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    Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis

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    Author
    Lambert, B; MacLean, AL; Fletcher, AG; Combes, AN; Little, MH; Byrne, HM
    Date
    2018-06-01
    Source Title
    Journal of Mathematical Biology
    Publisher
    SPRINGER HEIDELBERG
    University of Melbourne Author/s
    Combes, Alexander; Little, Melissa
    Affiliation
    Paediatrics (RCH)
    Anatomy and Neuroscience
    Metadata
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    Document Type
    Journal Article
    Citations
    Lambert, B., MacLean, A. L., Fletcher, A. G., Combes, A. N., Little, M. H. & Byrne, H. M. (2018). Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis. JOURNAL OF MATHEMATICAL BIOLOGY, 76 (7), pp.1673-1697. https://doi.org/10.1007/s00285-018-1208-z.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253183
    DOI
    10.1007/s00285-018-1208-z
    Abstract
    The adult mammalian kidney has a complex, highly-branched collecting duct epithelium that arises as a ureteric bud sidebranch from an epithelial tube known as the nephric duct. Subsequent branching of the ureteric bud to form the collecting duct tree is regulated by subcellular interactions between the epithelium and a population of mesenchymal cells that surround the tips of outgrowing branches. The mesenchymal cells produce glial cell-line derived neurotrophic factor (GDNF), that binds with RET receptors on the surface of the epithelial cells to stimulate several subcellular pathways in the epithelium. Such interactions are known to be a prerequisite for normal branching development, although competing theories exist for their role in morphogenesis. Here we introduce the first agent-based model of ex vivo kidney uretic branching. Through comparison with experimental data, we show that growth factor-regulated growth mechanisms can explain early epithelial cell branching, but only if epithelial cell division depends in a switch-like way on the local growth factor concentration; cell division occurring only if the driving growth factor level exceeds a threshold. We also show how a recently-developed method, "Approximate Approximate Bayesian Computation", can be used to infer key model parameters, and reveal the dependency between the parameters controlling a growth factor-dependent growth switch. These results are consistent with a requirement for signals controlling proliferation and chemotaxis, both of which are previously identified roles for GDNF.

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