An empirical Bayesian approach for model-based inference of cellular signaling networks Export

@article{citeulike:6093089, abstract = {BACKGROUND:A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways.RESULTS:As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies.CONCLUSIONS:In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.}, author = {Klinke, David}, citeulike-article-id = {6093089}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-371}, doi = {10.1186/1471-2105-10-371}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {adaptive\_markov\_chain\_monte\_carlo, bayesian\_methods, mcmc, signaling\_pathway\_inference, signaling\_pathways, systems\_biology}, number = {1}, pages = {371+}, posted-at = {2009-11-10 17:43:41}, priority = {2}, title = {An empirical Bayesian approach for model-based inference of cellular signaling networks}, url = {http://dx.doi.org/10.1186/1471-2105-10-371}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:6093089 L3 - citeulike-article-id:6093089 N2 - BACKGROUND:A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways.RESULTS:As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies.CONCLUSIONS:In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - An empirical Bayesian approach for model-based inference of cellular signaling networks VL - 10 SP - 371 KW - adaptive_markov_chain_monte_carlo KW - bayesian_methods KW - mcmc KW - signaling_pathway_inference KW - signaling_pathways KW - systems_biology AU - Klinke, David PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-371 ER -