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Stochastic Models of Cell Proliferation Kinetics Based on Branching Processes

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Statistical Modeling for Biological Systems

Abstract

The aim of this memorial survey paper is to present some joint work with Andrei Yu. Yakovlev (http://www.biology-direct.com/content/3/1/10) focused on new ideas for the theory of branching processes arising in cell proliferation modeling. The following topics are considered: some basic characteristics of cell cycle temporal organization, distributions of pulse-labeled discrete markers in branching cell populations, distributions of a continuous label in proliferating cell populations, limiting age and residual lifetime distributions for continuous-time branching processes, limit theorems and estimation theory for multitype branching populations and relative frequencies with a large number of ancestor, age-dependent branching populations with randomly chosen paths of evolution. Some of the presented results have not been published yet.

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Notes

  1. 1.

    https://biologydirect.biomedcentral.com/articles/10.1186/1745-6150-3-10.

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Acknowledgements

The author is very grateful to the referee for the useful remarks. The paper is supported by NIH/NINDS grant NS39511, NIH/NCI R01 grant CA134839, NIH grant N01-AI-050020 and grant KP-6-H22/3 from the NSF of the Ministry of Education and Science of Bulgaria.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Nikolay M. Yanev

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  1. Nikolay M. Yanev
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Correspondence to Nikolay M. Yanev .

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Editors and Affiliations

  1. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    Anthony Almudevar

  2. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    David Oakes

  3. Rochester, NY, USA

    Jack Hall

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Yanev, N.M. (2020). Stochastic Models of Cell Proliferation Kinetics Based on Branching Processes. In: Almudevar, A., Oakes, D., Hall, J. (eds) Statistical Modeling for Biological Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-34675-1_1

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