Stochastic dynamics of interacting haematopoietic stem cell niche lineages.

Since we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity...

Full description

Bibliographic Details
Main Authors: Tamás Székely, Kevin Burrage, Marc Mangel, Michael B Bonsall
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-09-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC4154659?pdf=render
id doaj-b6f0691e28b44844a282e81d76b51d71
record_format Article
spelling doaj-b6f0691e28b44844a282e81d76b51d712020-11-24T21:51:14ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582014-09-01109e100379410.1371/journal.pcbi.1003794Stochastic dynamics of interacting haematopoietic stem cell niche lineages.Tamás SzékelyKevin BurrageMarc MangelMichael B BonsallSince we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity of stem cell populations. This is not appropriate at the level of individual cells and niches, when randomness is more likely to affect dynamics. In this paper, we introduce a fast stochastic method for simulating a metapopulation of stem cell niche lineages, that is, many sub-populations that together form a heterogeneous metapopulation, over time. By selecting the common limiting timestep, our method ensures that the entire metapopulation is simulated synchronously. This is important, as it allows us to introduce interactions between separate niche lineages, which would otherwise be impossible. We expand our method to enable the coupling of many lineages into niche groups, where differentiated cells are pooled within each niche group. Using this method, we explore the dynamics of the haematopoietic system from a demand control system perspective. We find that coupling together niche lineages allows the organism to regulate blood cell numbers as closely as possible to the homeostatic optimum. Furthermore, coupled lineages respond better than uncoupled ones to random perturbations, here the loss of some myeloid cells. This could imply that it is advantageous for an organism to connect together its niche lineages into groups. Our results suggest that a potential fruitful empirical direction will be to understand how stem cell descendants communicate with the niche and how cancer may arise as a result of a failure of such communication.http://europepmc.org/articles/PMC4154659?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Tamás Székely
Kevin Burrage
Marc Mangel
Michael B Bonsall
spellingShingle Tamás Székely
Kevin Burrage
Marc Mangel
Michael B Bonsall
Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
PLoS Computational Biology
author_facet Tamás Székely
Kevin Burrage
Marc Mangel
Michael B Bonsall
author_sort Tamás Székely
title Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
title_short Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
title_full Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
title_fullStr Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
title_full_unstemmed Stochastic dynamics of interacting haematopoietic stem cell niche lineages.
title_sort stochastic dynamics of interacting haematopoietic stem cell niche lineages.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2014-09-01
description Since we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity of stem cell populations. This is not appropriate at the level of individual cells and niches, when randomness is more likely to affect dynamics. In this paper, we introduce a fast stochastic method for simulating a metapopulation of stem cell niche lineages, that is, many sub-populations that together form a heterogeneous metapopulation, over time. By selecting the common limiting timestep, our method ensures that the entire metapopulation is simulated synchronously. This is important, as it allows us to introduce interactions between separate niche lineages, which would otherwise be impossible. We expand our method to enable the coupling of many lineages into niche groups, where differentiated cells are pooled within each niche group. Using this method, we explore the dynamics of the haematopoietic system from a demand control system perspective. We find that coupling together niche lineages allows the organism to regulate blood cell numbers as closely as possible to the homeostatic optimum. Furthermore, coupled lineages respond better than uncoupled ones to random perturbations, here the loss of some myeloid cells. This could imply that it is advantageous for an organism to connect together its niche lineages into groups. Our results suggest that a potential fruitful empirical direction will be to understand how stem cell descendants communicate with the niche and how cancer may arise as a result of a failure of such communication.
url http://europepmc.org/articles/PMC4154659?pdf=render
work_keys_str_mv AT tamasszekely stochasticdynamicsofinteractinghaematopoieticstemcellnichelineages
AT kevinburrage stochasticdynamicsofinteractinghaematopoieticstemcellnichelineages
AT marcmangel stochasticdynamicsofinteractinghaematopoieticstemcellnichelineages
AT michaelbbonsall stochasticdynamicsofinteractinghaematopoieticstemcellnichelineages
_version_ 1725879743783370752