[{"intvolume":"       461","citation":{"mla":"Lombardi, Fabrizio, et al. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” <i>Neurocomputing</i>, vol. 461, Elsevier, 2021, pp. 657–66, doi:<a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">10.1016/j.neucom.2020.05.126</a>.","ama":"Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. <i>Neurocomputing</i>. 2021;461:657-666. doi:<a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">10.1016/j.neucom.2020.05.126</a>","chicago":"Lombardi, Fabrizio, Oren Shriki, Hans J Herrmann, and Lucilla de Arcangelis. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” <i>Neurocomputing</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">https://doi.org/10.1016/j.neucom.2020.05.126</a>.","apa":"Lombardi, F., Shriki, O., Herrmann, H. J., &#38; de Arcangelis, L. (2021). Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. <i>Neurocomputing</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">https://doi.org/10.1016/j.neucom.2020.05.126</a>","ista":"Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. 2021. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing. 461, 657–666.","short":"F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461 (2021) 657–666.","ieee":"F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches,” <i>Neurocomputing</i>, vol. 461. Elsevier, pp. 657–666, 2021."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.02.03.930966"}],"status":"public","ec_funded":1,"acknowledgement":"LdA would like to acknowledge the financial support from MIUR-PRIN2017 WZFTZP and VALERE:VAnviteLli pEr la RicErca 2019. FL acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 754411. HJH would like to thank the Agencies CAPES and FUNCAP for financial support.","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"lang":"eng","text":"Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. A distinctive attribute of systems at criticality is the presence of long-range correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow-band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, brain activity is a broadband phenomenon, and a significant piece of information useful to precisely discriminate between normal (critical) and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal cortical dynamics. Here we propose to characterize the temporal correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that the avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms."}],"title":"Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches","_id":"7463","publication_identifier":{"issn":["0925-2312"],"eissn":["1872-8286"]},"scopus_import":"1","isi":1,"oa_version":"Preprint","article_type":"original","external_id":{"isi":["000704086300015"]},"project":[{"name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"}],"type":"journal_article","date_published":"2021-05-13T00:00:00Z","volume":461,"month":"05","quality_controlled":"1","oa":1,"article_processing_charge":"No","doi":"10.1016/j.neucom.2020.05.126","publisher":"Elsevier","date_updated":"2023-08-04T10:46:29Z","publication":"Neurocomputing","date_created":"2020-02-06T16:09:14Z","day":"13","publication_status":"published","author":[{"id":"A057D288-3E88-11E9-986D-0CF4E5697425","first_name":"Fabrizio","full_name":"Lombardi, Fabrizio","orcid":"0000-0003-2623-5249","last_name":"Lombardi"},{"first_name":"Oren","full_name":"Shriki, Oren","last_name":"Shriki"},{"last_name":"Herrmann","first_name":"Hans J","full_name":"Herrmann, Hans J"},{"full_name":"de Arcangelis, Lucilla","first_name":"Lucilla","last_name":"de Arcangelis"}],"page":"657-666","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"year":"2021"},{"volume":109,"date_published":"2021-04-07T00:00:00Z","type":"journal_article","project":[{"name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"external_id":{"isi":["000637809600006"]},"oa_version":"Preprint","publication":"Neuron","date_updated":"2025-06-30T13:21:05Z","publisher":"Cell Press","doi":"10.1016/j.neuron.2021.01.020","article_processing_charge":"No","quality_controlled":"1","oa":1,"month":"04","page":"1227-1241.e5","author":[{"last_name":"Mlynarski","full_name":"Mlynarski, Wiktor F","first_name":"Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87"},{"id":"4171253A-F248-11E8-B48F-1D18A9856A87","first_name":"Michal","full_name":"Hledik, Michal","last_name":"Hledik"},{"full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","orcid":"0000-0002-1287-3779","last_name":"Sokolowski"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}],"publication_status":"published","day":"07","date_created":"2020-02-28T11:00:12Z","year":"2021","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"15020"}],"link":[{"description":"News on IST Homepage","url":"https://ist.ac.at/en/news/can-evolution-be-predicted/","relation":"press_release"}]},"department":[{"_id":"GaTk"}],"ec_funded":1,"status":"public","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/848374"}],"citation":{"apa":"Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>","ista":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.","short":"W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5.","ieee":"W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021.","mla":"Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>.","ama":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>","chicago":"Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>."},"intvolume":"       109","_id":"7553","title":"Statistical analysis and optimality of neural systems","abstract":[{"text":"Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.","lang":"eng"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","acknowledgement":"The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018.","issue":"7","isi":1,"scopus_import":"1"},{"type":"preprint","date_published":"2021-09-29T00:00:00Z","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"call_identifier":"H2020","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385"},{"grant_number":"281511","_id":"257A4776-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex"},{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"ec_funded":1,"status":"public","oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/2021.09.28.460602"}],"citation":{"chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, n.d. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>.","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>.","ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>","ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory.","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., &#38; Savin, C. (n.d.). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv, <a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>."},"abstract":[{"lang":"eng","text":"Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain."}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","article_processing_charge":"No","publication":"bioRxiv","_id":"10077","date_updated":"2024-03-25T23:30:09Z","title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","publisher":"Cold Spring Harbor Laboratory","doi":"10.1101/2021.09.28.460602","month":"09","acknowledgement":"We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for the acquisition of the data. We thank Federico Stella for comments on an earlier version of the manuscript. MN was supported by European Union Horizon 2020 grant 665385, JC was supported by European Research Council consolidator grant 281511, GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01, by the National Science Foundation under NSF Award No. 1922658 and a Google faculty award.","oa":1,"author":[{"orcid":"0000-0001-8849-6570","last_name":"Nardin","full_name":"Nardin, Michele","first_name":"Michele","id":"30BD0376-F248-11E8-B48F-1D18A9856A87"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","first_name":"Jozsef L","full_name":"Csicsvari, Jozsef L","orcid":"0000-0002-5193-4036","last_name":"Csicsvari"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper"},{"last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina"}],"date_created":"2021-10-04T06:23:34Z","publication_status":"submitted","day":"29","language":[{"iso":"eng"}],"year":"2021","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"11932"}]},"department":[{"_id":"GradSch"},{"_id":"JoCs"},{"_id":"GaTk"}],"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)"}},{"file_date_updated":"2022-05-16T08:53:11Z","scopus_import":"1","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"issue":"12","acknowledgement":"Computational resources for the study were provided by the Institute of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20.","pmid":1,"title":"Dynamic maximum entropy provides accurate approximation of structured population dynamics","_id":"10535","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment."}],"intvolume":"        17","citation":{"ista":"Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 17(12), e1009661.","apa":"Bodova, K., Szep, E., &#38; Barton, N. H. (2021). Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>","ieee":"K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate approximation of structured population dynamics,” <i>PLoS Computational Biology</i>, vol. 17, no. 12. Public Library of Science, 2021.","short":"K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).","ama":"Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. 2021;17(12). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>","mla":"Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>, vol. 17, no. 12, e1009661, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>.","chicago":"Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>."},"has_accepted_license":"1","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"NiBa"},{"_id":"GaTk"}],"year":"2021","language":[{"iso":"eng"}],"ddc":["570"],"day":"01","publication_status":"published","arxiv":1,"date_created":"2021-12-12T23:01:27Z","acknowledged_ssus":[{"_id":"ScienComp"}],"author":[{"last_name":"Bod'ová","orcid":"0000-0002-7214-0171","first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína"},{"id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","first_name":"Eniko","full_name":"Szep, Eniko","last_name":"Szep"},{"first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton"}],"oa":1,"file":[{"access_level":"open_access","file_name":"2021_PLOsComBio_Bodova.pdf","success":1,"checksum":"dcd185d4f7e0acee25edf1d6537f447e","date_updated":"2022-05-16T08:53:11Z","file_size":2299486,"content_type":"application/pdf","file_id":"11383","date_created":"2022-05-16T08:53:11Z","creator":"dernst","relation":"main_file"}],"quality_controlled":"1","article_number":"e1009661","month":"12","date_updated":"2022-08-01T10:48:04Z","doi":"10.1371/journal.pcbi.1009661","publisher":"Public Library of Science","publication":"PLoS Computational Biology","article_processing_charge":"No","oa_version":"Published Version","external_id":{"pmid":["34851948"],"arxiv":["2102.03669"]},"article_type":"original","type":"journal_article","date_published":"2021-12-01T00:00:00Z","volume":17},{"status":"public","type":"preprint","date_published":"2021-12-27T00:00:00Z","citation":{"chicago":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>.","mla":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, 2112.13558, doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>.","ama":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>","ieee":"B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion process,” <i>arXiv</i>. .","short":"B. Kavcic, G. Tkačik, ArXiv (n.d.).","apa":"Kavcic, B., &#38; Tkačik, G. (n.d.). Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>","ista":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. arXiv, 2112.13558."},"has_accepted_license":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2112.13558"}],"external_id":{"arxiv":["2112.13558"]},"oa_version":"Preprint","title":"Token-driven totally asymmetric simple exclusion process","doi":"10.48550/arXiv.2112.13558","date_updated":"2023-05-03T10:54:05Z","_id":"10579","publication":"arXiv","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a \"token\" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations."}],"oa":1,"article_number":"2112.13558","acknowledgement":"B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for their help with the usage of the cluster. B.K. additionally thanks Călin Guet and his group for help and advice. We thank M. Hennessey-Wesen for constructive comments on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting a typographical error in Eq. (49) in the preprint version of this paper.","month":"12","author":[{"full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","first_name":"Bor","last_name":"Kavcic","orcid":"0000-0001-6041-254X"},{"orcid":"0000-0002-6699-1455","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"}],"day":"27","publication_status":"submitted","arxiv":1,"date_created":"2021-12-28T06:52:09Z","year":"2021","ddc":["530"],"language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"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","date_published":"2021-05-20T00:00:00Z","project":[{"grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships"}],"volume":24,"article_type":"original","oa_version":"Preprint","external_id":{"isi":["000652577300003"]},"date_updated":"2023-08-08T13:51:14Z","publisher":"Springer Nature","doi":"10.1038/s41593-021-00846-0","publication":"Nature Neuroscience","article_processing_charge":"No","quality_controlled":"1","oa":1,"month":"05","page":"998-1009","author":[{"last_name":"Mlynarski","first_name":"Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","full_name":"Mlynarski, Wiktor F"},{"last_name":"Hermundstad","full_name":"Hermundstad, Ann M.","first_name":"Ann M."}],"day":"20","publication_status":"published","date_created":"2021-05-30T22:01:24Z","year":"2021","language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"status":"public","ec_funded":1,"intvolume":"        24","citation":{"ama":"Mlynarski WF, Hermundstad AM. Efficient and adaptive sensory codes. <i>Nature Neuroscience</i>. 2021;24:998-1009. doi:<a href=\"https://doi.org/10.1038/s41593-021-00846-0\">10.1038/s41593-021-00846-0</a>","mla":"Mlynarski, Wiktor F., and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” <i>Nature Neuroscience</i>, vol. 24, Springer Nature, 2021, pp. 998–1009, doi:<a href=\"https://doi.org/10.1038/s41593-021-00846-0\">10.1038/s41593-021-00846-0</a>.","chicago":"Mlynarski, Wiktor F, and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” <i>Nature Neuroscience</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41593-021-00846-0\">https://doi.org/10.1038/s41593-021-00846-0</a>.","ista":"Mlynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes. Nature Neuroscience. 24, 998–1009.","apa":"Mlynarski, W. F., &#38; Hermundstad, A. M. (2021). Efficient and adaptive sensory codes. <i>Nature Neuroscience</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41593-021-00846-0\">https://doi.org/10.1038/s41593-021-00846-0</a>","ieee":"W. F. Mlynarski and A. M. Hermundstad, “Efficient and adaptive sensory codes,” <i>Nature Neuroscience</i>, vol. 24. Springer Nature, pp. 998–1009, 2021.","short":"W.F. Mlynarski, A.M. Hermundstad, Nature Neuroscience 24 (2021) 998–1009."},"main_file_link":[{"url":"https://doi.org/10.1101/669200 ","open_access":"1"}],"title":"Efficient and adaptive sensory codes","_id":"9439","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"lang":"eng","text":"The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks."}],"acknowledgement":"We thank D. Kastner and T. Münch for generously providing figures from their work. We also thank V. Jayaraman, M. Noorman, T. Ma, and K. Krishnamurthy for useful discussions and feedback on the manuscript. W.F.M. was funded by the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie Grant Agreement No. 754411. A.M.H. was supported by the Howard Hughes Medical Institute.","publication_identifier":{"eissn":["1546-1726"],"issn":["1097-6256"]},"scopus_import":"1","isi":1},{"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"lang":"eng","text":"Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science."}],"title":"Sequential and switchable patterning for studying cellular processes under spatiotemporal control","_id":"9822","pmid":1,"acknowledgement":"We would like to thank Charlott Leu for the production of our chromium wafers, Louise Ritter for her contribution of the IF stainings in Figure 4, Shokoufeh Teymouri for her help with the Bioinert coated slides, and finally Prof. Dr. Joachim Rädler for his valuable scientific guidance.","status":"public","ec_funded":1,"intvolume":"        13","citation":{"mla":"Zisis, Themistoklis, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30, American Chemical Society, 2021, pp. 35545–35560, doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>.","ama":"Zisis T, Schwarz J, Balles M, et al. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. 2021;13(30):35545–35560. doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>","chicago":"Zisis, Themistoklis, Jan Schwarz, Miriam Balles, Maibritt Kretschmer, Maria Nemethova, Remy P Chait, Robert Hauschild, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>.","apa":"Zisis, T., Schwarz, J., Balles, M., Kretschmer, M., Nemethova, M., Chait, R. P., … Zahler, S. (2021). Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>","ista":"Zisis T, Schwarz J, Balles M, Kretschmer M, Nemethova M, Chait RP, Hauschild R, Lange J, Guet CC, Sixt MK, Zahler S. 2021. