Drug Vector Minimization in Cancer Therapy based on Boolean Logic Model of Gene Regulatory Network

Cell signalling regulates and coordinates the action of the cells. After receiving any external stimuli, gene regulatory networks (GRN) activated by the signalling pathways. GRN is the biological representation of the molecular regulators. In computational logic, the GRNs can be visualized as the Boolean logic model where interactions between the different pathway components are modelled with Boolean logic gates. Sometimes cells acquire genetic alterations that drive adverse transcriptional methods and results pathway faults. These aberrant cell signalling and pathway faults are the primary cause of cancer. The defects in the signalling pathway can be mapped as the faults in the Boolean network. The available drug set can overturn some of the faults in the GRN and is used in the therapeutic purposes. In this paper, we propose a novel method to identify the minimized drug set for the breast cancer based on the experimental findings of the corresponding growth factor (GF) pathways. The test pattern of drug vector covers maximum malfunctions and generates non-cancerous state. The minimized drug vector will reduce the cost of drug and the chance of drug side effects. Alongside, the suggestion for the drug location is also given which may be employed by the biologist in future to develop some new drug set for advanced treatment. The proposed technique is more accurate and generalized than the other methods and can be utilized in interdisciplinary research areas.