Cancer Systems Biology & Epidemiology: Application in Target Identification,Combinatorial Drug Therapy & Personalized Medicine

Abstract

Cancer-signalling networks are typically complex which involves gene regulation,
signalling, cell metabolism, and the alterations in its dynamics caused by the several
different types of mutations leading to malignancy. Computational model of networks make
possible to understand the complex behaviour of cancer-signalling network. Correlation
between complexity (clustering coefficient) of cancer-signalling network pathway and
Cancer Epidemiological data sets (cancer incidence, death rate and lifetime risk of cancer)
has been validated. Results of study support the initial assumption, that the complexity of
network matrices is a direct indicator of cancer threat. Understanding the differential
behaviour of regulatory networks during health, disease and in response to drugs play a
crucial role to enhance drug development efforts, new target identification, delineation of
off-target effects, methods of disease prediction, combinatorial drug regimens and also in
development of molecularly targeted personalized treatment.