{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:19:34Z","timestamp":1772173174911,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010599","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000}}],"reference-count":71,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT210100260."],"award-info":[{"award-number":["FT210100260."]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP200100177."],"award-info":[{"award-number":["DP200100177."]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Parameter estimation for mathematical models of biological processes is often difficult and depends significantly on the quality and quantity of available data. We introduce an efficient framework using Gaussian processes to discover mechanisms underlying delay, migration, and proliferation in a cell invasion experiment. Gaussian processes are leveraged with bootstrapping to provide uncertainty quantification for the mechanisms that drive the invasion process. Our framework is efficient, parallelisable, and can be applied to other biological problems. We illustrate our methods using a canonical scratch assay experiment, demonstrating how simply we can explore different functional forms and develop and test hypotheses about underlying mechanisms, such as whether delay is present. All code and data to reproduce this work are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/DanielVandH\/EquationLearning.jl\" xlink:type=\"simple\">https:\/\/github.com\/DanielVandH\/EquationLearning.jl<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010599","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T13:40:53Z","timestamp":1668606053000},"page":"e1010599","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Computationally efficient mechanism discovery for cell invasion with uncertainty quantification"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6462-0135","authenticated-orcid":true,"given":"Daniel J.","family":"VandenHeuvel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9222-8763","authenticated-orcid":true,"given":"Christopher","family":"Drovandi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6254-313X","authenticated-orcid":true,"given":"Matthew J.","family":"Simpson","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"pcbi.1010599.ref001","doi-asserted-by":"crossref","first-page":"107","DOI":"10.3389\/fcell.2019.00107","article-title":"In vitro cell migration, invasion, and adhesion assays: from cell imaging to data analysis","volume":"7","author":"J Pijuan","year":"2019","journal-title":"Frontiers in Cell and Developmental Biology"},{"key":"pcbi.1010599.ref002","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.addr.2011.01.010","article-title":"Cancer cell invasion: treatment and monitoring opportunities in nanomedicine","volume":"63","author":"O Veiseh","year":"2011","journal-title":"Advanced Drug Delivery Reviews"},{"key":"pcbi.1010599.ref003","doi-asserted-by":"crossref","first-page":"026601","DOI":"10.1088\/0034-4885\/77\/2\/026601","article-title":"Noise in biology","volume":"77","author":"LS Tsimring","year":"2014","journal-title":"Reports on Progress in Physics"},{"key":"pcbi.1010599.ref004","doi-asserted-by":"crossref","first-page":"e1602614","DOI":"10.1126\/sciadv.1602614","article-title":"Data-driven discovery of partial differential equations","volume":"3","author":"SH Rudy","year":"2017","journal-title":"Science Advances"},{"key":"pcbi.1010599.ref005","doi-asserted-by":"crossref","first-page":"9943","DOI":"10.1073\/pnas.0609476104","article-title":"Automated reverse engineering of nonlinear dynamical systems","volume":"104","author":"J Bongard","year":"2007","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pcbi.1010599.ref006","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1126\/science.1165893","article-title":"Distilling free-form natural laws from experimental data","volume":"324","author":"M Schmidt","year":"2009","journal-title":"Science"},{"key":"pcbi.1010599.ref007","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1073\/pnas.1517384113","article-title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","volume":"113","author":"SL Brunton","year":"2016","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pcbi.1010599.ref008","doi-asserted-by":"crossref","first-page":"108833","DOI":"10.1016\/j.ymssp.2022.108833","article-title":"Parsimony-enhanced