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. 13(30), 35545–35560.","short":"T. Zisis, J. Schwarz, M. Balles, M. Kretschmer, M. Nemethova, R.P. Chait, R. Hauschild, J. Lange, C.C. Guet, M.K. Sixt, S. Zahler, ACS Applied Materials and Interfaces 13 (2021) 35545–35560.","ieee":"T. Zisis <i>et al.</i>, “Sequential and switchable patterning for studying cellular processes under spatiotemporal control,” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30. American Chemical Society, pp. 35545–35560, 2021."},"has_accepted_license":"1","scopus_import":"1","isi":1,"file_date_updated":"2021-08-09T09:44:03Z","issue":"30","publication_identifier":{"issn":["19448244"],"eissn":["19448252"]},"article_processing_charge":"Yes (in subscription journal)","doi":"10.1021/acsami.1c09850","date_updated":"2023-08-10T14:22:48Z","publisher":"American Chemical Society","publication":"ACS Applied Materials and Interfaces","month":"08","oa":1,"file":[{"date_updated":"2021-08-09T09:44:03Z","file_size":7123293,"file_name":"2021_ACSAppliedMaterialsAndInterfaces_Zisis.pdf","access_level":"open_access","success":1,"checksum":"b043a91d9f9200e467b970b692687ed3","relation":"main_file","creator":"asandaue","content_type":"application/pdf","file_id":"9833","date_created":"2021-08-09T09:44:03Z"}],"quality_controlled":"1","type":"journal_article","project":[{"name":"Cellular navigation along spatial gradients","call_identifier":"H2020","grant_number":"724373","_id":"25FE9508-B435-11E9-9278-68D0E5697425"}],"date_published":"2021-08-04T00:00:00Z","volume":13,"oa_version":"Published Version","article_type":"original","external_id":{"pmid":["34283577"],"isi":["000683741400026"]},"year":"2021","language":[{"iso":"eng"}],"ddc":["620","570"],"department":[{"_id":"MiSi"},{"_id":"GaTk"},{"_id":"Bio"},{"_id":"CaGu"}],"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)"},"author":[{"last_name":"Zisis","first_name":"Themistoklis","full_name":"Zisis, Themistoklis"},{"last_name":"Schwarz","full_name":"Schwarz, Jan","first_name":"Jan","id":"346C1EC6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Balles","full_name":"Balles, Miriam","first_name":"Miriam"},{"first_name":"Maibritt","full_name":"Kretschmer, Maibritt","last_name":"Kretschmer"},{"first_name":"Maria","id":"34E27F1C-F248-11E8-B48F-1D18A9856A87","full_name":"Nemethova, Maria","last_name":"Nemethova"},{"last_name":"Chait","orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","full_name":"Chait, Remy P"},{"first_name":"Robert","id":"4E01D6B4-F248-11E8-B48F-1D18A9856A87","full_name":"Hauschild, Robert","last_name":"Hauschild","orcid":"0000-0001-9843-3522"},{"last_name":"Lange","first_name":"Janina","full_name":"Lange, Janina"},{"first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","last_name":"Guet","orcid":"0000-0001-6220-2052"},{"id":"41E9FBEA-F248-11E8-B48F-1D18A9856A87","first_name":"Michael K","full_name":"Sixt, Michael K","last_name":"Sixt","orcid":"0000-0002-4561-241X"},{"full_name":"Zahler, Stefan","first_name":"Stefan","last_name":"Zahler"}],"page":"35545–35560","date_created":"2021-08-08T22:01:28Z","day":"04","publication_status":"published"},{"isi":1,"scopus_import":"1","publication_identifier":{"eissn":["1941-0476"],"issn":["1053-587X"]},"_id":"9828","title":"Fast and accurate amplitude demodulation of wideband signals","abstract":[{"text":"Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals.","lang":"eng"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","acknowledgement":"The author thanks his colleagues K. Huszár and G. Tkačik for valuable discussions and comments on the manuscript.","status":"public","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2102.04832"}],"citation":{"chicago":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>.","ama":"Gabrielaitis M. Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. 2021;69:4039-4054. doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>","mla":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>, vol. 69, Institute of Electrical and Electronics Engineers, 2021, pp. 4039–54, doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>.","ieee":"M. Gabrielaitis, “Fast and accurate amplitude demodulation of wideband signals,” <i>IEEE Transactions on Signal Processing</i>, vol. 69. Institute of Electrical and Electronics Engineers, pp. 4039–4054, 2021.","short":"M. Gabrielaitis, IEEE Transactions on Signal Processing 69 (2021) 4039–4054.","ista":"Gabrielaitis M. 2021. Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. 69, 4039–4054.","apa":"Gabrielaitis, M. (2021). Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>"},"intvolume":"        69","year":"2021","language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"page":"4039 - 4054","author":[{"orcid":"0000-0002-7758-2016","last_name":"Gabrielaitis","id":"4D5B0CBC-F248-11E8-B48F-1D18A9856A87","first_name":"Mantas","full_name":"Gabrielaitis, Mantas"}],"publication_status":"published","day":"09","arxiv":1,"date_created":"2021-08-08T22:01:31Z","publication":"IEEE Transactions on Signal Processing","doi":"10.1109/TSP.2021.3087899","publisher":"Institute of Electrical and Electronics Engineers","date_updated":"2023-08-10T14:19:33Z","article_processing_charge":"No","quality_controlled":"1","oa":1,"month":"06","volume":69,"date_published":"2021-06-09T00:00:00Z","type":"journal_article","oa_version":"Preprint","article_type":"original","external_id":{"isi":["000682123900002"],"arxiv":["2102.04832"]}},{"status":"public","ec_funded":1,"date_published":"2021-08-17T00:00:00Z","type":"preprint","project":[{"grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020"},{"grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","name":"Efficient coding with biophysical realism"}],"citation":{"chicago":"Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.” arXiv, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>.","mla":"Lombardi, Fabrizio, et al. <i>Quantifying the Coexistence of Neuronal Oscillations and Avalanches</i>. arXiv, doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>.","ama":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>","short":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).","ieee":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv.","apa":"Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., &#38; De Martino, D. (n.d.). Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>","ista":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>."},"main_file_link":[{"url":"https://arxiv.org/abs/2108.06686","open_access":"1"}],"oa_version":"Preprint","external_id":{"arxiv":["2108.06686"]},"date_updated":"2022-03-22T07:53:18Z","doi":"10.48550/ARXIV.2108.06686","title":"Quantifying the coexistence of neuronal oscillations and avalanches","publisher":"arXiv","_id":"10912","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of \"closeness to criticality\", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.","lang":"eng"}],"oa":1,"acknowledgement":"FL acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411. GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone Grant\r\nNo. P34015.","month":"08","page":"37","author":[{"id":"A057D288-3E88-11E9-986D-0CF4E5697425","first_name":"Fabrizio","full_name":"Lombardi, Fabrizio","last_name":"Lombardi","orcid":"0000-0003-2623-5249"},{"first_name":"Selver","id":"F93245C4-C3CA-11E9-B4F0-C6F4E5697425","full_name":"Pepic, Selver","last_name":"Pepic"},{"first_name":"Oren","full_name":"Shriki, Oren","last_name":"Shriki"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"},{"last_name":"De Martino","full_name":"De Martino, Daniele","first_name":"Daniele"}],"day":"17","publication_status":"submitted","arxiv":1,"date_created":"2022-03-21T11:41:28Z","year":"2021","ddc":["570"],"language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}]},{"file_date_updated":"2020-08-17T07:36:57Z","isi":1,"publication_identifier":{"issn":["2041-1723"]},"acknowledgement":"We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K. Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support, which rendered this\r\nwork possible. B.K. thanks all members of Guet group for many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work. We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A. Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310 (to T.B.). Open access funding provided by\r\nProjekt DEAL.","_id":"8250","title":"Mechanisms of drug interactions between translation-inhibiting antibiotics","abstract":[{"lang":"eng","text":"Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","has_accepted_license":"1","intvolume":"        11","citation":{"chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>.","mla":"Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>, vol. 11, 4013, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>.","ama":"Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).","ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions between translation-inhibiting antibiotics,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2020). Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. 11, 4013."},"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"year":"2020","ddc":["570"],"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"8657"}]},"publication_status":"published","day":"11","date_created":"2020-08-12T09:13:50Z","author":[{"full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","first_name":"Bor","last_name":"Kavcic","orcid":"0000-0001-6041-254X"},{"orcid":"0000-0002-6699-1455","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper"},{"orcid":"0000-0003-4398-476X","last_name":"Bollenbach","full_name":"Bollenbach, Tobias","first_name":"Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"article_number":"4013","quality_controlled":"1","file":[{"date_updated":"2020-08-17T07:36:57Z","file_size":1965672,"success":1,"file_name":"2020_NatureComm_Kavcic.pdf","access_level":"open_access","checksum":"986bebb308850a55850028d3d2b5b664","relation":"main_file","creator":"dernst","content_type":"application/pdf","date_created":"2020-08-17T07:36:57Z","file_id":"8275"}],"oa":1,"month":"08","publication":"Nature Communications","date_updated":"2024-03-25T23:30:05Z","publisher":"Springer Nature","doi":"10.1038/s41467-020-17734-z","article_processing_charge":"No","article_type":"original","external_id":{"isi":["000562769300008"]},"oa_version":"Published Version","volume":11,"date_published":"2020-08-11T00:00:00Z","project":[{"call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","grant_number":"P27201-B22"},{"name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"type":"journal_article"},{"date_published":"2020-10-14T00:00:00Z","type":"dissertation","oa_version":"Published Version","article_processing_charge":"No","date_updated":"2023-09-07T13:20:48Z","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:8657","month":"10","file":[{"content_type":"application/pdf","date_created":"2020-10-15T06:41:20Z","file_id":"8663","embargo":"2021-10-06","creator":"bkavcic","relation":"main_file","file_name":"kavcicB_thesis202009.pdf","access_level":"open_access","checksum":"d708ecd62b6fcc3bc1feb483b8dbe9eb","date_updated":"2021-10-07T22:30:03Z","file_size":52636162},{"checksum":"bb35f2352a04db19164da609f00501f3","access_level":"closed","file_name":"2020b.zip","file_size":321681247,"embargo_to":"open_access","date_updated":"2021-10-07T22:30:03Z","date_created":"2020-10-15T06:41:53Z","file_id":"8664","content_type":"application/zip","creator":"bkavcic","relation":"source_file"}],"oa":1,"author":[{"orcid":"0000-0001-6041-254X","last_name":"Kavcic","first_name":"Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","full_name":"Kavcic, Bor"}],"page":"271","alternative_title":["ISTA Thesis"],"date_created":"2020-10-13T16:46:14Z","acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"M-Shop"}],"degree_awarded":"PhD","day":"14","publication_status":"published","related_material":{"record":[{"id":"7673","status":"public","relation":"part_of_dissertation"},{"id":"8250","status":"public","relation":"part_of_dissertation"}]},"language":[{"iso":"eng"}],"year":"2020","ddc":["571","530","570"],"supervisor":[{"full_name":"Tkačik, Gašper","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"0000-0002-6699-1455"},{"full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach"}],"department":[{"_id":"GaTk"}],"status":"public","citation":{"mla":"Kavcic, Bor. <i>Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8657\">10.15479/AT:ISTA:8657</a>.","ama":"Kavcic B. Perturbations of protein synthesis: from antibiotics to genetics and physiology. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8657\">10.15479/AT:ISTA:8657</a>","chicago":"Kavcic, Bor. “Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8657\">https://doi.org/10.15479/AT:ISTA:8657</a>.","apa":"Kavcic, B. (2020). <i>Perturbations of protein synthesis: from antibiotics to genetics and physiology</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8657\">https://doi.org/10.15479/AT:ISTA:8657</a>","ista":"Kavcic B. 2020. Perturbations of protein synthesis: from antibiotics to genetics and physiology. Institute of Science and Technology Austria.","short":"B. Kavcic, Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology, Institute of Science and Technology Austria, 2020.","ieee":"B. Kavcic, “Perturbations of protein synthesis: from antibiotics to genetics and physiology,” Institute of Science and Technology Austria, 2020."},"has_accepted_license":"1","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"lang":"eng","text":"Synthesis of proteins – translation – is a fundamental process of life. Quantitative studies anchor translation into the context of bacterial physiology and reveal several mathematical relationships, called “growth laws,” which capture physiological feedbacks between protein synthesis and cell growth. Growth laws describe the dependency of the ribosome abundance as a function of growth rate, which can change depending on the growth conditions. Perturbations of translation reveal that bacteria employ a compensatory strategy in which the reduced translation capability results in increased expression of the translation machinery.\r\nPerturbations of translation are achieved in various ways; clinically interesting is the application of translation-targeting antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology are often poorly understood. Bacterial responses to two or more simultaneously applied antibiotics are even more puzzling. The combined antibiotic effect determines the type of drug interaction, which ranges from synergy (the effect is stronger than expected) to antagonism (the effect is weaker) and suppression (one of the drugs loses its potency).\r\nIn the first part of this work, we systematically measure the pairwise interaction network for translation inhibitors that interfere with different steps in translation. We find that the interactions are surprisingly diverse and tend to be more antagonistic. To explore the underlying mechanisms, we begin with a minimal biophysical model of combined antibiotic action. We base this model on the kinetics of antibiotic uptake and binding together with the physiological response described by the growth laws. The biophysical model explains some drug interactions, but not all; it specifically fails to predict suppression.\r\nIn the second part of this work, we hypothesize that elusive suppressive drug interactions result from the interplay between ribosomes halted in different stages of translation. To elucidate this putative mechanism of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using in- ducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks partially causes these interactions.\r\nWe extend this approach by varying two translation bottlenecks simultaneously. This approach reveals the suppression of translocation inhibition by inhibited translation. We rationalize this effect by modeling dense traffic of ribosomes that move on transcripts in a translation factor-mediated manner. This model predicts a dissolution of traffic jams caused by inhibited translocation when the density of ribosome traffic is reduced by lowered initiation. We base this model on the growth laws and quantitative relationships between different translation and growth parameters.\r\nIn the final part of this work, we describe a set of tools aimed at quantification of physiological and translation parameters. We further develop a simple model that directly connects the abundance of a translation factor with the growth rate, which allows us to extract physiological parameters describing initiation. We demonstrate the development of tools for measuring translation rate.\r\nThis thesis showcases how a combination of high-throughput growth rate mea- surements, genetics, and modeling can reveal mechanisms of drug interactions. Furthermore, by a gradual transition from combinations of antibiotics to precise genetic interventions, we demonstrated the equivalency between genetic and chemi- cal perturbations of translation. These findings tile the path for quantitative studies of antibiotic combinations and illustrate future approaches towards the quantitative description of translation."