Properties of cell death models calibrated and compared using Bayesian approaches
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 m...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Nature Publishing Group
2013
|
| Online Access: | http://hdl.handle.net/1721.1/78587 https://orcid.org/0000-0003-2658-8239 |
| _version_ | 1811081688880513024 |
|---|---|
| author | Eydgahi, Hoda Chen, William W. Muhlich, Jeremy L. Vitkup, Dennis Tsitsiklis, John N. Sorger, Peter K. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Eydgahi, Hoda Chen, William W. Muhlich, Jeremy L. Vitkup, Dennis Tsitsiklis, John N. Sorger, Peter K. |
| author_sort | Eydgahi, Hoda |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:50:55Z |
| format | Article |
| id | mit-1721.1/78587 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:50:55Z |
| publishDate | 2013 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/785872022-10-01T06:25:48Z Properties of cell death models calibrated and compared using Bayesian approaches Eydgahi, Hoda Chen, William W. Muhlich, Jeremy L. Vitkup, Dennis Tsitsiklis, John N. Sorger, Peter K. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Eydgahi, Hoda Tsitsiklis, John N. 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. National Institutes of Health (Grant CA139980) National Institutes of Health (Grant GM68762) 2013-04-24T15:55:22Z 2013-04-24T15:55:22Z 2013-02 2012-06 Article http://purl.org/eprint/type/JournalArticle 1744-4292 http://hdl.handle.net/1721.1/78587 Eydgahi, Hoda et al. “Properties of Cell Death Models Calibrated and Compared Using Bayesian Approaches.” Molecular Systems Biology 9 (2013). ©2013 Nature Publishing Group https://orcid.org/0000-0003-2658-8239 en_US http://dx.doi.org/10.1038/msb.2012.69 Molecular Systems Biology Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Nature Publishing Group Molecular Systems Biology/Nature Publishing Group |
| spellingShingle | Eydgahi, Hoda Chen, William W. Muhlich, Jeremy L. Vitkup, Dennis Tsitsiklis, John N. Sorger, Peter K. Properties of cell death models calibrated and compared using Bayesian approaches |
| title | Properties of cell death models calibrated and compared using Bayesian approaches |
| title_full | Properties of cell death models calibrated and compared using Bayesian approaches |
| title_fullStr | Properties of cell death models calibrated and compared using Bayesian approaches |
| title_full_unstemmed | Properties of cell death models calibrated and compared using Bayesian approaches |
| title_short | Properties of cell death models calibrated and compared using Bayesian approaches |
| title_sort | properties of cell death models calibrated and compared using bayesian approaches |
| url | http://hdl.handle.net/1721.1/78587 https://orcid.org/0000-0003-2658-8239 |
| work_keys_str_mv | AT eydgahihoda propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches AT chenwilliamw propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches AT muhlichjeremyl propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches AT vitkupdennis propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches AT tsitsiklisjohnn propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches AT sorgerpeterk propertiesofcelldeathmodelscalibratedandcomparedusingbayesianapproaches |