Dynamic causal modelling of immune heterogeneity

Abstract An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection—even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the...

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Autores principales:	Thomas Parr, Anjali Bhat, Peter Zeidman, Aimee Goel, Alexander J. Billig, Rosalyn Moran, Karl J. Friston
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c5a0622510dd4edd9d484209203bee98
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spelling	oai:doaj.org-article:c5a0622510dd4edd9d484209203bee982021-12-02T18:24:53ZDynamic causal modelling of immune heterogeneity10.1038/s41598-021-91011-x2045-2322https://doaj.org/article/c5a0622510dd4edd9d484209203bee982021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91011-xhttps://doaj.org/toc/2045-2322Abstract An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection—even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay—based on sequential serology—that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines.Thomas ParrAnjali BhatPeter ZeidmanAimee GoelAlexander J. BilligRosalyn MoranKarl J. FristonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Thomas Parr Anjali Bhat Peter Zeidman Aimee Goel Alexander J. Billig Rosalyn Moran Karl J. Friston Dynamic causal modelling of immune heterogeneity
description	Abstract An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection—even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay—based on sequential serology—that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines.
format	article
author	Thomas Parr Anjali Bhat Peter Zeidman Aimee Goel Alexander J. Billig Rosalyn Moran Karl J. Friston
author_facet	Thomas Parr Anjali Bhat Peter Zeidman Aimee Goel Alexander J. Billig Rosalyn Moran Karl J. Friston
author_sort	Thomas Parr
title	Dynamic causal modelling of immune heterogeneity
title_short	Dynamic causal modelling of immune heterogeneity
title_full	Dynamic causal modelling of immune heterogeneity
title_fullStr	Dynamic causal modelling of immune heterogeneity
title_full_unstemmed	Dynamic causal modelling of immune heterogeneity
title_sort	dynamic causal modelling of immune heterogeneity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c5a0622510dd4edd9d484209203bee98
work_keys_str_mv	AT thomasparr dynamiccausalmodellingofimmuneheterogeneity AT anjalibhat dynamiccausalmodellingofimmuneheterogeneity AT peterzeidman dynamiccausalmodellingofimmuneheterogeneity AT aimeegoel dynamiccausalmodellingofimmuneheterogeneity AT alexanderjbillig dynamiccausalmodellingofimmuneheterogeneity AT rosalynmoran dynamiccausalmodellingofimmuneheterogeneity AT karljfriston dynamiccausalmodellingofimmuneheterogeneity
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Dynamic causal modelling of immune heterogeneity

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