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dc.contributor.authorKursawe, Jochen
dc.contributor.authorBaker, Ruth E.
dc.contributor.authorFletcher, Alexander G.
dc.identifier.citationKursawe , J , Baker , R E & Fletcher , A G 2018 , ' Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues ' , Journal of Theoretical Biology , vol. 443 , pp. 66-81 .
dc.identifier.otherPURE: 260459524
dc.identifier.otherPURE UUID: f9b477eb-4260-4aba-b736-ccfafeaf322e
dc.identifier.otherPubMed: 29391171
dc.identifier.otherScopus: 85042943713
dc.identifier.otherORCID: /0000-0002-0314-9623/work/60427773
dc.descriptionFunding: UK Engineering and Physical Sciences Research Council (grant number EP/N509711/1) (JK).en
dc.description.abstractThe growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.
dc.relation.ispartofJournal of Theoretical Biologyen
dc.rightsCopyright © 2018 Elsevier Ltd. All rights reserved. This work has been made available online in accordance with publisher policies or with permission from the rights holder. Permissions for further reuse of this content should be sought from the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The published version should be used for citation purposes. The final published version of this work is available at
dc.subjectCell-based modelsen
dc.subjectApproximate Bayesian computationen
dc.subjectParameter inferenceen
dc.subjectVertex modelsen
dc.subjectDrosophila wing imaginal discen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.titleApproximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissuesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.description.statusPeer revieweden

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