癌症进展的等待时间模型
Waiting time models of cancer progression
摘要：
癌症进展是由肿瘤细胞群体中的突变和选择驱动的进化过程。我们讨论的数学模型的癌症进展，从传统的多阶段理论。每个阶段都与遗传变异的发生及其在种群中的固定有关。我们描述了使用合取贝叶斯网络，指数家庭的等待时间模型，其中突变的发生被约束到一个部分的时间顺序突变的积累。如果突变遵循线性顺序或独立发生，则会出现两种相反的极限情况。我们推导出精确的解析表达式的等待时间，直到特定数量的突变已经积累在这些极限情况下，以及一般的合取贝叶斯网络。最后，我们分析了一个随机群体遗传学模型，明确占突变和选择。在这个模型中，克隆扩张的浪潮以等距离的间隔席卷整个种群。我们提出了一个近似的解析表达式在这个模型中的等待时间，并比较它的合取贝叶斯网络得到的结果。
英文摘要：
Cancer progression is an evolutionary process that is driven by mutation and selection in a population of tumor cells. We discuss mathematical models of cancer progression, starting from traditional multistage theory. Each stage is associated with the occurrence of genetic alterations and their fixation in the population. We describe the accumulation of mutations using conjunctive Bayesian networks, an exponential family of waiting time models in which the occurrence of mutations is constrained to a partial temporal order. Two opposing limit cases arise if mutations either follow a linear order or occur independently. We derive exact analytical expressions for the waiting time until a specific number of mutations have accumulated in these limit cases as well as for the general conjunctive Bayesian network. Finally, we analyze a stochastic population genetics model that explicitly accounts for mutation and selection. In this model, waves of clonal expansions sweep through the population at equidistant intervals. We present an approximate analytical expression for the waiting time in this model and compare it to the results obtained for the conjunctive Bayesian networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
