ArticlePLoS computational biology2014
ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 54 citations in OpenAlex.
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- Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway.Methods in molecular biology (Clifton, N.J.) · 2023Review
- What's next for computational systems biology?Frontiers in systems biology · 2023Review
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
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- Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks.Cell systems · 2020Article
- Intrinsic and extrinsic noise of gene expression in lineage trees.Scientific reports · 2019Article
- Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection.NPJ systems biology and applications · 2019Article
- Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis.Bioinformatics (Oxford, England) · 2018Article
- Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines.BMC systems biology · 2017Article
- Performance of objective functions and optimisation procedures for parameter estimation in system biology models.NPJ systems biology and applications · 2017Article
- Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.BMC systems biology · 2016Article
- MEMO: multi-experiment mixture model analysis of censored data.Bioinformatics (Oxford, England) · 2016Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.