}],"title":"Perturbations of protein synthesis: from antibiotics to genetics and physiology","_id":"8657","acknowledgement":"I thank Life Science Facilities for their continuous support with providing top-notch laboratory materials, keeping the devices humming, and coordinating the repairs and building of custom-designed laboratory equipment with the MIBA Machine shop.","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-011-4"]},"file_date_updated":"2021-10-07T22:30:03Z"},{"publication_identifier":{"eissn":["10916490"],"issn":["00278424"]},"issue":"40","file_date_updated":"2020-10-27T14:57:50Z","isi":1,"scopus_import":"1","has_accepted_license":"1","citation":{"chicago":"Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>.","mla":"Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>.","ama":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2020;117(40):25066-25073. doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>","ieee":"O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020.","short":"O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073.","apa":"Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., &#38; Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>","ista":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073."},"intvolume":"       117","status":"public","pmid":1,"acknowledgement":"We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.).","abstract":[{"lang":"eng","text":"The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"8698","title":"Learning probabilistic neural representations with randomly connected circuits","date_created":"2020-10-25T23:01:16Z","publication_status":"published","day":"06","author":[{"first_name":"Ori","full_name":"Maoz, Ori","last_name":"Maoz"},{"first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"},{"last_name":"Esteki","full_name":"Esteki, Mohamad Saleh","first_name":"Mohamad Saleh"},{"full_name":"Kiani, Roozbeh","first_name":"Roozbeh","last_name":"Kiani"},{"last_name":"Schneidman","full_name":"Schneidman, Elad","first_name":"Elad"}],"page":"25066-25073","department":[{"_id":"GaTk"}],"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)"},"ddc":["570"],"language":[{"iso":"eng"}],"year":"2020","external_id":{"isi":["000579045200012"],"pmid":["32948691"]},"article_type":"original","oa_version":"Published Version","volume":117,"date_published":"2020-10-06T00:00:00Z","type":"journal_article","month":"10","quality_controlled":"1","oa":1,"file":[{"success":1,"access_level":"open_access","file_name":"2020_PNAS_Maoz.pdf","checksum":"c6a24fdecf3f28faf447078e7a274a88","date_updated":"2020-10-27T14:57:50Z","file_size":1755359,"content_type":"application/pdf","date_created":"2020-10-27T14:57:50Z","file_id":"8713","relation":"main_file","creator":"cziletti"}],"article_processing_charge":"No","publication":"Proceedings of the National Academy of Sciences of the United States of America","date_updated":"2023-08-22T12:11:23Z","doi":"10.1073/pnas.1912804117","publisher":"National Academy of Sciences"},{"citation":{"ieee":"B. Kavcic, “Analysis scripts and research data for the paper ‘Minimal biophysical model of combined antibiotic action.’” Institute of Science and Technology Austria, 2020.","short":"B. Kavcic, (2020).","apa":"Kavcic, B. (2020). Analysis scripts and research data for the paper “Minimal biophysical model of combined antibiotic action.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">https://doi.org/10.15479/AT:ISTA:8930</a>","ista":"Kavcic B. 2020. Analysis scripts and research data for the paper ‘Minimal biophysical model of combined antibiotic action’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>.","chicago":"Kavcic, Bor. “Analysis Scripts and Research Data for the Paper ‘Minimal Biophysical Model of Combined Antibiotic Action.’” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">https://doi.org/10.15479/AT:ISTA:8930</a>.","mla":"Kavcic, Bor. <i>Analysis Scripts and Research Data for the Paper “Minimal Biophysical Model of Combined Antibiotic Action.”</i> Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>.","ama":"Kavcic B. Analysis scripts and research data for the paper “Minimal biophysical model of combined antibiotic action.” 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>"},"has_accepted_license":"1","contributor":[{"orcid":"0000-0002-6699-1455","last_name":"Tkačik","contributor_type":"supervisor","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias","last_name":"Bollenbach","contributor_type":"supervisor"}],"oa_version":"Published Version","status":"public","type":"research_data","date_published":"2020-12-10T00:00:00Z","oa":1,"file":[{"file_id":"8932","date_created":"2020-12-09T15:00:19Z","content_type":"application/zip","creator":"bkavcic","relation":"main_file","checksum":"60a818edeffaa7da1ebf5f8fbea9ba18","file_name":"PLoSCompBiol2020_datarep.zip","access_level":"open_access","success":1,"file_size":315494370,"date_updated":"2020-12-09T15:00:19Z"}],"month":"12","publisher":"Institute of Science and Technology Austria","date_updated":"2024-02-21T12:41:42Z","title":"Analysis scripts and research data for the paper \"Minimal biophysical model of combined antibiotic action\"","doi":"10.15479/AT:ISTA:8930","_id":"8930","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.","lang":"eng"}],"day":"10","date_created":"2020-12-09T15:04:02Z","author":[{"last_name":"Kavcic","orcid":"0000-0001-6041-254X","full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","first_name":"Bor"}],"keyword":["Escherichia coli","antibiotic combinations","translation","growth laws","drug interactions","bacterial physiology","translation inhibitors"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"file_date_updated":"2020-12-09T15:00:19Z","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8997"}]},"ddc":["570"],"year":"2020"},{"volume":11,"type":"journal_article","project":[{"name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"}],"date_published":"2020-11-26T00:00:00Z","external_id":{"isi":["000596849400001"],"pmid":["33324233"]},"oa_version":"Published Version","article_type":"original","article_processing_charge":"No","publication":"Frontiers in Physiology","doi":"10.3389/fphys.2020.558070","date_updated":"2023-08-24T11:00:45Z","publisher":"Frontiers","month":"11","article_number":"558070","file":[{"file_size":13380030,"date_updated":"2020-12-21T10:37:50Z","checksum":"ef9515b28c5619b7126c0f347958bcb3","success":1,"access_level":"open_access","file_name":"2020_Frontiers_Rizzo.pdf","relation":"main_file","creator":"dernst","date_created":"2020-12-21T10:37:50Z","file_id":"8961","content_type":"application/pdf"}],"oa":1,"quality_controlled":"1","author":[{"first_name":"Rossella","full_name":"Rizzo, Rossella","last_name":"Rizzo"},{"last_name":"Zhang","full_name":"Zhang, Xiyun","first_name":"Xiyun"},{"first_name":"Jilin W.J.L.","full_name":"Wang, Jilin W.J.L.","last_name":"Wang"},{"orcid":"0000-0003-2623-5249","last_name":"Lombardi","first_name":"Fabrizio","id":"A057D288-3E88-11E9-986D-0CF4E5697425","full_name":"Lombardi, Fabrizio"},{"first_name":"Plamen Ch","full_name":"Ivanov, Plamen Ch","last_name":"Ivanov"}],"date_created":"2020-12-20T23:01:18Z","publication_status":"published","day":"26","ddc":["570"],"year":"2020","language":[{"iso":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"ec_funded":1,"status":"public","has_accepted_license":"1","citation":{"chicago":"Rizzo, Rossella, Xiyun Zhang, Jilin W.J.L. Wang, Fabrizio Lombardi, and Plamen Ch Ivanov. “Network Physiology of Cortico–Muscular Interactions.” <i>Frontiers in Physiology</i>. Frontiers, 2020. <a href=\"https://doi.org/10.3389/fphys.2020.558070\">https://doi.org/10.3389/fphys.2020.558070</a>.","mla":"Rizzo, Rossella, et al. “Network Physiology of Cortico–Muscular Interactions.” <i>Frontiers in Physiology</i>, vol. 11, 558070, Frontiers, 2020, doi:<a href=\"https://doi.org/10.3389/fphys.2020.558070\">10.3389/fphys.2020.558070</a>.","ama":"Rizzo R, Zhang X, Wang JWJL, Lombardi F, Ivanov PC. Network physiology of cortico–muscular interactions. <i>Frontiers in Physiology</i>. 2020;11. doi:<a href=\"https://doi.org/10.3389/fphys.2020.558070\">10.3389/fphys.2020.558070</a>","short":"R. Rizzo, X. Zhang, J.W.J.L. Wang, F. Lombardi, P.C. Ivanov, Frontiers in Physiology 11 (2020).","ieee":"R. Rizzo, X. Zhang, J. W. J. L. Wang, F. Lombardi, and P. C. Ivanov, “Network physiology of cortico–muscular interactions,” <i>Frontiers in Physiology</i>, vol. 11. Frontiers, 2020.","apa":"Rizzo, R., Zhang, X., Wang, J. W. J. L., Lombardi, F., &#38; Ivanov, P. C. (2020). Network physiology of cortico–muscular interactions. <i>Frontiers in Physiology</i>. Frontiers. <a href=\"https://doi.org/10.3389/fphys.2020.558070\">https://doi.org/10.3389/fphys.2020.558070</a>","ista":"Rizzo R, Zhang X, Wang JWJL, Lombardi F, Ivanov PC. 2020. Network physiology of cortico–muscular interactions. Frontiers in Physiology. 11, 558070."