Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis
Figure 1
Schematic of Bayesian network reverse engineering and Monte Carlo simulation.
A. Genetic, gene expression and phenotypic data are prepared for modeling by formatting followed by investigation to select the appropriate data transformation for the particular data type. Confounding factors and other explanatory variables are considered and modeled if appropriate. B. Likely fragments for network reconstruction are identified by scoring all 2-, 3- and 4-variable combinations with the constraint that SNPs are causally upstream. There are too many scored combinations to consider all during network reconstruction. The fragments that had the most likely Bayesian scores for each individual variable were identified and retained for network reconstruction. C. Parallel global network sampling constructs an ensemble of 1024 network structures that explain the data. The probabilistic directionality computed by the Bayesian framework allows inferences to be made about what lies upstream and downstream of particular phenotypic variables D. Diversity in network structures identified during network reconstruction captures uncertainty in the model. Hypotheses are extracted from the network ensemble by completing Monte Carlo simulations of “what-if” scenarios. Down-regulating the blue transcript would be expected to impact both TJ and SJ, while leaving CRP unchanged. The change in these phenotypic parameters would further predict reactive transcription changes in the liver. The separation of upstream and downstream components can identify potential intervention points and markers.