GPU Accelerated Drug Application on Signaling Pathways Containing Multiple Faults Using Boolean Networks

Cell growth is governed by the flow of information from growth factors to transcription factors. This flow involves protein-protein interactions known as a signaling pathway, which triggers the cell division. The biological network in the presence of malfunctions leads to a rapid cell division without any necessary input conditions. The effect of these malfunctions or faults can be observed if it is simulated explicitly in the Boolean derivative of the biological networks. The consequences thus produced can be nullified to a large extent, with the application of a reduced combination of drugs. This paper provides an insight into the behavior of the signaling pathway in the presence of multiple concurrent malfunctions. First, we simulate the behavior of malfunctions in the Boolean networks. Next, we apply the drug therapy to reduce the effects of malfunctions. In our approach, we introduce a parameter called probabilistic_score, which identifies the reduced drug combinations without prior knowledge of the malfunctions, and it is more beneficial in realistic cancerous conditions. The combinations of different custom drug inhibition points are chosen to produce more efficient results than known drugs. Our approach is significantly faster as GPU acceleration has been carried out during modeling the multiple faults/malfunctions in the Boolean networks.