[{"file_date_updated":"2023-06-13T08:05:46Z","publication":"Nature Communications","type":"journal_article","day":"03","status":"public","intvolume":"        14","department":[{"_id":"SyCr"},{"_id":"GaTk"}],"has_accepted_license":"1","file":[{"creator":"dernst","file_id":"13132","relation":"main_file","content_type":"application/pdf","success":1,"access_level":"open_access","date_updated":"2023-06-13T08:05:46Z","checksum":"4af0393e3ed47b3fc46e68b81c3c1007","date_created":"2023-06-13T08:05:46Z","file_size":2358167,"file_name":"2023_NatureComm_CasillasPerez.pdf"}],"date_created":"2023-06-11T22:00:40Z","date_published":"2023-06-03T00:00:00Z","article_type":"original","month":"06","language":[{"iso":"eng"}],"publisher":"Springer Nature","scopus_import":"1","volume":14,"oa":1,"date_updated":"2023-08-07T13:09:09Z","article_processing_charge":"Yes","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"We thank Mike Bidochka for the fungal strains, the ISTA Social Immunity Team for ant collection, Hanna Leitner for experimental and molecular support, Jennifer Robb and Lukas Lindorfer for microscopy, and the LabSupport Facility at ISTA for general laboratory support. We further thank Victor Mireles, Iain Couzin, Fabian Theis and the Social Immunity Team for continued feedback throughout, and Michael Sixt, Yuko Ulrich, Koos Boomsma, Erika Dawson, Megan Kutzer and Hinrich Schulenburg for comments on the manuscript. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant No. 771402; EPIDEMICSonCHIP) to SC, from the Scientific Grant Agency of the Slovak Republic (Grant No. 1/0521/20) to KB, and the Human Frontier Science Program (Grant No. RGP0065/2012) to GT.","oa_version":"Published Version","quality_controlled":"1","project":[{"grant_number":"771402","name":"Epidemics in ant societies on a chip","_id":"2649B4DE-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"name":"Information processing and computation in fish groups","_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012"}],"pmid":1,"_id":"13127","publication_identifier":{"eissn":["2041-1723"]},"publication_status":"published","citation":{"apa":"Casillas Perez, B. E., Bodova, K., Grasse, A. V., Tkačik, G., &#38; Cremer, S. (2023). Dynamic pathogen detection and social feedback shape collective hygiene in ants. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-38947-y\">https://doi.org/10.1038/s41467-023-38947-y</a>","ieee":"B. E. Casillas Perez, K. Bodova, A. V. Grasse, G. Tkačik, and S. Cremer, “Dynamic pathogen detection and social feedback shape collective hygiene in ants,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023.","chicago":"Casillas Perez, Barbara E, Katarina Bodova, Anna V Grasse, Gašper Tkačik, and Sylvia Cremer. “Dynamic Pathogen Detection and Social Feedback Shape Collective Hygiene in Ants.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-38947-y\">https://doi.org/10.1038/s41467-023-38947-y</a>.","ama":"Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. Dynamic pathogen detection and social feedback shape collective hygiene in ants. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-38947-y\">10.1038/s41467-023-38947-y</a>","mla":"Casillas Perez, Barbara E., et al. “Dynamic Pathogen Detection and Social Feedback Shape Collective Hygiene in Ants.” <i>Nature Communications</i>, vol. 14, 3232, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-38947-y\">10.1038/s41467-023-38947-y</a>.","short":"B.E. Casillas Perez, K. Bodova, A.V. Grasse, G. Tkačik, S. Cremer, Nature Communications 14 (2023).","ista":"Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. 2023. Dynamic pathogen detection and social feedback shape collective hygiene in ants. Nature Communications. 14, 3232."