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Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation.

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Liepe,  J.
Research Group of Quantitative and System Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Liepe, J., Taylor, H., Barnes, C. P., Huvet, M., Bugeon, L., Thorne, T., et al. (2012). Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation. Integrative Biology, 4(3), 335-345. doi:10.1039/c2ib00175f.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-0ECE-5
Abstract
In vivo studies allow us to investigate biological processes at the level of the organism. But not all aspects of in vivo systems are amenable to direct experimental measurements. In order to make the most of such data we therefore require statistical tools that allow us to obtain reliable estimates for e. g. kinetic in vivo parameters. Here we show how we can use approximate Bayesian computation approaches in order to analyse leukocyte migration in zebrafish embryos in response to injuries. We track individual leukocytes using live imaging following surgical injury to the embryos' tail-fins. The signalling gradient that leukocytes follow towards the site of the injury cannot be directly measured but we can estimate its shape and how it changes with time from the directly observed patterns of leukocyte migration. By coupling simple models of immune signalling and leukocyte migration with the unknown gradient shape into a single statistical framework we can gain detailed insights into the tissue-wide processes that are involved in the innate immune response to wound injury. In particular we find conclusive evidence for a temporally and spatially changing signalling gradient that modulates the changing activity of the leukocyte population in the embryos. We conclude with a robustness analysis which highlights the most important factors determining the leukocyte dynamics. Our approach relies only on the ability to simulate numerically the process under investigation and is therefore also applicable in other in vivo contexts and studies.