},"intvolume":"        11","abstract":[{"lang":"eng","text":"Skeletal muscle activity is continuously modulated across physiologic states to provide coordination, flexibility and responsiveness to body tasks and external inputs. Despite the central role the muscular system plays in facilitating vital body functions, the network of brain-muscle interactions required to control hundreds of muscles and synchronize their activation in relation to distinct physiologic states has not been investigated. Recent approaches have focused on general associations between individual brain rhythms and muscle activation during movement tasks. However, the specific forms of coupling, the functional network of cortico-muscular coordination, and how network structure and dynamics are modulated by autonomic regulation across physiologic states remains unknown. To identify and quantify the cortico-muscular interaction network and uncover basic features of neuro-autonomic control of muscle function, we investigate the coupling between synchronous bursts in cortical rhythms and peripheral muscle activation during sleep and wake. Utilizing the concept of time delay stability and a novel network physiology approach, we find that the brain-muscle network exhibits complex dynamic patterns of communication involving multiple brain rhythms across cortical locations and different electromyographic frequency bands. Moreover, our results show that during each physiologic state the cortico-muscular network is characterized by a specific profile of network links strength, where particular brain rhythms play role of main mediators of interaction and control. Further, we discover a hierarchical reorganization in network structure across physiologic states, with high connectivity and network link strength during wake, intermediate during REM and light sleep, and low during deep sleep, a sleep-stage stratification that demonstrates a unique association between physiologic states and cortico-muscular network structure. The reported empirical observations are consistent across individual subjects, indicating universal behavior in network structure and dynamics, and high sensitivity of cortico-muscular control to changes in autonomic regulation, even at low levels of physical activity and muscle tone during sleep. Our findings demonstrate previously unrecognized basic principles of brain-muscle network communication and control, and provide new perspectives on the regulatory mechanisms of brain dynamics and locomotor activation, with potential clinical implications for neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"8955","title":"Network physiology of cortico–muscular interactions","pmid":1,"acknowledgement":"We acknowledge support from the W. M. Keck Foundation, National Institutes of Health (NIH Grant 1R01-HL098437), the US-Israel Binational Science Foundation (BSF Grant 2012219), and the Office of Naval Research (ONR Grant 000141010078). FL acknowledges support also from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411.","publication_identifier":{"eissn":["1664042X"]},"isi":1,"scopus_import":"1","file_date_updated":"2020-12-21T10:37:50Z"},{"scopus_import":"1","isi":1,"file_date_updated":"2021-01-11T08:37:31Z","issue":"50","publication_identifier":{"issn":["00278424"],"eissn":["10916490"]},"title":"Nonequilibrium models of optimal enhancer function","_id":"9000","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"lang":"eng","text":"In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future."}],"acknowledgement":"G.T. was supported by Human Frontiers Science Program Grant RGP0034/2018. R.G. was supported by the Austrian Academy of Sciences DOC Fellowship. R.G. thanks S. Avvakumov for helpful discussions.","pmid":1,"status":"public","citation":{"ieee":"R. Grah, B. Zoller, and G. Tkačik, “Nonequilibrium models of optimal enhancer function,” <i>PNAS</i>, vol. 117, no. 50. National Academy of Sciences, pp. 31614–31622, 2020.","short":"R. Grah, B. Zoller, G. Tkačik, PNAS 117 (2020) 31614–31622.","apa":"Grah, R., Zoller, B., &#38; Tkačik, G. (2020). Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>","ista":"Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer function. PNAS. 117(50), 31614–31622.","chicago":"Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>.","mla":"Grah, Rok, et al. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>, vol. 117, no. 50, National Academy of Sciences, 2020, pp. 31614–22, doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>.","ama":"Grah R, Zoller B, Tkačik G. Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. 2020;117(50):31614-31622. doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>"},"intvolume":"       117","has_accepted_license":"1","related_material":{"link":[{"description":"News on IST Homepage","relation":"press_release","url":"https://ist.ac.at/en/news/new-compact-model-for-gene-regulation-in-higher-organisms/"}]},"language":[{"iso":"eng"}],"year":"2020","ddc":["570"],"department":[{"_id":"GaTk"}],"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)"},"page":"31614-31622","author":[{"full_name":"Grah, Rok","first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","last_name":"Grah","orcid":"0000-0003-2539-3560"},{"first_name":"Benjamin","full_name":"Zoller, Benjamin","last_name":"Zoller"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}],"day":"15","publication_status":"published","date_created":"2021-01-10T23:01:17Z","date_updated":"2023-08-24T11:10:22Z","doi":"10.1073/pnas.2006731117","publisher":"National Academy of Sciences","publication":"PNAS","article_processing_charge":"No","file":[{"checksum":"69039cd402a571983aa6cb4815ffa863","access_level":"open_access","file_name":"2020_PNAS_Grah.pdf","success":1,"file_size":1199247,"date_updated":"2021-01-11T08:37:31Z","file_id":"9004","date_created":"2021-01-11T08:37:31Z","content_type":"application/pdf","creator":"dernst","relation":"main_file"}],"quality_controlled":"1","oa":1,"month":"12","type":"journal_article","date_published":"2020-12-15T00:00:00Z","project":[{"grant_number":"RGP0034/2018","_id":"2665AAFE-B435-11E9-9278-68D0E5697425","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?"},{"name":"Biophysically realistic genotype-phenotype maps for regulatory networks","_id":"267C84F4-B435-11E9-9278-68D0E5697425"}],"volume":117,"external_id":{"isi":["000600608300015"],"pmid":["33268497"]},"article_type":"original","oa_version":"Published Version"},{"oa_version":"Published Version","contributor":[{"orcid":"0000-0001-6220-2052","last_name":"Guet","contributor_type":"project_leader","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"}],"has_accepted_license":"1","citation":{"apa":"Grah, R. (2020). Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:7383\">https://doi.org/10.15479/AT:ISTA:7383</a>","ista":"Grah R. 2020. Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:7383\">10.15479/AT:ISTA:7383</a>.","ieee":"R. Grah, “Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation.” Institute of Science and Technology Austria, 2020.","short":"R. Grah, (2020).","mla":"Grah, Rok. <i>Matlab Scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression Regulation</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:7383\">10.15479/AT:ISTA:7383</a>.","ama":"Grah R. Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:7383\">10.15479/AT:ISTA:7383</a>","chicago":"Grah, Rok. “Matlab Scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression Regulation.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:7383\">https://doi.org/10.15479/AT:ISTA:7383</a>."