{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:11:50Z","timestamp":1774505510818,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010191","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000}}],"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["Doctoral Training Award"],"award-info":[{"award-number":["Doctoral Training Award"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["204724\/Z\/16\/Z"],"award-info":[{"award-number":["204724\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000288","name":"Royal Society","doi-asserted-by":"crossref","award":["Wolfson Research Merit Award"],"award-info":[{"award-number":["Wolfson Research Merit Award"]}],"id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a\n                    <jats:italic>minibatch<\/jats:italic>\n                    approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010191","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T13:33:50Z","timestamp":1655818430000},"page":"e1010191","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["Efficient Bayesian inference for mechanistic modelling with high-throughput data"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8596-8595","authenticated-orcid":true,"given":"Simon","family":"Martina Perez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6600-1255","authenticated-orcid":true,"given":"Heba","family":"Sailem","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6304-9333","authenticated-orcid":true,"given":"Ruth E.","family":"Baker","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"issue":"7","key":"pcbi.1010191.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0232565","article-title":"An image J plugin for the high throughput image analysis of in vitro scratch wound healing assays","volume":"15","author":"A Suarez-Arnedo","year":"2020","journal-title":"PLOS ONE"},{"issue":"1472-6750","key":"pcbi.1010191.ref002","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1472-6750-4-21","article-title":"A high-throughput cell migration assay using scratch wound healing, a comparison of image-based readout methods","volume":"4","author":"JC Yarrow","year":"2004","journal-title":"BMC Biotechnology"},{"key":"pcbi.1010191.ref003","doi-asserted-by":"crossref","first-page":"100405","DOI":"10.1016\/j.coisb.2021.100405","article-title":"Editorial overview: \u2018Mathematical modelling of high-throughput and high-content data\u2019","volume":"29","author":"J Hasenauer","year":"2022","journal-title":"Current Opinion in Systems Biology"},{"issue":"1","key":"pcbi.1010191.ref004","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab387","article-title":"A protocol for dynamic model calibration","volume":"23","author":"AF Villaverde","year":"2021","journal-title":"Briefings in Bioinformatics"},{"issue":"2","key":"pcbi.1010191.ref005","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1214\/18-BA1121","article-title":"Efficient acquisition rules for Model-Based Approximate Bayesian computation","volume":"14","author":"M J\u00e4rvenp\u00e4\u00e4","year":"2019","journal-title":"Bayesian Analysis"},{"issue":"1","key":"pcbi.1010191.ref006","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1214\/15-EJS988","article-title":"The rate of convergence for approximate Bayesian computation","volume":"9","author":"S Barber","year":"2015","journal-title":"Electronic Journal of Statistics"},{"key":"pcbi.1010191.ref007","doi-asserted-by":"crossref","DOI":"10.1201\/9781315117195","volume-title":"Handbook of Approximate Bayesian Computation","author":"SA Sisson","year":"2018","edition":"1"},{"issue":"1","key":"pcbi.1010191.ref008","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1137\/18M1229742","article-title":"Multifidelity Approximate Bayesian computation","volume":"8","author":"TP Prescott","year":"2020","journal-title":"SIAM\/ASA Journal on Uncertainty Quantification"},{"issue":"1","key":"pcbi.1010191.ref009","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1515\/sagmb-2012-0069","article-title":"On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo","volume":"12","author":"S Filippi","year":"2013","journal-title":"Statistical Applications in Genetics and Molecular Biology"},{"key":"pcbi.1010191.ref010","unstructured":"Forrow A, Baker RE. Measuring the accuracy of likelihood-free inference; 2021. 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