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Modeling Spatiotemporal Dynamics of Bacterial Populations

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Computational Modeling of Signaling Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 880))

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Abstract

Quantitative modeling of spatiotemporal dynamics of cells facilitates understanding and engineering of biological systems. Using a synthetic bacterial ecosystem as a workbench, we present the approach to mathematically simulate the spatiotemporal population dynamics of the ecosystem. A description of ecosystem’s genetic construction and model development is firstly given. Parameter estimation and computational approach for the derived partial differential equations (PDEs) are then given. Spatiotemporal pattern formation is computed by numerically solving the PDE model. Biodiversity of the ecosystem and its impacts by cellular seeding distance and motility are computed according to the cell distribution patterns.

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Acknowledgments

This study was supported by a Startup Grant (H.S.), AcRF TIER 1 Grant (H.S.), the National Institute of Health (5R01CA118486, L.Y.), a David and Lucile Packard Fellowship (L.Y.), and a DuPont Young Professor Award (L.Y.).

Author information

Authors and Affiliations

  1. Division of Bioengineering, School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore

    Hao Song

  2. Department of Biomedical Engineering, Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA

    Lingchong You

Authors
  1. Hao Song
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  2. Lingchong You
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Corresponding author

Correspondence to Hao Song .

Editor information

Editors and Affiliations

  1. Dept. Chemistry & Biochemistry, University of Colorado, Boulder, Boulder, 80309-0215, Colorado, USA

    Xuedong Liu

  2. Dept. Physics, University of Colorado, Boulder, Boulder, 80309, Colorado, USA

    Meredith D. Betterton

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Song, H., You, L. (2012). Modeling Spatiotemporal Dynamics of Bacterial Populations. In: Liu, X., Betterton, M. (eds) Computational Modeling of Signaling Networks. Methods in Molecular Biology, vol 880. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-833-7_11

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  • DOI: https://doi.org/10.1007/978-1-61779-833-7_11

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-832-0

  • Online ISBN: 978-1-61779-833-7

  • eBook Packages: Springer Protocols

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