The species translation challenge - A systems biology perspective on human and rat bronchial epithelial cells

Carine Poussin, Carole Mathis, Leonidas G. Alexopoulos, Dimitris E. Messinis, Rémi H.J. Dulize, Vincenzo Belcastro, Ioannis N. Melas, Theodore Sakellaropoulos, Kahn Rhrissorrakrai, Erhan Bilal, Pablo Meyer, Marja Talikka, Stéphanie Boué, Raquel Norel, John J. Rice, Gustavo Stolovitzky, Nikolai V. Ivanov, Manuel C. Peitsch, Julia Hoeng

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


The biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and in the field of toxicology, and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to " translate" the results to humans. In this context, the SBV IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research) collaborative initiative, which uses crowd-sourcing techniques to address fundamental questions in systems biology, invited scientists to deploy their own computational methodologies to make predictions on species translatability. A multi-layer systems biology dataset was generated that was comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, generation, processing and quality control analysis of the multi-layer omics dataset accessible in public repositories for further intra- and inter-species translation studies.

Original languageEnglish (US)
Article number140009
JournalScientific Data
StatePublished - Jun 10 2014
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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