Warning: strtotime(): It is not safe to rely on the system's timezone settings. You are *required* to use the date.timezone setting or the date_default_timezone_set() function. In case you used any of those methods and you are still getting this warning, you most likely misspelled the timezone identifier. We selected the timezone 'UTC' for now, but please set date.timezone to select your timezone. in /var/www/html/www_publications/index.php on line 342

Warning: Cannot modify header information - headers already sent by (output started at /var/www/html/www_publications/index.php:342) in /var/www/html/www_publications/index.php on line 4076
Controlling Directed Protein Interaction Networks in Cancer (bibtex)
Controlling Directed Protein Interaction Networks in Cancer (bibtex)
by Kanhaiya, Krishna, Czeizler, Eugen, Gratie, Cristian and Petre, Ion
Abstract:
Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.
Reference:
Controlling Directed Protein Interaction Networks in Cancer (Kanhaiya, Krishna, Czeizler, Eugen, Gratie, Cristian and Petre, Ion), In Scientific Reports, volume 7, 2017.
Bibtex Entry:
@Article{Kanhaiya2017,
author        = {Kanhaiya, Krishna and Czeizler, Eugen and Gratie, Cristian and Petre, Ion},
title         = {Controlling Directed Protein Interaction Networks in Cancer},
journal       = {Scientific Reports},
year          = {2017},
volume        = {7},
number        = {1},
pages         = {10327},
month         = sep,
issn          = {2045-2322},
__markedentry = {[vrogojin:6]},
abstract      = {Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.},
refid         = {Kanhaiya2017},
url           = {https://doi.org/10.1038/s41598-017-10491-y},
}