{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T14:22:56Z","timestamp":1792678976179,"version":"4.5.0"},"reference-count":38,"publisher":"The Royal Society","issue":"97","license":[{"start":{"date-parts":[[2014,8,6]],"date-time":"2014-08-06T00:00:00Z","timestamp":1407283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/royalsociety.org\/journals\/ethics-policies\/data-sharing-mining\/"}],"content-domain":{"domain":["royalsocietypublishing.org"],"crossmark-restriction":true},"short-container-title":["J. R. Soc. Interface."],"published-print":{"date-parts":[[2014,8,6]]},"abstract":"<jats:p>\n            Moving cell fronts are an essential feature of wound healing, development and disease. The rate at which a cell front moves is driven, in part, by the cell motility, quantified in terms of the cell diffusivity\n            <jats:italic>D<\/jats:italic>\n            , and the cell proliferation rate\n            <jats:italic>\u03bb<\/jats:italic>\n            . Scratch assays are a commonly reported procedure used to investigate the motion of cell fronts where an initial cell monolayer is scratched, and the motion of the front is monitored over a short period of time, often less than 24 h. The simplest way of quantifying a scratch assay is to monitor the progression of the leading edge. Use of leading edge data is very convenient because, unlike other methods, it is non-destructive and does not require labelling, tracking or counting individual cells among the population. In this work, we study short-time leading edge data in a scratch assay using a discrete mathematical model and automated image analysis with the aim of investigating whether such data allow us to reliably identify\n            <jats:italic>D<\/jats:italic>\n            and\n            <jats:italic>\u03bb<\/jats:italic>\n            . Using a naive calibration approach where we simply scan the relevant region of the (\n            <jats:italic>D<\/jats:italic>\n            ,\n            <jats:italic>\u03bb<\/jats:italic>\n            ) parameter space, we show that there are many choices of\n            <jats:italic>D<\/jats:italic>\n            and\n            <jats:italic>\u03bb<\/jats:italic>\n            for which our model produces indistinguishable short-time leading edge data. Therefore, without due care, it is impossible to estimate\n            <jats:italic>D<\/jats:italic>\n            and\n            <jats:italic>\u03bb<\/jats:italic>\n            from this kind of data. To address this, we present a modified approach accounting for the fact that cell motility occurs over a much shorter time scale than proliferation. Using this information, we divide the duration of the experiment into two periods, and we estimate\n            <jats:italic>D<\/jats:italic>\n            using data from the first period, whereas we estimate\n            <jats:italic>\u03bb<\/jats:italic>\n            using data from the second period. We confirm the accuracy of our approach using\n            <jats:italic>in silico<\/jats:italic>\n            data and a new set of\n            <jats:italic>in vitro<\/jats:italic>\n            data, which shows that our method recovers estimates of\n            <jats:italic>D<\/jats:italic>\n            and\n            <jats:italic>\u03bb<\/jats:italic>\n            that are consistent with previously reported values except that that our approach is fast, inexpensive, non-destructive and avoids the need for cell labelling and cell counting.\n          <\/jats:p>","DOI":"10.1098\/rsif.2014.0325","type":"journal-article","created":{"date-parts":[[2014,5,21]],"date-time":"2014-05-21T09:21:00Z","timestamp":1400664060000},"page":"20140325","update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["How much information can be obtained from tracking the position of the leading edge in a scratch assay?"],"prefix":"10.1098","volume":"11","author":[{"given":"Stuart T.","family":"Johnston","sequence":"first","affiliation":[{"name":"Mathematical Sciences, Queensland University of Technology, Brisbane, Australia"},{"name":"Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J.","family":"Simpson","sequence":"additional","affiliation":[{"name":"Mathematical Sciences, Queensland University of Technology, Brisbane, Australia"},{"name":"Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. 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