sparse Bayesian learning for robust discovery of partial differential equations","volume":"171","author":"Z Zhang","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"pcbi.1010599.ref009","doi-asserted-by":"crossref","first-page":"20210426","DOI":"10.1098\/rspa.2021.0426","article-title":"Bayesian uncertainty quantification for data-driven equation learning","volume":"477","author":"S Martina-Perez","year":"2021","journal-title":"Proceedings of the Royal Society A"},{"key":"pcbi.1010599.ref010","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"M Raissi","year":"2019","journal-title":"Journal of Computational Physics"},{"key":"pcbi.1010599.ref011","doi-asserted-by":"crossref","first-page":"6136","DOI":"10.1038\/s41467-021-26434-1","article-title":"Physics-informed learning of governing equations from scarce data","volume":"12","author":"Z Chen","year":"2021","journal-title":"Nature Communications"},{"key":"pcbi.1010599.ref012","doi-asserted-by":"crossref","first-page":"e1008462","DOI":"10.1371\/journal.pcbi.1008462","article-title":"Biologically-informed neural networks guide mechanistic modeling from sparse experimental data","volume":"16","author":"JH Lagergren","year":"2020","journal-title":"PLoS Computational Biology"},{"key":"pcbi.1010599.ref013","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jtbi.2015.10.040","article-title":"Reproducibility of scratch assays is affected by the initial degree of confluence: Experiments, modelling and model selection","volume":"390","author":"W Jin","year":"2016","journal-title":"Journal of Theoretical Biology"},{"key":"pcbi.1010599.ref014","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1615\/Int.J.UncertaintyQuantification.2021034382","article-title":"Explicit estimation of derivatives from data and differential equations by Gaussian process regression","volume":"11","author":"H Wang","year":"2021","journal-title":"International Journal for Uncertainty Quantification"},{"key":"pcbi.1010599.ref015","doi-asserted-by":"crossref","first-page":"110668","DOI":"10.1016\/j.jcp.2021.110668","article-title":"Solving and learning nonlinear PDEs with Gaussian processes","volume":"447","author":"Y Chen","year":"2021","journal-title":"Journal of Computational Physics"},{"key":"pcbi.1010599.ref016","doi-asserted-by":"crossref","first-page":"A172","DOI":"10.1137\/17M1120762","article-title":"Numerical Gaussian processes for time-dependent and nonlinear partial differential equations","volume":"40","author":"M Raissi","year":"2018","journal-title":"SIAM Journal on Scientific Computing"},{"key":"pcbi.1010599.ref017","unstructured":"Bajaj C, McLennan L, Andeen T, Roy A. Robust learning of physics informed neural networks. arXiv:2110.13330 [Preprint]. 2021 [cited 2022 May 09]. Available from: https:\/\/doi.org\/10.48550\/arXiv.2110.13330."},{"key":"pcbi.1010599.ref018","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/00401706.2020.1817790","article-title":"Gaussian process assisted active learning of physical laws","volume":"63","author":"J Chen","year":"2021","journal-title":"Technometrics"},{"key":"pcbi.1010599.ref019","doi-asserted-by":"crossref","first-page":"20210201","DOI":"10.1098\/rsta.2021.0201","article-title":"Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data","volume":"380","author":"MA Bhouri","year":"2022","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"pcbi.1010599.ref020","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jcp.2017.11.039","article-title":"Hidden physics models: Machine learning of nonlinear partial differential equations","volume":"357","author":"M Raissi","year":"2018","journal-title":"Journal of Computational Physics"},{"key":"pcbi.1010599.ref021","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1007\/s00285-018-1208-z","article-title":"Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis","volume":"76","author":"B Lambert","year":"2018","journal-title":"Journal of Mathematical Biology"},{"key":"pcbi.1010599.ref022","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1085\/jgp.201311116","article-title":"Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach","volume":"143","author":"KE Hines","year":"2014","journal-title":"Journal