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Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

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Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues. / Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

In: Journal of Theoretical Biology, Vol. 443, 14.04.2018, p. 66-81.

Research output: Contribution to journalArticlepeer-review

Harvard

Kursawe, J, Baker, RE & Fletcher, AG 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. https://doi.org/10.1016/j.jtbi.2018.01.020

APA

Kursawe, 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, 443, 66-81. https://doi.org/10.1016/j.jtbi.2018.01.020

Vancouver

Kursawe J, Baker RE, Fletcher AG. Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues. Journal of Theoretical Biology. 2018 Apr 14;443:66-81. https://doi.org/10.1016/j.jtbi.2018.01.020

Author

Kursawe, Jochen ; Baker, Ruth E. ; Fletcher, Alexander G. / Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues. In: Journal of Theoretical Biology. 2018 ; Vol. 443. pp. 66-81.

Bibtex - Download

@article{f9b477eb42604abab736ccfafeaf322e,
title = "Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues",
abstract = "The 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.",
keywords = "Cell-based models, Approximate Bayesian computation, Parameter inference, Vertex models, Drosophila wing imaginal disc",
author = "Jochen Kursawe and Baker, {Ruth E.} and Fletcher, {Alexander G.}",
note = "Funding: UK Engineering and Physical Sciences Research Council (grant number EP/N509711/1) (JK).",
year = "2018",
month = apr,
day = "14",
doi = "10.1016/j.jtbi.2018.01.020",
language = "English",
volume = "443",
pages = "66--81",
journal = "Journal of Theoretical Biology",
issn = "0022-5193",
publisher = "ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

AU - Kursawe, Jochen

AU - Baker, Ruth E.

AU - Fletcher, Alexander G.

N1 - Funding: UK Engineering and Physical Sciences Research Council (grant number EP/N509711/1) (JK).

PY - 2018/4/14

Y1 - 2018/4/14

N2 - The 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.

AB - The 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.

KW - Cell-based models

KW - Approximate Bayesian computation

KW - Parameter inference

KW - Vertex models

KW - Drosophila wing imaginal disc

U2 - 10.1016/j.jtbi.2018.01.020

DO - 10.1016/j.jtbi.2018.01.020

M3 - Article

C2 - 29391171

VL - 443

SP - 66

EP - 81

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

SN - 0022-5193

ER -

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