Intercellular Interactions Modulate Carcinogenesis Course: A Dynamic System Study

描述(申请人提供)：为了研究癌症，现在使用了非常强大的现代实验方法--包括高通量方法--以及现代统计学/计算机科学方法。从非常早期到临床疾病的癌症时间发展的机械性定量模型，在某种程度上还没有得到很好的探索。一个由癌症生物学家、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、异常密切互动的团队已经聚集在一起，致力于建立动态的、机械的癌症发生模型，这些模型被如此简约地参数化，以至于具有预测能力。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。将强调涉及不同的、相互作用的细胞群体和/或肿瘤进展的时间进程，这是漫长的癌症潜伏期中的一个相对较晚的阶段。最近发现在人类中几乎无处不在的微观肿瘤休眠现象，以及由于血液供应重新启动而从休眠中苏醒的现象，将是一个主要的焦点。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，以及对特定蛋白质的深入研究；使用基因工程小鼠的实验；高度发展的成像方法；以及关于人类癌症发病率或死亡率的文献结果。其中几个单独的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尺度上，以及整个人类一生和整个生物体，都得到了很好的描述，因此它对癌症的总体发展具有很高的信息量。数学方法将包括以下内容：用常微分方程组或偏微分方程组处理经典数学生物学方法；以处理多个细胞为基础的现代离散和混合离散-连续模型，每个细胞都可按相对简单的规则与其他细胞及其微环境相互作用，这些规则产生复杂的全系统行为；概率“随机过程”模型，因为大多数新的癌症进化阶段假定始于单个面临根除或意外灭绝风险的细胞；多时间尺度计算模型，其中较短时间尺度的影响影响较长时间尺度的癌症进化，反之亦然。

DESCRIPTION (provided by applicant): 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 time-development, 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, mathematical/computational model, 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 of the 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 whole-system 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.