},"author":[{"id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","first_name":"Barbara E","last_name":"Casillas Perez","full_name":"Casillas Perez, Barbara E"},{"first_name":"Katarína","full_name":"Bod'Ová, Katarína","last_name":"Bod'Ová","orcid":"0000-0002-7214-0171","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"id":"406F989C-F248-11E8-B48F-1D18A9856A87","first_name":"Anna V","full_name":"Grasse, Anna V","last_name":"Grasse"},{"first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","last_name":"Cremer","full_name":"Cremer, Sylvia","orcid":"0000-0002-2193-3868","first_name":"Sylvia"}],"abstract":[{"text":"Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.","lang":"eng"}],"isi":1,"article_number":"3232","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ddc":["570"],"related_material":{"record":[{"status":"public","id":"12945","relation":"research_data"}]},"ec_funded":1,"acknowledged_ssus":[{"_id":"LifeSc"}],"doi":"10.1038/s41467-023-38947-y","year":"2023","title":"Dynamic pathogen detection and social feedback shape collective hygiene in ants","external_id":{"isi":["001002562700005"],"pmid":["37270641"]}},{"volume":13,"publist_id":"7423","oa":1,"date_updated":"2023-09-15T12:06:19Z","article_processing_charge":"Yes","_id":"406","acknowledgement":"This work was supported by the Human Frontier Science Program RGP0065/2012 (GT, ES).","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","quality_controlled":"1","project":[{"grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425","name":"Information processing and computation in fish groups"}],"oa_version":"Submitted Version","publication_status":"published","citation":{"chicago":"Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper Tkačik. “Probabilistic Models of Individual and Collective Animal Behavior.” <i>PLoS One</i>. Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pone.0193049\">https://doi.org/10.1371/journal.pone.0193049</a>.","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018). Probabilistic models of individual and collective animal behavior. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0193049\">https://doi.org/10.1371/journal.pone.0193049</a>","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Probabilistic models of individual and collective animal behavior,” <i>PLoS One</i>, vol. 13, no. 3. Public Library of Science, 2018.","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, PLoS One 13 (2018).","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Probabilistic models of individual and collective animal behavior. PLoS One. 13(3).","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Probabilistic models of individual and collective animal behavior. <i>PLoS One</i>. 2018;13(3). doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049\">10.1371/journal.pone.0193049</a>","mla":"Bod’Ová, Katarína, et al. “Probabilistic Models of Individual and Collective Animal Behavior.” <i>PLoS One</i>, vol. 13, no. 3, Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049\">10.1371/journal.pone.0193049</a>."},"abstract":[{"text":"Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data. ","lang":"eng"}],"author":[{"last_name":"Bod’Ová","full_name":"Bod’Ová, Katarína","first_name":"Katarína"},{"id":"315BCD80-F248-11E8-B48F-1D18A9856A87","last_name":"Mitchell","full_name":"Mitchell, Gabriel","first_name":"Gabriel"},{"first_name":"Roy","last_name":"Harpaz","full_name":"Harpaz, Roy"},{"first_name":"Elad","last_name":"Schneidman","full_name":"Schneidman, Elad"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"isi":1,"related_material":{"record":[{"status":"public","relation":"research_data","id":"9831"}]},"ddc":["530","571"],"year":"2018","doi":"10.1371/journal.pone.0193049","pubrep_id":"995","external_id":{"isi":["000426896800032"]},"title":"Probabilistic models of individual and collective animal behavior","issue":"3","publication":"PLoS One","file_date_updated":"2020-07-14T12:46:22Z","day":"07","type":"journal_article","intvolume":"        13","status":"public","has_accepted_license":"1","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:46:18Z","file":[{"creator":"system","file_id":"5165","relation":"main_file","content_type":"application/pdf","access_level":"open_access","date_updated":"2020-07-14T12:46:22Z","checksum":"684229493db75b43e98a46cd922da497","date_created":"2018-12-12T10:15:43Z","file_size":6887358,"file_name":"IST-2018-995-v1+1_2018_Bodova_Probabilistic.pdf"}],"month":"03","date_published":"2018-03-07T00:00:00Z","publisher":"Public Library of Science","scopus_import":"1","language":[{"iso":"eng"}]},{"pubrep_id":"884","title":"Probabilistic models for neural populations that naturally capture global coupling and criticality","year":"2017","doi":"10.1371/journal.pcbi.1005763","ddc":["530","571"],"article_number":"e1005763","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"author":[{"id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","full_name":"Humplik, Jan","last_name":"Humplik","first_name":"Jan"},{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"abstract":[{"text":"Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.","lang":"eng"}],"publication_status":"published","citation":{"short":"J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).","ista":"Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763.","ama":"Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. 