Prediction of Novel Pathway Elements and Interactions Using Bayesian Networks

Signalling and regulatory pathways that guide gene expression have only been partially defined for most organisms. Given the increasing number of microarray measurements, it may be possible to reconstruct such pathways and uncover missing connections directly from experimental data. One major question in the area of microarray-based pathway analysis is the prediction of new elements to a particular pathway. Such prediction is possible by independently testing the effects of added genes or variables on the overall scores of the corresponding expanded networks. A general network expansion framework to predict new components of a pathway was suggested in 2001 (Tanay and Shamir, 2001). Many machine learning approaches for identifying hidden or unknown factors have appeared in the literature recently (Gat-Viks and Shamir, 2007; Hashimoto, et al., 2004; Herrgard, et al., 2003; Ihmels, et al., 2002; Needham, et al., 2009; Parikh, et al., 2010; Pena, et al., 2005; Tanay and Shamir, 2001; Yu and Li, 2005). Compared to existing pathway expansion methods based on correlation, Boolean, or other strategies (Hashimoto, et al., 2004; Herrgard, et al., 2003; Ihmels, et al., 2002; Tanay and Shamir, 2001), Bayesian network-based expansion methods provide distinct advantages. A Bayesian network (BN) is a representation of a joint probability distribution over a set of random variables (Friedman, et al., 2000). Bayesian networks are able to identify causal or apparently causal relationships (Friedman, et al., 2000), and can be used to predict both linear and nonlinear functions. Furthermore, BN analysis is robust to error and noise and easily interpretable by humans. Bayesian network-based expansion has been used for gene expression data analysis (Gat-Viks and Shamir, 2007; Pena, et al.). We have recently developed an algorithm termed “BN+1” which implements Bayesian network expansion to predict new factors and interactions that participate in a specific pathway (Hodges, et al., 2010; Hodges, et al., 2010). This algorithm has been tested using E. coli microarray data (Hodges, et al., 2010) and verified with a synthetic network (Hodges, et al., 2010). This Book Chapter aims to first provide a detailed review on different computational methods for pathway element prediction, introduce how a BN analysis is typically performed, and then describe how this BN+1 algorithm works. We will also introduce our MARIMBA software program (http://marimba.hegroup.org) which can implement the BN+1 algorithm along with many other useful features. So far, the success of BN+1 in new pathway element prediction has been demonstrated in prokaryotic E. coli system. This paper will introduce our new study of applying BN+1 to predict new pathway elements for