Imperial College London
Alternate

Nick S Jones

Faculty of Natural Sciences, Department of Mathematics

Professor of Mathematical Sciences
 
 
 
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Contact

 

+44 (0)20 7594 1146nick.jones

 
 
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Location

 

301aSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inbook{Hoitzing:2017:10.1007/978-3-319-62627-7_13,
author = {Hoitzing, H and Johnston, IG and Jones, NS},
booktitle = {Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology},
doi = {10.1007/978-3-319-62627-7_13},
pages = {287--314},
title = {Stochastic models for evolving cellular populations of mitochondria: Disease, development, and ageing},
url = {http://dx.doi.org/10.1007/978-3-319-62627-7_13},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - Mitochondria are essential cellular organelles whose dysfunction is associated with ageing, cancer, mitochondrial diseases, and many other disorders. They contain their own genomes (mtDNA), of which thousands can be present in a single cell. These genomes are repeatedly replicated and degraded over time, and are prone to mutations. If the fraction of mutated genomes (heteroplasmy) exceeds a certain threshold, cellular defects can arise. The dynamics of mtDNAs over time and the accumulation of mutant genomes form a rich and vital stochastic process, the understanding of which provides important insights into disease progression. Numerous mathematical models have been constructed to provide a better understanding of how mitochondrial dysfunctions arise and, importantly, how clinical interventions can alleviate disease symptoms. For a given mean heteroplasmy, an increased variance-and thus a wider cell-to-cell heteroplasmy distribution-implies a higher probability of exceeding a given threshold value, meaning that stochastic models are essential to describe mtDNA disease. Mitochondria can undergo fusion and fission events with each other making the mitochondrial population a dynamic network that continuously changes its morphology, and allowing for the possibility of exchange of mtDNA molecules: coupled stochastic physical and genetic dynamics thus govern cellular mtDNA populations. Here, an overview is given of the kinds of stochastic mathematical models constructed describing mitochondria, their implications, and currently existing open problems.
AU  - Hoitzing,H
AU  - Johnston,IG
AU  - Jones,NS
DO  - 10.1007/978-3-319-62627-7_13
EP  - 314
PY  - 2017///
SN  - 9783319626260
SP  - 287
TI  - Stochastic models for evolving cellular populations of mitochondria: Disease, development, and ageing
T1  - Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology
UR  - http://dx.doi.org/10.1007/978-3-319-62627-7_13
ER  -
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