},"date_published":"2020-01-28T00:00:00Z","type":"research_data","status":"public","month":"01","oa":1,"file":[{"file_id":"7384","date_created":"2020-01-28T10:39:40Z","content_type":"application/zip","relation":"main_file","creator":"rgrah","checksum":"9d292cf5207b3829225f44c044cdb3fd","file_name":"Scripts.zip","access_level":"open_access","file_size":73363365,"date_updated":"2020-07-14T12:47:57Z"},{"checksum":"4076ceab32ef588cc233802bab24c1ab","file_name":"READ_ME_MAIN.txt","access_level":"open_access","file_size":962,"date_updated":"2020-07-14T12:47:57Z","date_created":"2020-01-28T10:39:30Z","file_id":"7385","content_type":"text/plain","relation":"main_file","creator":"rgrah"}],"abstract":[{"text":"Organisms cope with change by employing transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. We ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. By real-time monitoring of gene copy number mutations in E. coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy number, and hence expression level, polymorphism. This ‘amplification-mediated gene expression tuning’ occurs on timescales similar to canonical gene regulation and can deal with rapid environmental changes. Mathematical modeling shows that amplifications also tune gene expression in stochastic environments where transcription factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune expression of any gene, without leaving any genomic signature.","lang":"eng"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"7383","date_updated":"2024-02-21T12:42:31Z","title":"Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation","doi":"10.15479/AT:ISTA:7383","publisher":"Institute of Science and Technology Austria","date_created":"2020-01-28T10:41:49Z","day":"28","author":[{"full_name":"Grah, Rok","first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2539-3560","last_name":"Grah"}],"keyword":["Matlab scripts","analysis of microfluidics","mathematical model"],"file_date_updated":"2020-07-14T12:47:57Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"year":"2020","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"7652"}]}},{"month":"01","oa":1,"file":[{"relation":"main_file","creator":"dernst","file_id":"7494","date_created":"2020-02-18T07:21:16Z","content_type":"application/pdf","file_size":7247468,"date_updated":"2020-07-14T12:47:59Z","checksum":"2052daa4be5019534f3a42f200a09f32","file_name":"2020_eLife_Narasimhan.pdf","access_level":"open_access"}],"quality_controlled":"1","article_number":"e52067","article_processing_charge":"No","publisher":"eLife Sciences Publications","doi":"10.7554/eLife.52067","date_updated":"2023-08-18T06:33:07Z","publication":"eLife","oa_version":"Published Version","external_id":{"isi":["000514104100001"],"pmid":["31971511"]},"article_type":"original","type":"journal_article","date_published":"2020-01-23T00:00:00Z","project":[{"call_identifier":"H2020","name":"Tracing Evolution of Auxin Transport and Polarity in Plants","grant_number":"742985","_id":"261099A6-B435-11E9-9278-68D0E5697425"},{"_id":"26538374-B435-11E9-9278-68D0E5697425","grant_number":"I03630","call_identifier":"FWF","name":"Molecular mechanisms of endocytic cargo recognition in plants"}],"volume":9,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"JiFr"},{"_id":"GaTk"},{"_id":"EM-Fac"},{"_id":"SyCr"}],"language":[{"iso":"eng"}],"year":"2020","ddc":["570","580"],"date_created":"2020-02-16T23:00:50Z","acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"Bio"},{"_id":"EM-Fac"}],"day":"23","publication_status":"published","author":[{"last_name":"Narasimhan","orcid":"0000-0002-8600-0671","full_name":"Narasimhan, Madhumitha","first_name":"Madhumitha","id":"44BF24D0-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-2739-8843","last_name":"Johnson","id":"46A62C3A-F248-11E8-B48F-1D18A9856A87","first_name":"Alexander J","full_name":"Johnson, Alexander J"},{"first_name":"Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87","full_name":"Prizak, Roshan","last_name":"Prizak"},{"full_name":"Kaufmann, Walter","first_name":"Walter","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9735-5315","last_name":"Kaufmann"},{"orcid":"0000-0002-0471-8285","last_name":"Tan","id":"2DE75584-F248-11E8-B48F-1D18A9856A87","first_name":"Shutang","full_name":"Tan, Shutang"},{"last_name":"Casillas Perez","first_name":"Barbara E","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","full_name":"Casillas Perez, Barbara E"},{"id":"4159519E-F248-11E8-B48F-1D18A9856A87","first_name":"Jiří","full_name":"Friml, Jiří","orcid":"0000-0002-8302-7596","last_name":"Friml"}],"pmid":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"text":"In plants, clathrin mediated endocytosis (CME) represents the major route for cargo internalisation from the cell surface. It has been assumed to operate in an evolutionary conserved manner as in yeast and animals. Here we report characterisation of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement in electron microscopy and quantitative live imaging techniques. Arabidopsis CME appears to follow the constant curvature model and the bona fide CME population generates vesicles of a predominantly hexagonal-basket type; larger and with faster kinetics than in other models. Contrary to the existing paradigm, actin is dispensable for CME events at the plasma membrane but plays a unique role in collecting endocytic vesicles, sorting of internalised cargos and directional endosome movement that itself actively promote CME events. Internalized vesicles display a strongly delayed and sequential uncoating. These unique features highlight the independent evolution of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes.","lang":"eng"}],"title":"Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants","_id":"7490","citation":{"chicago":"Narasimhan, Madhumitha, Alexander J Johnson, Roshan Prizak, Walter Kaufmann, Shutang Tan, Barbara E Casillas Perez, and Jiří Friml. “Evolutionarily Unique Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” <i>ELife</i>. eLife Sciences Publications, 2020. <a href=\"https://doi.org/10.7554/eLife.52067\">https://doi.org/10.7554/eLife.52067</a>.","mla":"Narasimhan, Madhumitha, et al. “Evolutionarily Unique Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” <i>ELife</i>, vol. 9, e52067, eLife Sciences Publications, 2020, doi:<a href=\"https://doi.org/10.7554/eLife.52067\">10.7554/eLife.52067</a>.","ama":"Narasimhan M, Johnson AJ, Prizak R, et al. Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. <i>eLife</i>. 2020;9. doi:<a href=\"https://doi.org/10.7554/eLife.52067\">10.7554/eLife.52067</a>","ieee":"M. Narasimhan <i>et al.</i>, “Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants,” <i>eLife</i>, vol. 9. eLife Sciences Publications, 2020.","short":"M. Narasimhan, A.J. Johnson, R. Prizak, W. Kaufmann, S. Tan, B.E. Casillas Perez, J. Friml, ELife 9 (2020).","apa":"Narasimhan, M., Johnson, A. J., Prizak, R., Kaufmann, W., Tan, S., Casillas Perez, B. E., &#38; Friml, J. (2020). Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.52067\">https://doi.org/10.7554/eLife.52067</a>","ista":"Narasimhan M, Johnson AJ, Prizak R, Kaufmann W, Tan S, Casillas Perez BE, Friml J. 2020. Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. eLife. 9, e52067."},"intvolume":"         9","has_accepted_license":"1","status":"public","ec_funded":1,"file_date_updated":"2020-07-14T12:47:59Z","scopus_import":"1","isi":1,"publication_identifier":{"eissn":["2050-084X"]}},{"date_created":"2020-03-06T07:39:38Z","day":"25","publication_status":"published","author":[{"last_name":"Grah","orcid":"0000-0003-2539-3560","full_name":"Grah, Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","first_name":"Rok"},{"first_name":"Tamar","full_name":"Friedlander, Tamar","last_name":"Friedlander"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"related_material":{"record":[{"status":"deleted","relation":"research_data","id":"9716"},{"id":"9776","relation":"research_data","status":"public"},{"relation":"used_in_publication","status":"public","id":"9779"},{"id":"8155","relation":"dissertation_contains","status":"public"},{"status":"public","relation":"research_data","id":"9777"}]},"ddc":["000","570"],"year":"2020","language":[{"iso":"eng"}],"external_id":{"isi":["000526725200019"]},"article_type":"original","oa_version":"Published Version","date_published":"2020-02-25T00:00:00Z","type":"journal_article","volume":16,"month":"02","oa":1,"file":[{"relation":"main_file","creator":"dernst","date_created":"2020-03-09T15:12:21Z","file_id":"7579","content_type":"application/pdf","file_size":2209325,"date_updated":"2020-07-14T12:48:00Z","checksum":"5239dd134dc6e1c71fe7b3ce2953da37","file_name":"2020_PlosCompBio_Grah.pdf","access_level":"open_access"}],"quality_controlled":"1","article_number":"e1007642","article_processing_charge":"No","date_updated":"2023-09-12T11:02:24Z","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1007642","publication":"PLOS Computational Biology","publication_identifier":{"issn":["1553-7358"]},"issue":"2","file_date_updated":"2020-07-14T12:48:00Z","scopus_import":"1","isi":1,"intvolume":"        16","citation":{"short":"R. Grah, T. Friedlander, PLOS Computational Biology 16 (2020).","ieee":"R. Grah and T. Friedlander, “The relation between crosstalk and gene regulation form revisited,” <i>PLOS Computational Biology</i>, vol. 16, no. 2. Public Library of Science, 2020.","apa":"Grah, R., &#38; Friedlander, T. (2020). The relation between crosstalk and gene regulation form revisited. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">https://doi.org/10.1371/journal.pcbi.1007642</a>","ista":"Grah R, Friedlander T. 2020. The relation between crosstalk and gene regulation form revisited. PLOS Computational Biology. 16(2), e1007642.","chicago":"Grah, Rok, and Tamar Friedlander. “The Relation between Crosstalk and Gene Regulation Form Revisited.” <i>PLOS Computational Biology</i>. Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">https://doi.org/10.1371/journal.pcbi.1007642</a>.","mla":"Grah, Rok, and Tamar Friedlander. “The Relation between Crosstalk and Gene Regulation Form Revisited.” <i>PLOS Computational Biology</i>, vol. 16, no. 2, e1007642, Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">10.1371/journal.pcbi.1007642</a>.","ama":"Grah R, Friedlander T. The relation between crosstalk and gene regulation form revisited. <i>PLOS Computational Biology</i>. 2020;16(2). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">10.1371/journal.pcbi.1007642</a>"},"has_accepted_license":"1","status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"text":"Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.","lang":"eng"}],"title":"The relation between crosstalk and gene regulation form revisited","_id":"7569"},{"publication":"Nature Ecology & Evolution","date_updated":"2024-03-25T23:30:20Z","doi":"10.1038/s41559-020-1132-7","publisher":"Springer Nature","article_processing_charge":"No","quality_controlled":"1","file":[{"content_type":"application/pdf","file_id":"8640","date_created":"2020-10-09T09:56:01Z","creator":"dernst","relation":"main_file","file_name":"2020_NatureEcolEvo_Tomanek.pdf","access_level":"open_access","success":1,"checksum":"ef3bbf42023e30b2c24a6278025d2040","date_updated":"2020-10-09T09:56:01Z","file_size":745242}],"oa":1,"month":"04","volume":4,"project":[{"_id":"267C84F4-B435-11E9-9278-68D0E5697425","name":"Biophysically realistic genotype-phenotype maps for regulatory networks"}],"type":"journal_article","date_published":"2020-04-01T00:00:00Z","external_id":{"isi":["000519008300005"]},"article_type":"original","oa_version":"Submitted Version","year":"2020","ddc":["570"],"language":[{"iso":"eng"}],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"8155"},{"id":"7383","relation":"research_data","status":"public"},{"id":"7016","relation":"research_data","status":"public"},{"status":"public","relation":"used_in_publication","id":"8653"}],"link":[{"description":"News on IST Homepage","relation":"press_release","url":"https://ist.ac.at/en/news/how-to-thrive-without-gene-regulation/"}]},"department":[{"_id":"GaTk"},{"_id":"CaGu"}],"page":"612-625","author":[{"last_name":"Tomanek","orcid":"0000-0001-6197-363X","id":"3981F020-F248-11E8-B48F-1D18A9856A87","first_name":"Isabella","full_name":"Tomanek, Isabella"},{"orcid":"0000-0003-2539-3560","last_name":"Grah","first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok"},{"last_name":"Lagator","first_name":"M.","full_name":"Lagator, M."},{"full_name":"Andersson, A. M. C.","first_name":"A. M. C.","last_name":"Andersson"},{"full_name":"Bollback, Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","first_name":"Jonathan P","orcid":"0000-0002-4624-4612","last_name":"Bollback"},{"orcid":"0000-0002-6699-1455","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet"}],"publication_status":"published","day":"01","date_created":"2020-04-08T15:20:53Z","_id":"7652","title":"Gene amplification as a form of population-level gene expression regulation","abstract":[{"lang":"eng","text":"Organisms cope with change by taking advantage of transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. Here, we investigate whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using real-time monitoring of gene-copy-number mutations in Escherichia coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy-number and, therefore, expression-level polymorphisms. This amplification-mediated gene expression tuning (AMGET) occurs on timescales that are similar to canonical gene regulation and can respond to rapid environmental changes. Mathematical modelling shows that amplifications also tune gene expression in stochastic environments in which transcription-factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune the expression of any gene, without leaving any genomic signature."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","acknowledgement":"We thank L. Hurst, N. Barton, M. Pleska, M. Steinrück, B. Kavcic and A. Staron for input on the manuscript, and To. Bergmiller and R. Chait for help with microfluidics experiments. I.T. is a recipient the OMV fellowship. R.G. is a recipient of a DOC (Doctoral Fellowship Programme of the Austrian Academy of Sciences) Fellowship of the Austrian Academy of Sciences.","status":"public","has_accepted_license":"1","intvolume":"         4","citation":{"short":"I. Tomanek, R. Grah, M. Lagator, A.M.C. Andersson, J.P. Bollback, G. Tkačik, C.C. Guet, Nature Ecology &#38; Evolution 4 (2020) 612–625.","ieee":"I. Tomanek <i>et al.</i>, “Gene amplification as a form of population-level gene expression regulation,” <i>Nature Ecology &#38; Evolution</i>, vol. 4, no. 4. Springer Nature, pp. 612–625, 2020.","ista":"Tomanek I, Grah R, Lagator M, Andersson AMC, Bollback JP, Tkačik G, Guet CC. 2020. Gene amplification as a form of population-level gene expression regulation. Nature Ecology &#38; Evolution. 4(4), 612–625.","apa":"Tomanek, I., Grah, R., Lagator, M., Andersson, A. M. C., Bollback, J. P., Tkačik, G., &#38; Guet, C. C. (2020). Gene amplification as a form of population-level gene expression regulation. <i>Nature Ecology &#38; Evolution</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41559-020-1132-7\">https://doi.org/10.1038/s41559-020-1132-7</a>","chicago":"Tomanek, Isabella, Rok Grah, M. Lagator, A. M. C. Andersson, Jonathan P Bollback, Gašper Tkačik, and Calin C Guet. “Gene Amplification as a Form of Population-Level Gene Expression Regulation.” <i>Nature Ecology &#38; Evolution</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41559-020-1132-7\">https://doi.org/10.1038/s41559-020-1132-7</a>.","ama":"Tomanek I, Grah R, Lagator M, et al. Gene amplification as a form of population-level gene expression regulation. <i>Nature Ecology &#38; Evolution</i>. 2020;4(4):612-625. doi:<a href=\"https://doi.org/10.1038/s41559-020-1132-7\">10.1038/s41559-020-1132-7</a>","mla":"Tomanek, Isabella, et al. “Gene Amplification as a Form of Population-Level Gene Expression Regulation.” <i>Nature Ecology &#38; Evolution</i>, vol. 4, no. 4, Springer Nature, 2020, pp. 612–25, doi:<a href=\"https://doi.org/10.1038/s41559-020-1132-7\">10.1038/s41559-020-1132-7</a>."},"isi":1,"scopus_import":"1","file_date_updated":"2020-10-09T09:56:01Z","issue":"4","publication_identifier":{"issn":["2397-334X"]}},{"status":"public","has_accepted_license":"1","citation":{"chicago":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>. Frontiers, 2020. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>.","ama":"Berry MJ, Tkačik G. Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. 2020;14. doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>","mla":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>, vol. 14, 20, Frontiers, 2020, doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>.","ieee":"M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle for population codes,” <i>Frontiers in Computational Neuroscience</i>, vol. 14. Frontiers, 2020.","short":"M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).","ista":"Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 14, 20.","apa":"Berry, M. J., &#38; Tkačik, G. (2020). Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. Frontiers. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>"},"intvolume":"        14","abstract":[{"text":"We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.","lang":"eng"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"7656","title":"Clustering of neural activity: A design principle for population codes","pmid":1,"publication_identifier":{"eissn":["16625188"]},"isi":1,"scopus_import":"1","file_date_updated":"2020-07-14T12:48:01Z","volume":14,"date_published":"2020-03-13T00:00:00Z","type":"journal_article","external_id":{"pmid":["32231528"],"isi":["000525543200001"]},"oa_version":"Published Version","article_type":"original","article_processing_charge":"No","publication":"Frontiers in Computational Neuroscience","publisher":"Frontiers","date_updated":"2023-08-18T10:30:11Z","doi":"10.3389/fncom.2020.00020","month":"03","article_number":"20","file":[{"file_size":4082937,"date_updated":"2020-07-14T12:48:01Z","checksum":"2b1da23823eae9cedbb42d701945b61e","file_name":"2020_Frontiers_Berry.pdf","access_level":"open_access","relation":"main_file","creator":"dernst","date_created":"2020-04-14T12:20:39Z","file_id":"7659","content_type":"application/pdf"}],"quality_controlled":"1","oa":1,"author":[{"full_name":"Berry, Michael J.","first_name":"Michael J.","last_name":"Berry"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"}],"date_created":"2020-04-12T22:00:40Z","publication_status":"published","day":"13","language":[{"iso":"eng"}],"year":"2020","ddc":["570"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}]}]