of General Physiology"},{"key":"pcbi.1010599.ref023","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1098\/rsif.2008.0172","article-title":"Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems","volume":"6","author":"T Toni","year":"2008","journal-title":"Journal of the Royal Society Interface"},{"key":"pcbi.1010599.ref024","doi-asserted-by":"crossref","first-page":"20200143","DOI":"10.1098\/rsif.2020.0143","article-title":"Identifying density-dependent interactions in collective cell behaviour","volume":"17","author":"AP Browning","year":"2020","journal-title":"Journal of the Royal Society Interface"},{"key":"pcbi.1010599.ref025","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1214\/18-BA1129","article-title":"Sequential Monte Carlo for static Bayesian models with independent Markov chain Monte Carlo proposals","volume":"14","author":"LF South","year":"2019","journal-title":"Bayesian Analysis"},{"key":"pcbi.1010599.ref026","volume-title":"Bayesian data analysis","author":"A Gelman","year":"2014"},{"key":"pcbi.1010599.ref027","doi-asserted-by":"crossref","first-page":"20200055","DOI":"10.1098\/rsif.2020.0055","article-title":"Practical parameter identifiability for spatio-temporal models of cell invasion","volume":"17","author":"MJ Simpson","year":"2020","journal-title":"Journal of the Royal Society Interface"},{"key":"pcbi.1010599.ref028","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1080\/10635150490522304","article-title":"Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests","volume":"53","author":"D Posada","year":"2004","journal-title":"Systematic Biology"},{"key":"pcbi.1010599.ref029","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.idm.2019.12.010","article-title":"A primer on model selection using the Akaike information criterion","volume":"5","author":"S Portet","year":"2020","journal-title":"Infectious Disease Modelling"},{"key":"pcbi.1010599.ref030","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1098\/rspb.1990.0061","article-title":"Models of epidermal wound healing","volume":"241","author":"JA Sherratt","year":"1990","journal-title":"Proceedings of the Royal Society B"},{"key":"pcbi.1010599.ref031","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.physd.2019.06.005","article-title":"Hole-closing model reveals exponents for nonlinear degenerate diffusivity functions in cell biology","volume":"398","author":"SW McCue","year":"2019","journal-title":"Physica D: Nonlinear Phenomena"},{"key":"pcbi.1010599.ref032","first-page":"16","article-title":"Establishment and characterization of a human prostatic carcinoma cell line (PC-3)","volume":"17","author":"ME Kaighn","year":"1979","journal-title":"Investigative Urology"},{"key":"pcbi.1010599.ref033","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2006"},{"key":"pcbi.1010599.ref034","doi-asserted-by":"crossref","first-page":"e9089","DOI":"10.7717\/peerj.9089","article-title":"Resampling-based methods for biologists","volume":"8","author":"JR Fieberg","year":"2020","journal-title":"PeerJ"},{"key":"pcbi.1010599.ref035","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1007\/s11538-017-0267-4","article-title":"Logistic proliferation of cells in scratch assays is delayed","volume":"79","author":"W Jin","year":"2017","journal-title":"Bulletin of Mathematical Biology"},{"key":"pcbi.1010599.ref036","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S0025-5564(02)00096-2","article-title":"Analysis of logistic growth models","volume":"179","author":"A Tsoularis","year":"2002","journal-title":"Mathematical Biosciences"},{"key":"pcbi.1010599.ref037","doi-asserted-by":"crossref","DOI":"10.1007\/b98868","volume-title":"Mathematical biology I. An introduction","author":"JD Murray","year":"2002"},{"key":"pcbi.1010599.ref038","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"H Akaike","year":"1974","journal-title":"IEEE Transactions on Automatic Control"},{"key":"pcbi.1010599.ref039","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1137\/141000671","article-title":"Julia: A fresh approach to numerical computing","volume":"59","author":"J Bezanson","year":"2017","journal-title":"SIAM Review"},{"key":"pcbi.1010599.ref040","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5334\/jors.151","article-title":"DifferentialEquations.jl\u2014A