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GPU Accelerated Drug Application on Signaling Pathways Containing Multiple Faults Using Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2020-08-04 , DOI: 10.1109/tcbb.2020.3014172
Tapan Chowdhury 1 , Susanta Chakraborty 1 , Argha Nandan 2
Affiliation  

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.

中文翻译:

使用布尔网络在包含多个故障的信号通路上的 GPU 加速药物应用

细胞生长受从生长因子到转录因子的信息流控制。这种流动涉及称为信号通路的蛋白质-蛋白质相互作用,它触发细胞分裂。存在故障的生物网络会导致快速的细胞分裂,而无需任何必要的输入条件。如果在生物网络的布尔导数中明确模拟,则可以观察到这些故障或故障的影响。通过减少药物组合的应用,可以在很大程度上消除由此产生的后果。本文提供了对信号通路在存在多个并发故障时的行为的见解。首先,我们模拟布尔网络中的故障行为。下一个,我们应用药物治疗来减少故障的影响。在我们的方法中,我们引入了一个名为probabilistic_score ,它在没有事先了解故障的情况下识别减少的药物组合,并且在现实的癌症条件下更有益。选择不同定制药物抑制点的组合以产生比已知药物更有效的结果。我们的方法明显更快,因为在布尔网络中的多个故障/故障建模期间执行了 GPU 加速。
更新日期:2020-08-04
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