03070nas a2200385 4500008004100000022001400041245005700055210005600112260000900168300000700177490001400184520197500198653002202173653001502195653002002210653003002230653002902260653002002289653001502309653003002324653001602354653001202370653002902382653002002411653001402431100002102445700001802466700002002484700001802504700002002522700002102542700002002563700001602583856008502599 2014 eng d a1752-050900aPathway network inference from gene expression data.0 aPathway network inference from gene expression data c2014 aS70 v8 Suppl 23 a

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.

10aAlzheimer Disease10aCell Cycle10aDNA Replication10aGene Expression Profiling10aGene Regulatory Networks10aGluconeogenesis10aGlycolysis10aOxidative Phosphorylation10aProteolysis10aPurines10aSaccharomyces cerevisiae10aSystems biology10aUbiquitin1 aPonzoni, Ignacio1 aNueda, María1 aTarazona, Sonia1 aGötz, Stefan1 aMontaner, David1 aDussaut, Julieta1 aDopazo, Joaquin1 aConesa, Ana uhttp://clinbioinfosspa.es/content/pathway-network-inference-gene-expression-data