2017;13(9). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>","mla":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>.","chicago":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>.","ieee":"J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” <i>PLoS Computational Biology</i>, vol. 13, no. 9. Public Library of Science, 2017.","apa":"Humplik, J., &#38; Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","oa_version":"Published Version","project":[{"grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425","name":"Information processing and computation in fish groups"},{"grant_number":"P 25651-N26","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes"}],"_id":"720","publication_identifier":{"issn":["1553734X"]},"date_updated":"2021-01-12T08:12:21Z","publist_id":"6960","oa":1,"volume":13,"article_processing_charge":"Yes","language":[{"iso":"eng"}],"publisher":"Public Library of Science","scopus_import":1,"date_published":"2017-09-19T00:00:00Z","month":"09","date_created":"2018-12-11T11:48:08Z","file":[{"date_created":"2018-12-12T10:18:30Z","checksum":"81107096c19771c36ddbe6f0282a3acb","file_name":"IST-2017-884-v1+1_journal.pcbi.1005763.pdf","file_size":14167050,"date_updated":"2020-07-14T12:47:53Z","access_level":"open_access","creator":"system","file_id":"5352","content_type":"application/pdf","relation":"main_file"}],"department":[{"_id":"GaTk"}],"has_accepted_license":"1","status":"public","intvolume":"        13","type":"journal_article","day":"19","file_date_updated":"2020-07-14T12:47:53Z","issue":"9","publication":"PLoS Computational Biology"},{"publication":"Journal of Fluid Mechanics","page":"21 - 34","day":"25","type":"journal_article","intvolume":"       817","status":"public","department":[{"_id":"BjHo"}],"date_created":"2018-12-11T11:49:44Z","month":"04","date_published":"2017-04-25T00:00:00Z","scopus_import":"1","publisher":"Cambridge University Press","language":[{"iso":"eng"}],"article_processing_charge":"No","publist_id":"6371","volume":817,"date_updated":"2023-09-22T09:39:46Z","oa":1,"publication_identifier":{"issn":["00221120"]},"_id":"1021","oa_version":"Submitted Version","project":[{"grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425","name":"Information processing and computation in fish groups"}],"quality_controlled":"1","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"chicago":"Lopez Alonso, Jose M, and Marc Avila. “Boundary Layer Turbulence in Experiments on Quasi Keplerian Flows.” <i>Journal of Fluid Mechanics</i>. Cambridge University Press, 2017. <a href=\"https://doi.org/10.1017/jfm.2017.109\">https://doi.org/10.1017/jfm.2017.109</a>.","ieee":"J. M. Lopez Alonso and M. Avila, “Boundary layer turbulence in experiments on quasi Keplerian flows,” <i>Journal of Fluid Mechanics</i>, vol. 817. Cambridge University Press, pp. 21–34, 2017.","apa":"Lopez Alonso, J. M., &#38; Avila, M. (2017). Boundary layer turbulence in experiments on quasi Keplerian flows. <i>Journal of Fluid Mechanics</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/jfm.2017.109\">https://doi.org/10.1017/jfm.2017.109</a>","short":"J.M. Lopez Alonso, M. Avila, Journal of Fluid Mechanics 817 (2017) 21–34.","ista":"Lopez Alonso JM, Avila M. 2017. Boundary layer turbulence in experiments on quasi Keplerian flows. Journal of Fluid Mechanics. 817, 21–34.","ama":"Lopez Alonso JM, Avila M. Boundary layer turbulence in experiments on quasi Keplerian flows. <i>Journal of Fluid Mechanics</i>. 2017;817:21-34. doi:<a href=\"https://doi.org/10.1017/jfm.2017.109\">10.1017/jfm.2017.109</a>","mla":"Lopez Alonso, Jose M., and Marc Avila. “Boundary Layer Turbulence in Experiments on Quasi Keplerian Flows.” <i>Journal of Fluid Mechanics</i>, vol. 817, Cambridge University Press, 2017, pp. 21–34, doi:<a href=\"https://doi.org/10.1017/jfm.2017.109\">10.1017/jfm.2017.109</a>."