Modeling Tissue Division Patterns and Loss of Cell Polarity in Cancer

癌症是现代社会面临的最大问题之一。2015年，美国有160万新发癌症病例和59万例癌症相关死亡。数学和计算模型使关键的检查机制，可能参与肿瘤的启动和发展。由于组织的持续更新，一些个体人类细胞在一生中分裂多达5000次，积累了可能导致细胞癌变的有害突变。主要研究者将测试组织累积突变的概率如何取决于细胞的空间组织，以及发生初始致癌突变的细胞的发育阶段。确定哪些细胞分裂模式减缓了突变积累并延迟了癌症的发生，将促进我们对驱动肿瘤发生机制的理解，并提供可以通过实验验证的定量预测。该项目将通过参与跨学科领域的研究来培训本科生和研究生。 本研究项目中开发的数学模型将被纳入q-bio暑期学校的癌症动力学暑期课程，该课程向来自数学，物理和计算机科学等不同学科的学生介绍生物学建模。 动态模型将用于研究细胞分化层次对各种癌症类型的癌症发展和治疗结果的估计时间的影响。将在不同细胞类型及其空间位置的随机模型中探索对称细胞分裂和通过不对称分裂产生更分化的细胞之间的权衡。在细胞内水平上，将开发调节不对称分裂的细胞组分（细胞极性）的不均匀分布模型。 计算测试将检查极性生物化学网络中不同关键蛋白质的突变的影响，并将其与组织的群体水平模型中的宏观行为相关联。该项目将有助于了解不对称分裂机制的破坏如何作为癌症促进机制。

Cancer represents one of the biggest problems for modern societies. In 2015, there were 1.6 million new cancer cases and 590,000 cancer-related deaths in the US. Mathematical and computational models enable critical examination of mechanisms that may be involved in the initiation and development of tumors. Due to ongoing renewal of tissue, some individual human cells divide as many as 5000 times during a lifetime, accumulating deleterious mutations that may result in a cell becoming cancerous. The principal investigator will test how the probability of tissues accumulating mutations depends on the spatial organization of cells, and the developmental stage of the cell where the initial cancer-causing mutation occurs. Determining which cellular division patterns slow down mutation accumulation and delay the onset of cancer will advance our understanding of mechanisms driving tumorigenesis and provide quantitative predictions that can be validated experimentally. The project will train undergraduate and graduate students through research involvement in an interdisciplinary field. The mathematical models developed in this research project will be incorporated into a summer course on Cancer Dynamics in the q-bio Summer School that introduces students from diverse disciplines such as mathematics, physics, and computer science to modeling in biology. Dynamical models will be used to study the effects of cellular differentiation hierarchy on the estimated time to cancer development and treatment outcomes in various cancer types. Trade-offs between symmetric cell division and the production of more differentiated cells via asymmetric division will be explored in a stochastic model of different cell types and their spatial locations. On an intracellular level, a model for unequal distribution of cellular components (cellular polarity) that regulates asymmetric divisions will be developed. Computational tests will examine the effects of mutations of different key proteins in the polarity biochemical network and relate them to macroscopic behavior in a population level model of tissue. This project will aid in understanding how disruption of asymmetric division mechanisms can act as a cancer promoting mechanism.