A thermodynamic-based approach for the resolution and prediction of protein network structures
Name
White_A thermodynamic-based approach for the resolution and prediction of protein network structures.pdf
Description
Accepted version
Size
3.38 MB
Format
Adobe PDF
Checksum (MD5)
125b3caf0de55c82d8e09a03cb2b4c17
Author(s)
Flashner-Abramson, Efrat
Abramson, Jonathan
White, Forest M
Kravchenko-Balasha, Nataly
Date Issued
2018
Journal
Chemical Physics
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2018 Elsevier B.V. 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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CHEMPHYS.2018.03.005
