Properties of cell death models calibrated and compared using Bayesian approaches
| Autor: | Jeremy L. Muhlich, William W. Chen, Peter K. Sorger, John N. Tsitsiklis, Dennis Vitkup, Hoda Eydgahi |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Eydgahi, Hoda, Tsitsiklis, John N. |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
biochemical networks
Bayesian probability Receptors Cytoplasmic and Nuclear Biology Bioinformatics Models Biological General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences Bayes' theorem 0302 clinical medicine Joint probability distribution Odds Ratio Computer Simulation Statistical physics 030304 developmental biology Parametric statistics 0303 health sciences Bayes estimator General Immunology and Microbiology Cell Death Applied Mathematics apoptosis Bayes factor Bayes Theorem modeling Models Theoretical Bayesian estimation 3. Good health Computational Theory and Mathematics Calibration Probability distribution General Agricultural and Biological Sciences Monte Carlo Method 030217 neurology & neurosurgery Information Systems Free parameter |
| Zdroj: | Molecular Systems Biology/Nature Publishing Group Molecular Systems Biology |
| Popis: | A Bayesian framework is used to calibrate a mass-action model of receptor-mediated apoptosis. Despite parameter non-identifiability and model ‘sloppiness', Bayes factor analysis discriminates between two alternative models of mitochondrial outer membrane permeabilization. Bayesian estimation returns statistically complete joint parameter distribution for mass-action models of receptor-mediated apoptosis calibrated to dynamic, live-cell data. Analysis of joint distributions reveals strong, non-linear correlations between parameters that are poorly captured by a conventional table of mean values and covariances; a high-dimensional distribution must therefore be reported as the true estimate of parameter values. Despite non-identifiablility and model ‘sloppiness,' a Bayesian framework returns probabilistic predictions for cell death dynamics that have tight confidence intervals and match experimental data. Use of a Bayesian framework to discriminate between two competing models of mitochondrial outer membrane permeabilization shows that a ‘direct' mechanism has ∼20-fold greater plausibility than an ‘indirect' mechanism, even though both models exhibit equally good fits to data for some parameters. Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing ‘direct' and ‘indirect' apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty. |
| Databáze: | OpenAIRE |
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