ArticleBMC bioinformatics2009
An empirical Bayesian approach for model-based inference of cellular signaling networks.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 67 citations in OpenAlex.
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- Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor GrowthFrontiers in pharmacology · 2016Article
- Interlocked positive and negative feedback network motifs regulate β-catenin activity in the adherens junction pathway.Molecular biology of the cell · 2015Article
- In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.Current opinion in chemical engineering · 2015Article
- A primer on Bayesian inference for biophysical systems.Biophysical journal · 2015Review
- A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems.FEBS open bio · 2015Article
- An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway.PLoS computational biology · 2014Article
- Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach.The Journal of general physiology · 2014Article
- Optimal experiment design for model selection in biochemical networks.BMC systems biology · 2014Article
- Exploring higher-order EGFR oligomerisation and phosphorylation--a combined experimental and theoretical approach.Molecular bioSystems · 2013Article
- Parameter trajectory analysis to identify treatment effects of pharmacological interventions.PLoS computational biology · 2013Article
- Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.PLoS computational biology · 2013Article
Corrections and comments
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1 author at 1 institution in 1 country.
Funding
Abstract
backgroundA 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.
resultsAs 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.
conclusionIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.