Hasenauer, Jan ORCID: 0000-0002-4935-3312, Hasenauer, Christine, Hucho, Tim ORCID: 0000-0002-4147-9308 and Theis, Fabian J. (2014). ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics. PLoS Comput. Biol., 10 (7). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1553-7358

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Abstract

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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Hasenauer, JanUNSPECIFIEDorcid.org/0000-0002-4935-3312UNSPECIFIED
Hasenauer, ChristineUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hucho, TimUNSPECIFIEDorcid.org/0000-0002-4147-9308UNSPECIFIED
Theis, Fabian J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-434212
DOI: 10.1371/journal.pcbi.1003686
Journal or Publication Title: PLoS Comput. Biol.
Volume: 10
Number: 7
Date: 2014
Publisher: PUBLIC LIBRARY SCIENCE
Place of Publication: SAN FRANCISCO
ISSN: 1553-7358
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
PARAMETER-ESTIMATION; MAXIMUM-LIKELIHOOD; GENE-EXPRESSION; GROWTH-FACTOR; MAPK CASCADE; SINGLE-CELL; NETWORKS; DISTRIBUTIONS; IDENTIFIABILITY; OPTIMIZATIONMultiple languages
Biochemical Research Methods; Mathematical & Computational BiologyMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/43421

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