Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models.

ABSTRACT: The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates

SUBMITTER: Zhu P 

PROVIDER: S-EPMC4796823 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature