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We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-371.

Title:
An empirical Bayesian approach for model-based inference of cellular signaling networks | BMC Bioinformatics | Full Text
Description:
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. Conclusion 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.
Website Age:
25 years and 10 months (reg. 1999-08-06).

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🚀 Good Traffic: 50k - 100k visitors per month


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Keywords {🔍}

model, chain, distribution, parameters, google, scholar, article, data, markov, erbb, signaling, values, parameter, bayesian, figure, predictions, proposal, pubmed, chains, shown, cas, approach, models, mathematical, obtained, total, convergence, algorithm, amcmc, initial, egf, experimental, factor, shc, phosphorylated, function, observed, uncertainty, estimate, step, space, structure, mcmc, file, system, network, grb, biology, steps, receptor,

Topics {✒️}

g-protein-coupled signal transduction monte carlo algorithm monte carlo technique monte carlo sampling gelman-rubin convergence metrics le novere hkn full size image bmc bioinformatics 2007 bmc bioinformatics 10 high-dimensional parameter space computationally-intensive bayesian techniques authors scientific editing privacy choices/manage cookies epidermal growth factor gelman-rubin psrf statistics membrane-dependent signal integration high dimensional problems egf-binding affinities arises signal transduction networks pdf 106 kb markov chain represents authors’ original file clathrin-mediated endocytosis gelman-rubin statistic article klinke biomed central cumulative markov chain collective markov chain evolving markov chain markov chain generated gelman-rubin method national academies press bayesian model-based inference gelman-rubin statistics adaptive metropolis algorithm linear pharmacokinetic equations including significant covariance differential algebraic equations reaction path degeneracy oxford university press dimensional reduction implies signal transduction models cellular signaling networks protein-protein interaction fischer-tropsch synthesis cellular signaling pathways acetylcholine receptor channel markov chains generated bayesian inference problems protein interaction domains

Questions {❓}

  • Lazebnik Y: Can a biologist fix a radio?
  • Markevich NI, Moehren G, Demin OV, Kiyatkin A, Hoek JB, Kholodenko BN: Signal processing at the Ras circuit: what shapes Ras activation patterns?
  • The primary goal of the analysis of these reaction pathways is to make predictions: what do we expect to happen in a particular reacting mixture under particular reaction conditions, given our current understanding of molecular interactions?

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