Pathway network inference from gene expression data

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Title: Pathway network inference from gene expression data
Authors: Ponzoni, Ignacio | Nueda, María José | Tarazona, Sonia | Götz, Stefan | Montaner, David | Dussaut, Julieta Sol | Dopazo, Joaquín | Conesa, Ana
Research Group/s: Sistemas Dinámicos y Estadística (SISDINEST)
Center, Department or Service: Universidad de Alicante. Departamento de Estadística e Investigación Operativa
Keywords: Biological systems | Gene expression data | Functional connections | Pathway | PANA
Knowledge Area: Estadística e Investigación Operativa
Issue Date: 13-Mar-2014
Publisher: BioMed Central
Citation: BMC Systems Biology 2014, 8(Suppl 2):S7. doi:10.1186/1752-0509-8-S2-S7
Abstract: 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.
Sponsor: This work has been supported by the FP7 STATegra project, grant 306000, by CONICET (National Research Council of Argentina), grant PIP112-2009-0100322, and by Universidad Nacional del Sur (Bahía Blanca, Argentina), grant PGI 24/N032.
ISSN: 1752-0509
DOI: 10.1186/1752-0509-8-S2-S7
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2014 Ponzoni et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Peer Review: si
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Research funded by the EU
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