Evidence map›Paper›PMID 22327539›Full record

ArticleIntegrative biology : quantitative biosciences from nano to macro2012

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

Juliane Liepe, Harriet Taylor, Chris P Barnes, Maxime Huvet, Laurence Bugeon, Thomas Thorne, Jonathan R Lamb, Margaret J Dallman, Michael P H Stumpf

Open access · greenAbstract read
In one paragraph

Article in Integrative biology : quantitative biosciences from nano to macro, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Juliane Liepe *Centre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK.
Harriet Taylor *Division of Cell and Molecular Biology, Department of Life Sciences, Imperial College London, London, UK.
Chris P BarnesCentre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK.
Maxime HuvetCentre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK.
Laurence BugeonDivision of Cell and Molecular Biology, Department of Life Sciences, Imperial College London, London, UK.
Thomas ThorneCentre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK.
Jonathan R LambDivision of Cell and Molecular Biology, Department of Life Sciences, Imperial College London, London, UK.
Margaret J DallmanDivision of Cell and Molecular Biology, Department of Life Sciences, Imperial College London, London, UK.
Michael P H StumpfCentre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK.
Imperial College London · GBLondon Institute for Mathematical Sciences · GBUniversity of Edinburgh · GB

Funding

Biotechnology and Biological Sciences Research Council BB/F005210/1Biotechnology and Biological Sciences Research Council BB/G007934/1Wellcome Trust 086763
6 · The paper itself

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.

Indexed as

Models, BiologicalAlgorithmsAnimalsBayes TheoremCell MovementHumansLeukocytesSignal TransductionSystems BiologyTime-Lapse ImagingZebrafish

Identifiers

PMID22327539
PMCPMC5058438
OpenAlexW2135544870

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.