BackgroundRecently, melanoma has become the most malignant and commonly occurring skin cancer. Melanoma is not only the major source (75%) of deaths related to skin cancer, but also it is hard to be treated by the conventional drugs. Recent research indicated that angiogenesis is an important factor for tumor initiation, expansion, and response to therapy. Thus, we proposed a novel multi-scale agent-based computational model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment.ResultsOur multi-scale agent-based model can simulate the melanoma tumor growth with angiogenesis under combined drug treatment. The significant synergistic effects between drug Dox and drug Sunitinib demonstrated the clinical potential to interrupt the communication between melanoma cells and its related vasculatures. Also, the sensitivity analysis of the model revealed that diffusivity related to the micro-vasculatures around tumor tissues closely correlated with the spread, oscillation and destruction of the tumor.ConclusionsSimulation results showed that the 3D model can represent key features of melanoma growth, angiogenesis, and its related micro-environment. The model can help cancer researchers understand the melanoma developmental mechanism. Drug synergism analysis suggested that interrupting the communications between melanoma cells and the related vasculatures can significantly increase the drug efficacy against tumor cells.
Abstract. Smith-Waterman (SW) algorithm, which calculates the similarity between two given sequences, is broadly used in bioinformatics research field. However, the time complexity of the SW algorithm prevents it from being used for long sequence alignment. Since SW algorithm is based on dynamic programing, using single instruction multiple data parallel computing algorithm can significantly reduce the computing cost. For this reason, this review introduces three commonly used parallel computing algorithms based on Compute Unified Device Architecture (CUDA) for SW algorithm acceleration as well as illustrates their advantages and disadvantages.
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