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More Insights from Ultrastructural and Functional Plasmodesmata Data Using PDinsight

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Plasmodesmata

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2457))

Abstract

PDinsight is a Python-based tool for computing effective wall permeability for symplasmic transport based on plasmodesma (PD) size and distribution data. PDinsight can be used for direct computation of such permeabilities if full data is available, as well as in an explorative way if some data is either not available or considered unreliable. In this chapter, we briefly describe the basic model underlying the PDinsight calculations and discuss how the different modes of PDinsight can be used in relation to typical research questions. We also offer advice on choosing appropriate values for diffusion coefficients and particle size based on the currently most used experimental probes.

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Notes

  1. 1.

    In polymer physics, hydrodynamic radius has a somewhat different meaning. In the PD literature, hydrodynamic radius typically refers to the Stokes radius.

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Acknowledgements

I would like to thank Johannes Liesche for valuable comments on the manuscript.

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Authors and Affiliations

  1. Wageningen University, Wageningen, The Netherlands

    Eva E. Deinum

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  1. Eva E. Deinum
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Correspondence to Eva E. Deinum .

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Editors and Affiliations

  1. Centre for Plant Sciences, School of Biology, University of Leeds, Leeds, UK

    Yoselin Benitez-Alfonso

  2. Institut de Biologie Moléculaire des Plantes, CNRS, Université de Strasbourg, Strasbourg, France

    Manfred Heinlein

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Deinum, E.E. (2022). More Insights from Ultrastructural and Functional Plasmodesmata Data Using PDinsight. In: Benitez-Alfonso, Y., Heinlein, M. (eds) Plasmodesmata. Methods in Molecular Biology, vol 2457. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2132-5_30

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  • DOI: https://doi.org/10.1007/978-1-0716-2132-5_30

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2131-8

  • Online ISBN: 978-1-0716-2132-5

  • eBook Packages: Springer Protocols

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