},"publication_status":"published","abstract":[{"lang":"eng","text":"Most flows in nature and engineering are turbulent because of their large velocities and spatial scales. Laboratory experiments on rotating quasi-Keplerian flows, for which the angular velocity decreases radially but the angular momentum increases, are however laminar at Reynolds numbers exceeding one million. This is in apparent contradiction to direct numerical simulations showing that in these experiments turbulence transition is triggered by the axial boundaries. We here show numerically that as the Reynolds number increases, turbulence becomes progressively confined to the boundary layers and the flow in the bulk fully relaminarizes. Our findings support that turbulence is unlikely to occur in isothermal constant-density quasi-Keplerian flows."}],"author":[{"id":"40770848-F248-11E8-B48F-1D18A9856A87","first_name":"Jose M","last_name":"Lopez Alonso","full_name":"Lopez Alonso, Jose M","orcid":"0000-0002-0384-2022"},{"first_name":"Marc","full_name":"Avila, Marc","last_name":"Avila"}],"isi":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1608.05527"}],"doi":"10.1017/jfm.2017.109","year":"2017","title":"Boundary layer turbulence in experiments on quasi Keplerian flows","external_id":{"isi":["000398179100006"]}},{"page":"1523 - 1548","publication":"Genetics","issue":"4","status":"public","intvolume":"       202","type":"journal_article","day":"06","date_created":"2018-12-11T11:51:55Z","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"language":[{"iso":"eng"}],"scopus_import":"1","publisher":"Genetics Society of America","date_published":"2016-04-06T00:00:00Z","month":"04","quality_controlled":"1","oa_version":"Preprint","project":[{"_id":"25B07788-B435-11E9-9278-68D0E5697425","name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7","grant_number":"250152"},{"name":"Information processing and computation in fish groups","_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1420","article_processing_charge":"No","oa":1,"date_updated":"2025-05-28T11:42:47Z","publist_id":"5787","volume":202,"arxiv":1,"author":[{"first_name":"Katarína","full_name":"Bod'ová, Katarína","last_name":"Bod'ová","orcid":"0000-0002-7214-0171","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","last_name":"Barton","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240"}],"abstract":[{"text":"Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ &lt; 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. ","lang":"eng"}],"citation":{"chicago":"Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation for the Dynamics of Quantitative Traits.” <i>Genetics</i>. Genetics Society of America, 2016. <a href=\"https://doi.org/10.1534/genetics.115.184127\">https://doi.org/10.1534/genetics.115.184127</a>.","apa":"Bodova, K., Tkačik, G., &#38; Barton, N. H. (2016). A general approximation for the dynamics of quantitative traits. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.115.184127\">https://doi.org/10.1534/genetics.115.184127</a>","ieee":"K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics of quantitative traits,” <i>Genetics</i>, vol. 202, no. 4. Genetics Society of America, pp. 1523–1548, 2016.","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548.","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","mla":"Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative Traits.” <i>Genetics</i>, vol. 202, no. 4, Genetics Society of America, 2016, pp. 1523–48, doi:<a href=\"https://doi.org/10.1534/genetics.115.184127\">10.1534/genetics.115.184127</a>.","ama":"Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of quantitative traits. <i>Genetics</i>. 2016;202(4):1523-1548. doi:<a href=\"https://doi.org/10.1534/genetics.115.184127\">10.1534/genetics.115.184127</a>"},"publication_status":"published","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1510.08344"}],"external_id":{"arxiv":["1510.08344"]},"title":"A general approximation for the dynamics of quantitative traits","ec_funded":1,"year":"2016","doi":"10.1534/genetics.115.184127"}]