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Mathematical modelling of clonal stem cell dynamics

Mathematical modelling of clonal stem cell dynamics
Mathematical modelling of clonal stem cell dynamics

Studying cell fate dynamics is complicated by the fact that direct in vivo observation of individual cell fate outcomes is usually not possible and only multicellular data of cell clones can be obtained. In this situation, experimental data alone is not sufficient to validate biological models because the hypotheses and the data cannot be directly compared and thus standard statistical tests cannot be leveraged. On the other hand, mathematical modelling can bridge the scales between a hypothesis and measured data via quantitative predictions from a mathematical model. Here, we describe how to implement the rules behind a hypothesis (cell fate outcomes) one-to-one as a stochastic model, how to evaluate such a rule-based model mathematically via analytical calculation or stochastic simulations of the model's Master equation, and to predict the outcomes of clonal statistics for respective hypotheses. We also illustrate two approaches to compare these predictions directly with the clonal data to assess the models.

Clonal dynamics, Master equation, Stem cell fate, Stem cell heterogeneity
1064-3745
107-129
Humana
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Cahan, P.
Greulich, Philip
65da32ad-a73a-435a-86e0-e171437430a9
Cahan, P.

Greulich, Philip (2019) Mathematical modelling of clonal stem cell dynamics. In, Cahan, P. (ed.) Computational Stem Cell Biology. (Methods in Molecular Biology, , (doi:10.1007/978-1-4939-9224-9_5), 1975) New York. Humana, pp. 107-129. (doi:10.1007/978-1-4939-9224-9_5).

Record type: Book Section

Abstract

Studying cell fate dynamics is complicated by the fact that direct in vivo observation of individual cell fate outcomes is usually not possible and only multicellular data of cell clones can be obtained. In this situation, experimental data alone is not sufficient to validate biological models because the hypotheses and the data cannot be directly compared and thus standard statistical tests cannot be leveraged. On the other hand, mathematical modelling can bridge the scales between a hypothesis and measured data via quantitative predictions from a mathematical model. Here, we describe how to implement the rules behind a hypothesis (cell fate outcomes) one-to-one as a stochastic model, how to evaluate such a rule-based model mathematically via analytical calculation or stochastic simulations of the model's Master equation, and to predict the outcomes of clonal statistics for respective hypotheses. We also illustrate two approaches to compare these predictions directly with the clonal data to assess the models.

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More information

Published date: 7 May 2019
Keywords: Clonal dynamics, Master equation, Stem cell fate, Stem cell heterogeneity

Identifiers

Local EPrints ID: 431461
URI: http://eprints.soton.ac.uk/id/eprint/431461
ISSN: 1064-3745
PURE UUID: 488e6882-004a-4cd1-b381-dd870a824b92
ORCID for Philip Greulich: ORCID iD orcid.org/0000-0001-5247-6738

Catalogue record

Date deposited: 05 Jun 2019 16:30
Last modified: 20 Jul 2019 00:38

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