performant and feature-rich ecosystem for solving differential equations in Julia","volume":"5","author":"C Rackauckas","year":"2017","journal-title":"Journal of Open Research Software"},{"key":"pcbi.1010599.ref041","doi-asserted-by":"crossref","first-page":"110852","DOI":"10.1016\/j.jtbi.2021.110852","article-title":"Model-based data analysis of tissue growth in thin 3D printed scaffolds","volume":"528","author":"AP Browning","year":"2021","journal-title":"Journal of Theoretical Biology"},{"key":"pcbi.1010599.ref042","doi-asserted-by":"crossref","first-page":"110998","DOI":"10.1016\/j.jtbi.2021.110998","article-title":"Parameter identifiability and model selection for sigmoid population growth models","volume":"535","author":"MJ Simpson","year":"2022","journal-title":"Journal of Theoretical Biology"},{"key":"pcbi.1010599.ref043","volume-title":"Algorithms for optimization","author":"MJ Kochenderfer","year":"2019"},{"key":"pcbi.1010599.ref044","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1214\/009053604000001264","article-title":"Default priors for Gaussian processes","volume":"33","author":"R Paulo","year":"2005","journal-title":"Annals of Statistics"},{"key":"pcbi.1010599.ref045","unstructured":"Duvenaud, D. PhD Thesis, Automatic model construction with Gaussian processes. University o Cambridge. Available from: https:\/\/doi.org\/10.17863\/CAM.14087."},{"key":"pcbi.1010599.ref046","doi-asserted-by":"crossref","unstructured":"Le QV, Smola AJ, Canu S. Heteroscedastic Gaussian process regression. In: Raedt LD, Wrobel S, editors. International Conference on Machine Learning; 2005 Aug 7\u201311; Bonn, Germany, pp. 489\u2013496. Available from: https:\/\/doi.org\/10.1145\/1102351.1102413.","DOI":"10.1145\/1102351.1102413"},{"key":"pcbi.1010599.ref047","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-16-0626-7","volume-title":"Delay differential equations and applications to biology","author":"FA Rihan","year":"2021"},{"key":"pcbi.1010599.ref048","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1049\/iet-syb:20050098","article-title":"Efficient algorithms for ordinary differential equation model identification of biological systems","volume":"1","author":"P Gennemark","year":"2007","journal-title":"IET Systems Biology"},{"key":"pcbi.1010599.ref049","first-page":"1","article-title":"Exploring behaviors of stochastic differential equation models of biological systems using change of measures","volume":"13","author":"SK Jha","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1010599.ref050","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.chemolab.2015.01.016","article-title":"Gaussian process regression with multiple response variables","volume":"142","author":"B Wang","year":"2015","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"pcbi.1010599.ref051","doi-asserted-by":"crossref","DOI":"10.1201\/9780367815493","volume-title":"Surrogates","author":"RB Gramacy","year":"2020"},{"key":"pcbi.1010599.ref052","doi-asserted-by":"crossref","unstructured":"Gorbach NC, Bian AA, Fischer B, Bauer S, Buhmann JM. Model selection for Gaussian process regression. In: Roth V, Vetter T, editors. German Conference on Pattern Regression; 2017 Sep 12\u201315; Basel, Switzerland, pp. 306\u2013318. Available from https:\/\/doi.org\/10.1007\/978-3-319-66709-6_25.","DOI":"10.1007\/978-3-319-66709-6_25"},{"key":"pcbi.1010599.ref053","doi-asserted-by":"crossref","DOI":"10.1002\/9781118136188","volume-title":"Geostatistics: Modeling spatial uncertainty","author":"JP Chil\u00e8s","year":"2012"},{"key":"pcbi.1010599.ref054","volume-title":"Machine learning: A probabilistic perspective","author":"KP Murphy","year":"2012"},{"key":"pcbi.1010599.ref055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v102.i01","article-title":"GaussianProcesses.jl: A nonparametric Bayes package for the Julia language","volume":"102","author":"J Fairbrother","year":"2022","journal-title":"Journal of Statistical Software"},{"key":"pcbi.1010599.ref056","doi-asserted-by":"crossref","first-page":"615","DOI":"10.21105\/joss.00615","article-title":"Optim: A mathematical optimization package for Julia","volume":"3","author":"PK Mogensen","year":"2018","journal-title":"Journal of Open Source Software"},{"key":"pcbi.1010599.ref057","doi-asserted-by":"crossref","first-page":"106050","DOI":"10.1016\/j.asoc.2019.106050","article-title":"Surrogate-based optimisation using adaptively scaled radial basis functions","volume":"88","author":"M Urquhart","year":"2020","journal-title":"Applied Soft Computing"},{"key":"pcbi.1010599.ref058","doi-asserted-by":"crossref","first-page":"20190800","DOI":"10.1098\/rspa.2019.0800","article-title":"Learning partial differential equations for biological transport models from noisy spatio-temporal data","volume":"476","author":"JH Lagergren","year":"2020","journal-title":"Proceedings of the Royal Society A"},{"key":"pcbi.1010599.ref059","unstructured":"Townsend A. FastGaussQuadrature.jl. GitHub Repository. 2015 [cited 2022 May 10]. Available from: https:\/\/github.com\/JuliaApproximation\/FastGaussQuadrature.jl."},{"key":"pcbi.1010599.ref060","unstructured":"Revels J, Lubin M, Papamarkou T. Forward-mode automatic differentiation in Julia. arXiv:1607.07892 [Preprint]. 2016 [cited 2022 May 10]. Available from: https:\/\/doi.org\/10.48550\/arXiv.1607.07892."},{"key":"pcbi.1010599.ref061","doi-asserted-by":"crossref","first-page":"115007","DOI":"10.1088\/1361-6420\/ab2aab","article-title":"On the identification of a nonlinear term in a reaction-diffusion equation","volume":"35","author":"B Kaltenbacher","year":"2019","journal-title":"Inverse Problems"},{"key":"pcbi.1010599.ref062","volume-title":"Practical optimization","author":"PE Gill","year":"1997"},{"key":"pcbi.1010599.ref063","unstructured":"Byrne S. KernelDensity.jl. GitHub Repository. 2014 [cited 2022 May 10]. Available from: https:\/\/github.com\/JuliaStats\/KernelDensity.jl."},{"key":"pcbi.1010599.ref064","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12918-015-0182-y","article-title":"Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM\u2122 assay data using the Fisher-Kolmogorov model","volume":"9","author":"ST Johnston","year":"2015","journal-title":"BMC Systems Biology"},{"key":"pcbi.1010599.ref065","volume-title":"An introduction to computational fluid dynamics","author":"HK Versteeg","year":"2007"},{"key":"pcbi.1010599.ref066","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198534419.001.0001","volume-title":"Curve and surface fitting with splines","author":"P Dierckx","year":"1993"},{"key":"pcbi.1010599.ref067","unstructured":"Barbary K. Dierckx.jl. GitHub Repository. 2014 [cited 2022 May 10]. Available from: https:\/\/github.com\/kbarbary\/Dierckx.jl."},{"key":"pcbi.1010599.ref068","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1615\/JMachLearnModelComput.2020035155","article-title":"A survey of constrained Gaussian process regression: Approaches and implementation challenges","volume":"1","author":"LP Swiler","year":"2020","journal-title":"Journal of Machine Learning for Modeling and Computing"},{"key":"pcbi.1010599.ref069","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.aml.2017.05.005","article-title":"AIC under the framework of least squares estimation","volume":"74","author":"HT Banks","year":"2017","journal-title":"Applied Mathematics Letters"},{"key":"pcbi.1010599.ref070","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1080\/10705511.2016.1252265","article-title":"Assessing model selection uncertainty using a bootstrap approach: An update","volume":"24","author":"GH Lubke","year":"2017","journal-title":"Structural Equation Modeling"},{"key":"pcbi.1010599.ref071","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1177\/0049124104268644","article-title":"Multimodel inference: understanding AIC and BIC in model selection","volume":"33","author":"KP Burnham","year":"2004","journal-title":"Sociological Methods & Research"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010599","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010599","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T19:12:17Z","timestamp":1701371537000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010599"}},"subtitle":[],"editor":[{"given":"Philip K.","family":"Maini","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":71,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010599","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.05.12.491596","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,16]]}}}