Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS)

  • Alexander M. Aliper
  • Michael B. Korzinkin
  • Natalia B. Kuzmina
  • Alexander A. Zenin
  • Larisa S. Venkova
  • Philip Yu. Smirnov
  • Alex A. Zhavoronkov
  • Anton A. Buzdin
  • Nikolay M. BorisovEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1613)


Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.

Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.

Here we show two principal points:
  1. 1.

    First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified.

  2. 2.

    Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.


Key words

Systems biology Mitogenic cell signaling Protein-protein interaction Parameter sensitivity/stiffness analysis RNA microarray analysis 


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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Alexander M. Aliper
    • 1
    • 2
    • 3
  • Michael B. Korzinkin
    • 2
    • 3
  • Natalia B. Kuzmina
    • 4
  • Alexander A. Zenin
    • 4
  • Larisa S. Venkova
    • 2
    • 3
  • Philip Yu. Smirnov
    • 4
  • Alex A. Zhavoronkov
    • 1
    • 2
    • 3
  • Anton A. Buzdin
    • 1
    • 2
    • 3
    • 5
    • 6
  • Nikolay M. Borisov
    • 2
    • 3
    • 6
    Email author
  1. 1.Drug Research and Design DepartmentPathway PharmaceuticalsWan ChaiHong Kong SAR
  2. 2.Department of Personalized MedicineFirst Oncology Research and Advisory CenterMoscowRussia
  3. 3.Laboratory of BioinformaticsD. Rogachev Federal Research Center of Pediatric Hematology, Oncology and ImmunologyMoscowRussia
  4. 4.Laboratory of Systems BiologyA.I. Burnazyan Federal Medical Biophysical CenterMoscowRussia
  5. 5.Group for Genomic Regulation of Cell Signaling SystemsShemyakin-Ovchinnikov Institute of Bioorganic ChemistryMoscowRussia
  6. 6.National Research Centre “Kurchatov Institute”, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and TechnologiesMoscowRussia

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