Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence.

Journal article


Figueredo, Grazziela P., Siebers, Peer-Olaf, Aickelin, Uwe, Whitbrook, Amanda and Garibaldi, Jonathan M. 2015. Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence. PLos ONE. https://doi.org/10.1371/journal.pone.0118359
AuthorsFigueredo, Grazziela P., Siebers, Peer-Olaf, Aickelin, Uwe, Whitbrook, Amanda and Garibaldi, Jonathan M.
Abstract

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new unfaced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex aging phenomena needs further understanding. It is known that immunosenescence is highly correlated to the negative effects of aging. In this work we advocate the use of simulation as a tool to assist the understanding of immune aging phenomena. In particular, we are comparing system dynamics modelling and simulation (SDMS) and agent-based modelling and simulation (ABMS) for the case of age-related depletion of naive T cells in the organism.We address the following research questions: Which simulation approach is more suitable for this problem? Can these approaches be employed interchangeably? Is there any benefit of using one approach compared to the other? Results show that both simulation outcomes closely fit the observed data and existing mathematical model; and the likely contribution of each of the naive T cell repertoire maintenance method can therefore be estimated. The differences observed in the outcomes of both approaches are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not interfere in the overall expected dynamics of the populations. In this case, therefore, they can be employed interchangeably, with SDMS being simpler to implement and taking less computational resources.

Advances in healthcare and in the quality of life significantly increase human life expectancy.
With the aging of populations, new unfaced challenges are brought to science. The
human body is naturally selected to be well-functioning until the age of reproduction to keep
the species alive. However, as the lifespan extends, unseen problems due to the body deterioration
emerge. There are several age-related diseases with no appropriate treatment;
therefore, the complex aging phenomena needs further understanding. It is known that
immunosenescence is highly correlated to the negative effects of aging. In this work we advocate
the use of simulation as a tool to assist the understanding of immune aging phenomena.
In particular, we are comparing system dynamics modelling and simulation (SDMS)
and agent-based modelling and simulation (ABMS) for the case of age-related depletion of
naive T cells in the organism.We address the following research questions: Which simulation
approach is more suitable for this problem? Can these approaches be employed interchangeably?
Is there any benefit of using one approach compared to the other? Results
show that both simulation outcomes closely fit the observed data and existing mathematical
model; and the likely contribution of each of the naive T cell repertoire maintenance method
can therefore be estimated. The differences observed in the outcomes of both approaches
are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not
interfere in the overall expected dynamics of the populations. In this case, therefore, they
can be employed interchangeably, with SDMS being simpler to implement and taking less
computational resources.

KeywordsArtificial immune systems; Immunosenescence; System dynamics modelling; Agent based modelling
Year2015
JournalPLos ONE
PublisherPublic Library of Science (PLoS)
ISSN19326203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0118359
Web address (URL)http://hdl.handle.net/10545/622386
hdl:10545/622386
Publication dates25 Mar 2015
Publication process dates
Deposited19 Mar 2018, 16:24
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Archived with thanks to PLOS ONE

ContributorsUniversity of Nottingham and Loughborough University
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