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Cell Population Model to Track Stochastic Cellular Decision-Making During Differentiation

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Computational Stem Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1975))

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Abstract

Human pluripotent stem cells are defined by their potential to give rise to all of the lineages of an embryo proper. Guiding the differentiation of embryonic stem cells or induced pluripotent stem cells can be achieved by exposing them to a succession of signaling conditions meant to mimic developmental milieus. However, achieving a quantitative understanding of the relationship between proliferation, cell death, and commitment has been difficult due to the inherent heterogeneity of pluripotent stem cells and their differentiation. Here, we describe a computational modeling approach to track the dynamics of germ layer commitment of human embryonic stem cells. We demonstrate that simulations using this model yield specific hypotheses regarding proliferation, cell death, and commitment and that these predictions are consistent with experimental measurements.

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Keith Task & Ipsita Banerjee

Authors
  1. Keith Task
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  2. Ipsita Banerjee
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Corresponding author

Correspondence to Ipsita Banerjee .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick Cahan

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Task, K., Banerjee, I. (2019). Cell Population Model to Track Stochastic Cellular Decision-Making During Differentiation. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_3

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  • DOI: https://doi.org/10.1007/978-1-4939-9224-9_3

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9223-2

  • Online ISBN: 978-1-4939-9224-9

  • eBook Packages: Springer Protocols

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