ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models a...

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Autores principales:	Jan Hasenauer, Christine Hasenauer, Tim Hucho, Fabian J Theis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/939b3d72246243999e4f49303f11881e
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id	oai:doaj.org-article:939b3d72246243999e4f49303f11881e
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spelling	oai:doaj.org-article:939b3d72246243999e4f49303f11881e2021-11-25T05:41:00ZODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.1553-734X1553-735810.1371/journal.pcbi.1003686https://doaj.org/article/939b3d72246243999e4f49303f11881e2014-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24992156/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.Jan HasenauerChristine HasenauerTim HuchoFabian J TheisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 7, p e1003686 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jan Hasenauer Christine Hasenauer Tim Hucho Fabian J Theis ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
description	Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.
format	article
author	Jan Hasenauer Christine Hasenauer Tim Hucho Fabian J Theis
author_facet	Jan Hasenauer Christine Hasenauer Tim Hucho Fabian J Theis
author_sort	Jan Hasenauer
title	ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
title_short	ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
title_full	ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
title_fullStr	ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
title_full_unstemmed	ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
title_sort	ode constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/939b3d72246243999e4f49303f11881e
work_keys_str_mv	AT janhasenauer odeconstrainedmixturemodellingamethodforunravelingsubpopulationstructuresanddynamics AT christinehasenauer odeconstrainedmixturemodellingamethodforunravelingsubpopulationstructuresanddynamics AT timhucho odeconstrainedmixturemodellingamethodforunravelingsubpopulationstructuresanddynamics AT fabianjtheis odeconstrainedmixturemodellingamethodforunravelingsubpopulationstructuresanddynamics
_version_	1718414531338698752

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

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