Crosstalk pathway inference using topological information and biclustering of gene expression data.

Detection of crosstalks among pathways is a challenging task, which requires the identification of different types of interactions associated with cellular processes. A common strategy used in bioinformatics consists in extrapolating pathway associations from the pairwise analysis of some genes related to them, using gene expression data and topological information. PET, the method proposed in this paper, goes a step further by incorporating a strategy for the detection of correlation across conditions between differentially expressed genes based on biclustering analysis. In order to evaluate the performance of this new approach, a comparison with two recently published algorithms was carried out. The methods were contrasted in the inference of pathway associations from Alzheimer disease datasets, where the new proposal presents a higher crosstalk discoveries' rate. Finally, the analysis of the biological relevance of the pathway associations inferred by PET has shown the soundness of the extracted knowledge.

Pathway network inference from gene expression data.

BACKGROUND: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. RESULTS: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. CONCLUSIONS: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.