apa: Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S., … Marre, O. (2017). Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-02159-y chicago: Deny, Stephane, Ulisse Ferrari, Emilie Mace, Pierre Yger, Romain Caplette, Serge Picaud, Gašper Tkačik, and Olivier Marre. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/s41467-017-02159-y. ieee: S. Deny et al., “Multiplexed computations in retinal ganglion cells of a single type,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017. ista: Deny S, Ferrari U, Mace E, Yger P, Caplette R, Picaud S, Tkačik G, Marre O. 2017. Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. 8(1), 1964. mla: Deny, Stephane, et al. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” Nature Communications, vol. 8, no. 1, 1964, Nature Publishing Group, 2017, doi:10.1038/s41467-017-02159-y. short: S. Deny, U. Ferrari, E. Mace, P. Yger, R. Caplette, S. Picaud, G. Tkačik, O. Marre, Nature Communications 8 (2017). date_created: 2018-12-11T11:50:10Z date_published: 2017-12-06T00:00:00Z date_updated: 2023-09-20T11:41:19Z day: '06' ddc: - '571' department: - _id: GaTk doi: 10.1038/s41467-017-02159-y ec_funded: 1 external_id: isi: - '000417241200004' file: - access_level: open_access content_type: application/pdf creator: system date_created: 2018-12-12T10:16:06Z date_updated: 2018-12-12T10:16:06Z file_id: '5191' file_name: IST-2018-921-v1+1_s41467-017-02159-y.pdf file_size: 2872887 relation: main_file file_date_updated: 2018-12-12T10:16:06Z has_accepted_license: '1' intvolume: ' 8' isi: 1 issue: '1' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: 25CD3DD2-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '604102' name: Localization of ion channels and receptors by two and three-dimensional immunoelectron microscopic approaches - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: Nature Communications publication_identifier: issn: - '20411723' publication_status: published publisher: Nature Publishing Group publist_id: '6266' pubrep_id: '921' quality_controlled: '1' scopus_import: '1' status: public title: Multiplexed computations in retinal ganglion cells of a single type 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: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 8 year: '2017' ... --- _id: '993' abstract: - lang: eng text: In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system’s aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development. article_number: '15140' article_processing_charge: Yes (in subscription journal) author: - first_name: Anna full_name: Levina (Martius), Anna id: 35AF8020-F248-11E8-B48F-1D18A9856A87 last_name: Levina (Martius) - first_name: Viola full_name: Priesemann, Viola last_name: Priesemann citation: ama: Levina (Martius) A, Priesemann V. Subsampling scaling. Nature Communications. 2017;8. doi:10.1038/ncomms15140 apa: Levina (Martius), A., & Priesemann, V. (2017). Subsampling scaling. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms15140 chicago: Levina (Martius), Anna, and Viola Priesemann. “Subsampling Scaling.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/ncomms15140. ieee: A. Levina (Martius) and V. Priesemann, “Subsampling scaling,” Nature Communications, vol. 8. Nature Publishing Group, 2017. ista: Levina (Martius) A, Priesemann V. 2017. Subsampling scaling. Nature Communications. 8, 15140. mla: Levina (Martius), Anna, and Viola Priesemann. “Subsampling Scaling.” Nature Communications, vol. 8, 15140, Nature Publishing Group, 2017, doi:10.1038/ncomms15140. short: A. Levina (Martius), V. Priesemann, Nature Communications 8 (2017). date_created: 2018-12-11T11:49:35Z date_published: 2017-05-04T00:00:00Z date_updated: 2023-09-22T09:54:07Z day: '04' ddc: - '005' - '571' department: - _id: GaTk - _id: JoCs doi: 10.1038/ncomms15140 ec_funded: 1 external_id: isi: - '000400560700001' file: - access_level: open_access checksum: 9880212f8c4c53404c7c6fbf9023c53a content_type: application/pdf creator: system date_created: 2018-12-12T10:15:05Z date_updated: 2020-07-14T12:48:19Z file_id: '5122' file_name: IST-2017-819-v1+1_2017_Levina_SubsamplingScaling.pdf file_size: 746224 relation: main_file file_date_updated: 2020-07-14T12:48:19Z has_accepted_license: '1' intvolume: ' 8' isi: 1 language: - iso: eng month: '05' 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: Nature Communications publication_identifier: issn: - '20411723' publication_status: published publisher: Nature Publishing Group publist_id: '6406' pubrep_id: '819' quality_controlled: '1' scopus_import: '1' status: public title: Subsampling scaling 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: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 8 year: '2017' ... --- _id: '955' abstract: - lang: eng text: 'Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.' article_number: '216' article_processing_charge: Yes (in subscription journal) 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: 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, Barton NH, Tkačik G. Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-00238-8 apa: Friedlander, T., Prizak, R., Barton, N. H., & Tkačik, G. (2017). Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-00238-8 chicago: Friedlander, Tamar, Roshan Prizak, Nicholas H Barton, and Gašper Tkačik. “Evolution of New Regulatory Functions on Biophysically Realistic Fitness Landscapes.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/s41467-017-00238-8. ieee: T. Friedlander, R. Prizak, N. H. Barton, and G. Tkačik, “Evolution of new regulatory functions on biophysically realistic fitness landscapes,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017. ista: Friedlander T, Prizak R, Barton NH, Tkačik G. 2017. Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. 8(1), 216. mla: Friedlander, Tamar, et al. “Evolution of New Regulatory Functions on Biophysically Realistic Fitness Landscapes.” Nature Communications, vol. 8, no. 1, 216, Nature Publishing Group, 2017, doi:10.1038/s41467-017-00238-8. short: T. Friedlander, R. Prizak, N.H. Barton, G. Tkačik, Nature Communications 8 (2017). date_created: 2018-12-11T11:49:23Z date_published: 2017-08-09T00:00:00Z date_updated: 2023-09-22T10:00:49Z day: '09' ddc: - '539' - '576' department: - _id: GaTk - _id: NiBa doi: 10.1038/s41467-017-00238-8 ec_funded: 1 external_id: isi: - '000407198800005' file: - access_level: open_access checksum: 29a1b5db458048d3bd5c67e0e2a56818 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:14Z date_updated: 2020-07-14T12:48:16Z file_id: '5064' file_name: IST-2017-864-v1+1_s41467-017-00238-8.pdf file_size: 998157 relation: main_file - access_level: open_access checksum: 7b78401e52a576cf3e6bbf8d0abadc17 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:15Z date_updated: 2020-07-14T12:48:16Z file_id: '5065' file_name: IST-2017-864-v1+2_41467_2017_238_MOESM1_ESM.pdf file_size: 9715993 relation: main_file file_date_updated: 2020-07-14T12:48:16Z has_accepted_license: '1' intvolume: ' 8' isi: 1 issue: '1' 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_identifier: issn: - '20411723' publication_status: published publisher: Nature Publishing Group publist_id: '6459' pubrep_id: '864' quality_controlled: '1' related_material: record: - id: '6071' relation: dissertation_contains status: public scopus_import: '1' status: public title: Evolution of new regulatory functions on biophysically realistic fitness landscapes 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: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 8 year: '2017' ... --- _id: '959' abstract: - lang: eng text: In this work it is shown that scale-free tails in metabolic flux distributions inferred in stationary models are an artifact due to reactions involved in thermodynamically unfeasible cycles, unbounded by physical constraints and in principle able to perform work without expenditure of free energy. After implementing thermodynamic constraints by removing such loops, metabolic flux distributions scale meaningfully with the physical limiting factors, acquiring in turn a richer multimodal structure potentially leading to symmetry breaking while optimizing for objective functions. article_processing_charge: No 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. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419 apa: De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.95.062419 chicago: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary Metabolic Network Models via Thermodynamics.” Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.95.062419. ieee: D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017. ista: De Martino D. 2017. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 95(6), 062419. mla: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary Metabolic Network Models via Thermodynamics.” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6, American Institute of Physics, 2017, p. 062419, doi:10.1103/PhysRevE.95.062419. short: D. De Martino, Physical Review E Statistical Nonlinear and Soft Matter Physics 95 (2017) 062419. date_created: 2018-12-11T11:49:25Z date_published: 2017-06-28T00:00:00Z date_updated: 2023-09-22T09:59:01Z day: '28' department: - _id: GaTk doi: 10.1103/PhysRevE.95.062419 ec_funded: 1 external_id: isi: - '000404546400004' intvolume: ' 95' isi: 1 issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1703.00853.pdf month: '06' oa: 1 oa_version: Submitted Version page: '062419' 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_identifier: issn: - '24700045' publication_status: published publisher: American Institute of Physics publist_id: '6446' quality_controlled: '1' scopus_import: '1' status: public title: Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 95 year: '2017' ... --- _id: '947' abstract: - lang: eng text: Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximum-entropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genome-scale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximum-entropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence. article_number: '010401' article_processing_charge: No 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. Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;96(1). doi:10.1103/PhysRevE.96.010401 apa: De Martino, D., Capuani, F., & De Martino, A. (2017). Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.96.010401 chicago: De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Quantifying the Entropic Cost of Cellular Growth Control.” Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.96.010401. ieee: D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost of cellular growth control,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017. ista: De Martino D, Capuani F, De Martino A. 2017. Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . 96(1), 010401. mla: De Martino, Daniele, et al. “Quantifying the Entropic Cost of Cellular Growth Control.” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1, 010401, American Institute of Physics, 2017, doi:10.1103/PhysRevE.96.010401. short: D. De Martino, F. Capuani, A. De Martino, Physical Review E Statistical Nonlinear and Soft Matter Physics 96 (2017). date_created: 2018-12-11T11:49:21Z date_published: 2017-07-10T00:00:00Z date_updated: 2023-09-22T10:03:50Z day: '10' department: - _id: GaTk doi: 10.1103/PhysRevE.96.010401 ec_funded: 1 external_id: isi: - '000405194200002' intvolume: ' 96' isi: 1 issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1703.00219 month: '07' oa: 1 oa_version: Submitted Version 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_identifier: issn: - '24700045' publication_status: published publisher: American Institute of Physics publist_id: '6470' quality_controlled: '1' scopus_import: '1' status: public title: Quantifying the entropic cost of cellular growth control type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 96 year: '2017' ... --- _id: '943' abstract: - lang: eng text: Like many developing tissues, the vertebrate neural tube is patterned by antiparallel morphogen gradients. To understand how these inputs are interpreted, we measured morphogen signaling and target gene expression in mouse embryos and chick ex vivo assays. From these data, we derived and validated a characteristic decoding map that relates morphogen input to the positional identity of neural progenitors. Analysis of the observed responses indicates that the underlying interpretation strategy minimizes patterning errors in response to the joint input of noisy opposing gradients. We reverse-engineered a transcriptional network that provides a mechanistic basis for the observed cell fate decisions and accounts for the precision and dynamics of pattern formation. Together, our data link opposing gradient dynamics in a growing tissue to precise pattern formation. article_processing_charge: No author: - first_name: Marcin P full_name: Zagórski, Marcin P id: 343DA0DC-F248-11E8-B48F-1D18A9856A87 last_name: Zagórski orcid: 0000-0001-7896-7762 - first_name: Yoji full_name: Tabata, Yoji last_name: Tabata - first_name: Nathalie full_name: Brandenberg, Nathalie last_name: Brandenberg - first_name: Matthias full_name: Lutolf, Matthias last_name: Lutolf - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Tobias full_name: Bollenbach, Tobias last_name: Bollenbach - first_name: James full_name: Briscoe, James last_name: Briscoe - first_name: Anna full_name: Kicheva, Anna id: 3959A2A0-F248-11E8-B48F-1D18A9856A87 last_name: Kicheva orcid: 0000-0003-4509-4998 citation: ama: Zagórski MP, Tabata Y, Brandenberg N, et al. Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. 2017;356(6345):1379-1383. doi:10.1126/science.aam5887 apa: Zagórski, M. P., Tabata, Y., Brandenberg, N., Lutolf, M., Tkačik, G., Bollenbach, T., … Kicheva, A. (2017). Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aam5887 chicago: Zagórski, Marcin P, Yoji Tabata, Nathalie Brandenberg, Matthias Lutolf, Gašper Tkačik, Tobias Bollenbach, James Briscoe, and Anna Kicheva. “Decoding of Position in the Developing Neural Tube from Antiparallel Morphogen Gradients.” Science. American Association for the Advancement of Science, 2017. https://doi.org/10.1126/science.aam5887. ieee: M. P. Zagórski et al., “Decoding of position in the developing neural tube from antiparallel morphogen gradients,” Science, vol. 356, no. 6345. American Association for the Advancement of Science, pp. 1379–1383, 2017. ista: Zagórski MP, Tabata Y, Brandenberg N, Lutolf M, Tkačik G, Bollenbach T, Briscoe J, Kicheva A. 2017. Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. 356(6345), 1379–1383. mla: Zagórski, Marcin P., et al. “Decoding of Position in the Developing Neural Tube from Antiparallel Morphogen Gradients.” Science, vol. 356, no. 6345, American Association for the Advancement of Science, 2017, pp. 1379–83, doi:10.1126/science.aam5887. short: M.P. Zagórski, Y. Tabata, N. Brandenberg, M. Lutolf, G. Tkačik, T. Bollenbach, J. Briscoe, A. Kicheva, Science 356 (2017) 1379–1383. date_created: 2018-12-11T11:49:20Z date_published: 2017-06-30T00:00:00Z date_updated: 2023-09-26T15:38:05Z day: '30' department: - _id: AnKi - _id: GaTk doi: 10.1126/science.aam5887 ec_funded: 1 external_id: isi: - '000404351500036' pmid: - '28663499' intvolume: ' 356' isi: 1 issue: '6345' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568706/ month: '06' oa: 1 oa_version: Submitted Version page: 1379 - 1383 pmid: 1 project: - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation - _id: B6FC0238-B512-11E9-945C-1524E6697425 call_identifier: H2020 grant_number: '680037' name: Coordination of Patterning And Growth In the Spinal Cord - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 