Thus, an effective clinical application of this drug depends upon the ability to identify different tumor cell populations while they are dormant. Our work may serve to provide insights to the application of this new drug as well by contributing to the development of new early detection methods. In addition, our work may shed light on why the immune system may not always be able to prevent tumor progression. Specifically, our work shows that even if the immune system maintains its strength throughout the tumor growth process, there is still a high possibility that the immune system could eventually fail, which is to be contrasted with the simple explanations that it becomes weaker as the tumor develops. Also, for a tumor of a specific type, we can extract the parameter values in our model by fitting our simulation results to the statistics of a real in-vitro or in-vivo tumor of this type. Then we can utilize our model to explore optimal treatment strategies for the tumors of this specific type. In addition, once we determine the effects of a specific microenvironmental factor on the parameter values in our model, we could then study the effects of this microenvironmental factor on the tumor growth dynamics. Our current CA dormancy model is still preliminary, and to achieve our ultimate goal of understanding cancer dormancy and progression, we need to develop robust models that incorporate appropriate cell-level tumor-host interactions that are informed by experiments. For Remdesivir GS-5734 example, by explicitly considering angiogenesis and using more realistic distribution of “transformed” tumor cells’ resistance to microenvironmental suppression factors, our model might be able to yield more realistic results and improve our understanding of cancer dormancy and progression. Also, the effects of tumor cell competition, cooperation and the microenvironmental changes caused by tumor cell activities could be incorporated to further strengthen our CA dormancy model. It is noteworthy that although we employ interaction rules based on a discrete cell model to describe “competition” between the tumor and the microenvironmental suppression factors, alternatives such as evolutionary game theory implemented by partial-differential equations are also available to address the interplay between the tumor and the microenvironment. First, hybrid methods such as PacToCA, LSC and ECTools correct read errors using high-quality short reads obtained by second-generation sequencing such as Illumina and 454. These methods map or align short reads or unitigs assembled from short reads to long reads first and then correct errors in long reads by making a consensus sequence.