(541 e) Integration of Signaling Networks with Differential Gene Regulation Data Using a Probabilistic Pathway Impact Analysis Approach

摘要

The objective of the present study is to integrate the information on intracellular signaling network structures with large-scale gene expression data sets to predict functional changes in the cellular pathways that are operational under those conditions. Conventional approaches do not consider the network structure and transform the signaling pathway structures into lists of proteins, map the corresponding gene expression to these proteins and compute statistical confidence for observing these changes against a random chance. As the network structure is ignored in such 'enrichment'-based methods, the results do not reflect the biological constraints and are likely to be erroneous in typical use. For example, consider the case when only a single component of a pathway with 10 total nodes is significantly down-regulated. The biological significance is likely to be dependent on whether the down-regulated component is an activator or an inhibitor and its location in the pathway structure. However, conventional methods ignore this important connectivity information. Recently, a method named SPIA was proposed to overcome this issue by incorporating the signaling pathway structure explicitly in assessing the impact of gene expression changes on cellular function (Traca et al., Bioinformatics 2009). A limitation of SPIA is that it considers the canonical signaling pathways in its assessment and does not reflect the particular biological context from which the gene expression data was obtained.