2524F500-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '201439' name: Developing High-Throughput Bioassays for Human Cancers in Zebrafish publication: Science publication_identifier: issn: - '00368075' publication_status: published publisher: American Association for the Advancement of Science publist_id: '6474' quality_controlled: '1' scopus_import: '1' status: public title: Decoding of position in the developing neural tube from antiparallel morphogen gradients type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 356 year: '2017' ... --- _id: '823' abstract: - lang: eng text: The resolution of a linear system with positive integer variables is a basic yet difficult computational problem with many applications. We consider sparse uncorrelated random systems parametrised by the density c and the ratio α=N/M between number of variables N and number of constraints M. By means of ensemble calculations we show that the space of feasible solutions endows a Van-Der-Waals phase diagram in the plane (c, α). We give numerical evidence that the associated computational problems become more difficult across the critical point and in particular in the coexistence region. article_number: '093404' article_processing_charge: No author: - first_name: Simona full_name: Colabrese, Simona last_name: Colabrese - 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: Luca full_name: Leuzzi, Luca last_name: Leuzzi - first_name: Enzo full_name: Marinari, Enzo last_name: Marinari citation: ama: 'Colabrese S, De Martino D, Leuzzi L, Marinari E. Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. 2017;2017(9). doi:10.1088/1742-5468/aa85c3' apa: 'Colabrese, S., De Martino, D., Leuzzi, L., & Marinari, E. (2017). Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa85c3' chicago: 'Colabrese, Simona, Daniele De Martino, Luca Leuzzi, and Enzo Marinari. “Phase Transitions in Integer Linear Problems.” Journal of Statistical Mechanics: Theory and Experiment. IOPscience, 2017. https://doi.org/10.1088/1742-5468/aa85c3.' ieee: 'S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions in integer linear problems,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9. IOPscience, 2017.' ista: 'Colabrese S, De Martino D, Leuzzi L, Marinari E. 2017. Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. 2017(9), 093404.' mla: 'Colabrese, Simona, et al. “Phase Transitions in Integer Linear Problems.” Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9, 093404, IOPscience, 2017, doi:10.1088/1742-5468/aa85c3.' short: 'S. Colabrese, D. De Martino, L. Leuzzi, E. Marinari, Journal of Statistical Mechanics: Theory and Experiment 2017 (2017).' date_created: 2018-12-11T11:48:41Z date_published: 2017-09-26T00:00:00Z date_updated: 2023-09-26T16:18:12Z day: '26' department: - _id: GaTk doi: 10.1088/1742-5468/aa85c3 ec_funded: 1 external_id: isi: - '000411842900001' intvolume: ' 2017' isi: 1 issue: '9' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1705.06303 month: '09' oa: 1 oa_version: Submitted Version project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: ' Journal of Statistical Mechanics: Theory and Experiment' publication_identifier: issn: - '17425468' publication_status: published publisher: IOPscience publist_id: '6826' quality_controlled: '1' scopus_import: '1' status: public title: Phase transitions in integer linear problems type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '730' abstract: - lang: eng text: Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible. article_processing_charge: No 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. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 2017;46:120-126. doi:10.1016/j.conb.2017.08.001 apa: Savin, C., & Tkačik, G. (2017). Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. Elsevier. https://doi.org/10.1016/j.conb.2017.08.001 chicago: Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” Current Opinion in Neurobiology. Elsevier, 2017. https://doi.org/10.1016/j.conb.2017.08.001. ieee: C. Savin and G. Tkačik, “Maximum entropy models as a tool for building precise neural controls,” Current Opinion in Neurobiology, vol. 46. Elsevier, pp. 120–126, 2017. ista: Savin C, Tkačik G. 2017. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 46, 120–126. mla: Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” Current Opinion in Neurobiology, vol. 46, Elsevier, 2017, pp. 120–26, doi:10.1016/j.conb.2017.08.001. short: C. Savin, G. Tkačik, Current Opinion in Neurobiology 46 (2017) 120–126. date_created: 2018-12-11T11:48:11Z date_published: 2017-10-01T00:00:00Z date_updated: 2023-09-28T11:32:22Z day: '01' department: - _id: GaTk doi: 10.1016/j.conb.2017.08.001 ec_funded: 1 external_id: isi: - '000416196400016' intvolume: ' 46' isi: 1 language: - iso: eng month: '10' oa_version: None page: 120 - 126 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Current Opinion in Neurobiology publication_identifier: issn: - '09594388' publication_status: published publisher: Elsevier publist_id: '6943' quality_controlled: '1' scopus_import: '1' status: public title: Maximum entropy models as a tool for building precise neural controls type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 46 year: '2017' ...