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ODE constrained mixture modelling: A method for unraveling subpopulation structures and dynamics.
PLoS Comput. Biol. 10:e1003686 (2014)
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Trail-induced Apoptosis; Gene-expression; Biochemical Pathways; Confidence-intervals; Parameter-estimation; Global Optimization; Maximum-likelihood; Escherichia-coli; Growth-factor; Mapk Cascade
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Zeitschrift PLoS Computational Biology
Quellenangaben Band: 10, Heft: 7, Seiten: , Artikelnummer: e1003686 Supplement: ,
Verlag Public Library of Science (PLoS)
Verlagsort San Francisco
Begutachtungsstatus peer-reviewed