Dynamic Bayesian System Modeling and Structure Learning

The systems structure learning step integrates a selected set of significantly perturbed pathways and gene groups to construct a plausible system level model of the disease/condition under study. The selection and refinement is iterative in that input from the research biologists/scientist are essential in the selection process. The system model encompasses the whole time-course patterns and multi-conditional behaviors of a large group of genes/proteins ("omics" and other physiological responses). The system (disease) models can be used for more efficient comparative modeling, pattern recognition and simulations supporting "what-if" type of analyses. Two system model generation approaches are employed at the Seralogix. First is a top-down reconstruction method based on merging of pathways with known gene/protein relationships and the second is based on a bottom-up network learning approach based on the merger of gene/protein data and prior knowledge (i.e. known and/or predicted protein-protein interactions). The structure learning algorithms incorporate prior biological knowledge combined with multi-perturbation data (such as from knockout studies) to learn the system model network structure. Large amount of perturbation data provides better learning results. Figure 1 illustrates a simple merged model from the host response for a pathogen gene knockout study. Figure 2 is a magnified section of Figure 1 model. Only 3 pathways were selected as the seed pathways to constrain the network learning to a smaller set of genes. Figure 1b is an enlarged section of the system network at 36 hours post infection.

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Figure 1. Merged Systems Model Ready for Interrogation

A combination of visualization aids and methods for interrogation of a network model is necessary to hypothesize genetic mechanisms and identify targets of intervention or mechanisms of diseases. The merger of related pathways allows for the interrogation of a single systems offering a better view for discovering altered states. For each experimental condition, the trained network model can be interrogated for mechanistic genes across all time points and for the strength (correlation) of connections between interconnecting genes.

Figure 2. Magnified Section of System Model

This is a magnified section of the Figure 1 system model. Computational methods are available to sort and identify gene relationships which may be altered due to experimental conditions. Such analysis offers great insight for making novel discoveries.