A thermodynamic-based approach for the resolution and prediction of protein network structures

Efrat Flashner-Abramson, Jonathan Abramson, Forest M. White, Nataly Kravchenko-Balasha*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront. We suggest studying cancer from a thermodynamic-based point of view. We hypothesize that by modelling biological systems based on physico-chemical laws, highly complex systems can be reduced to a few parameters, and their behavior under varying conditions, including response to therapy, can be predicted. Here we validate the predictive power of our thermodynamic-based approach, by uncovering the protein network structure that emerges in MCF10a human mammary cells upon exposure to epidermal growth factor (EGF), and anticipating the consequences of treating the cells with the Src family kinase inhibitor, dasatinib.

Original languageAmerican English
Pages (from-to)20-30
Number of pages11
JournalChemical Physics
StatePublished - 25 Oct 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.


  • Cancer-altered signaling
  • Cell signaling
  • Drug response prediction
  • Information theory
  • Protein networks
  • Surprisal analysis
  • Thermodynamic-based approach


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