Exact, time-independent estimation of clone size distributions in normal and mutated cells

Roshan, A., Jones, P. H. and Greenman, C. D. (2014) Exact, time-independent estimation of clone size distributions in normal and mutated cells. Journal of The Royal Society Interface, 11 (99). ISSN 1742-5689

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Abstract

Biological tools such as genetic lineage tracing, three dimensional confocal microscopy and next generation DNA sequencing are providing new ways to quantify the distribution of clones of normal and mutated cells. Population-wide clone size distributions in vivo are complicated by multiple cell types, and overlapping birth and death processes. This has led to the increased need for mathematically informed models to understand their biological significance. Standard approaches usually require knowledge of clonal age. We show that modelling on clone size independent of time is an alternative method that offers certain analytical advantages; it can help parameterize these models, and obtain distributions for counts of mutated or proliferating cells, for example. When applied to a general birth-death process common in epithelial progenitors this takes the form of a gamblers ruin problem, the solution of which relates to counting Motzkin lattice paths. Applying this approach to mutational processes, an alternative, exact, formulation of the classic Luria Delbruck problem emerges. This approach can be extended beyond neutral models of mutant clonal evolution, and also describe some distributions relating to sub-clones within a tumour. The approaches above are generally applicable to any Markovian branching process where the dynamics of different "coloured" daughter branches are of interest.

Item Type:	Article
Additional Information:	© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
Faculty \ School:	Faculty of Science > School of Natural Sciences Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology
Depositing User:	Pure Connector
Date Deposited:	09 Jun 2014 20:30
Last Modified:	01 May 2024 10:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/48610
DOI:	10.1098/rsif.2014.0654

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