Industrial methodology for process verification in research (IMPROVER): Toward systems biology verification

Pablo Meyer, Julia Hoeng, J. Jeremy Rice, Raquel Norel, Jörg Sprengel, Katrin Stolle, Thomas Bonk, Stephanie Corthesy, Ajay Royyuru, Manuel C. Peitsch, Gustavo Stolovitzky

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations


Motivation: Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions. Results: We present here, a review of the current state of systems biology verification and a detailed methodology to address its shortcomings. This methodology named 'Industrial Methodology for Process Verification in Research' or IMPROVER, consists on evaluating a research program by dividing a workflow into smaller building blocks that are individually verified. The verification of each building block can be done internally by members of the research program or externally by 'crowd-sourcing' to an interested community. Implementation: This methodology could become the preferred choice to verify systems biology research workflows that are becoming increasingly complex and sophisticated in industrial and academic settings.

Original languageEnglish (US)
Article numberbts116
Pages (from-to)1193-1201
Number of pages9
Issue number9
StatePublished - May 2012
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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