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Cell Volume Distributions in Exponentially Growing Populations

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Computational Methods in Systems Biology (CMSB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11773))

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Abstract

Stochastic effects in cell growth and division drive variability in cellular volumes both at the single-cell level and at the level of growing cell populations. Here we consider a simple and tractable model in which cell volumes grow exponentially, cell division is symmetric, and its rate is volume-dependent. Consistently with previous observations, the model is shown to sustain oscillatory behaviour with alternating phases of slow and fast growth. Exact simulation algorithms and large-time asymptotics are developed and cross-validated for the single-cell and whole-population formulations of the model. The two formulations are shown to provide similar results during the phases of slow growth, but differ during the fast-growth phases. Specifically, the single-cell formulation systematically underestimates the proportion of small cells. More generally, our results suggest that measurable characteristics of cells may follow different distributions depending on whether a single-cell lineage or an entire population is considered.

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Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Comenius University, 84248, Bratislava, Slovakia

    Pavol Bokes

  2. Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA

    Abhyudai Singh

Authors
  1. Pavol Bokes
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  2. Abhyudai Singh
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Correspondence to Pavol Bokes .

Editor information

Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Luca Bortolussi

  2. University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

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Bokes, P., Singh, A. (2019). Cell Volume Distributions in Exponentially Growing Populations. In: Bortolussi, L., Sanguinetti, G. (eds) Computational Methods in Systems Biology. CMSB 2019. Lecture Notes in Computer Science(), vol 11773. Springer, Cham. https://doi.org/10.1007/978-3-030-31304-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-31304-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31303-6

  • Online ISBN: 978-3-030-31304-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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