Properties of cell death models calibrated and compared using Bayesian approaches

Molecular Systems Biology

Hoda Eydgahi¹  William W Chen¹  Jeremy L Muhlich¹  Dennis Vitkup³  John N Tsitsiklis² 

[1] Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA;Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA;Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA

关键词: apoptosis;    Bayesian estimation;    biochemical networks;    modeling;   

DOI  :  10.1038/msb.2012.69

来源: Wiley

【 摘 要 】

Abstract

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.

Synopsis

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.

【 授权许可】

CC BY-NC-SA   
Copyright © 2013 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.