Intercellular Interactions Modulate Carcinogenesis Course: A Dynamic System Study

细胞间相互作用调节癌发生过程：动态系统研究

• 批准号: 8137431
• 负责人: Lynn Hlatky
• 金额: $ 2.5万
• 依托单位: STEWARD ST. ELIZABETH'S MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8137431
• 关键词: System; carcinogenesis

To study cancer, very powerful modern experimental methods ~ including high-throughput approaches ~, and modern statistics/computer-science methods are now used. Mechanistic quantitative models of cancer timedevelopment, from very early stages to clinical disease, have been somewhat less well explored. A highly interdisciplinary, unusually closely interacting team of cancer biologists, molecular biologists, mathematicians, physicists, and computer scientists has been assembled to work toward dynamic, mechanistic carcinogenesis models so parsimoniously parameterized they have predictive capability. The overriding goal is to make the models more realistic and credible by taking into account the influence of intercellular interactions during carcinogenesis. The time-course of effects involving a diverse, interacting cell population and/or of tumor progression, a comparatively late stage in the lengthy cancer latency period, will be emphasized. The phenomena of microscopic tumor dormancy, recently found to be almost ubiquitous in humans, and of emergence from dormancy due to "switching on" of blood supply recruitment will be one main focus. The team will work under an iterative paradigm: experiments a mathematical/ computational model a experiments, etc. Data will be gathered from various sources: in vitro experiments, including modern high throughput results and in depth studies of particular proteins; experiments using genetically engineered mice; highly developed imaging approaches; and results in the literature on human cancer incidence or mortality. Several ofthe individual projects will involve ionizing radiation, a carcinogen whose action has been unusually well characterized at very small time and length scales, as well as over a whole human lifetime and for whole organisms, so that it is highly informative about cancer development in general. Mathematical methods will include the following: classic mathematical biology approaches with systems of ordinary or partial differential equations; modern discrete and hybrid discrete-continuous models based on treating many cells, each represented as an entity which can interact with other cells and with its microenvironment according to comparatively simple rules, with complicated wholesystem behavior arising from those rules; probabilistic "stochastic-process" models, needed since most new stages of cancer evolution putatively originate with a single cell, at risk for eradication or accidental extinction; and multi time-scale computational modeling, in which effects governed by comparatively very short time scales influence much longer time-scale cancer evolution and vice-versa.

为了研究癌症，现在使用非常强大的现代实验方法（包括高通量方法）和现代统计/计算机科学方法。癌症时间发展（从非常早期阶段到临床疾病）的机械定量模型尚未得到很好的探索。由癌症生物学家、分子生物学家、数学家、物理学家和计算机科学家组成的高度跨学科、互动异常密切的团队已经聚集在一起，致力于建立动态的、机械性的致癌模型，这些模型的参数化程度非常低，因此具有预测能力。最重要的目标是通过考虑致癌过程中细胞间相互作用的影响，使模型更加现实和可信。涉及多种相互作用的细胞群和/或肿瘤的效应的时间过程 进展是漫长的癌症潜伏期中相对较晚的阶段，将得到强调。最近发现的微观肿瘤休眠现象在人类中几乎普遍存在，以及由于“开启”血液供应募集而从休眠状态中苏醒将成为主要焦点之一。 该团队将在迭代范式下工作：实验、数学/计算模型、实验等。数据将从各种来源收集：体外实验，包括现代高通量结果和特定蛋白质的深入研究；使用基因工程小鼠进行实验；高度发达 成像方法；以及有关人类癌症发病率或死亡率的文献中的结果。其中几个项目将涉及电离辐射，这是一种致癌物质，其作用在非常小的时间和长度范围内，以及在整个人类一生和整个有机体中的作用已被非常清楚地表征，因此它可以提供有关癌症发展的总体信息。 。数学方法将包括以下内容：经典 使用常微分方程或偏微分方程系统的数学生物学方法；现代离散和混合离散连续模型基于处理许多细胞，每个细胞表示为一个实体，可以根据相对简单的规则与其他细胞及其微环境相互作用，具有复杂的整个系统 由这些规则引起的行为；概率“随机过程”模型，因为癌症进化的大多数新阶段被认为起源于单个细胞，有被根除或意外灭绝的风险，所以需要这种模型；以及多时间尺度计算模型，其中由相对非常短的时间尺度控制的效应影响更长时间尺度的癌症进化，反之亦然。