Intercellular Interactions Modulate Carcinogenesis Course: A Dynamic System Study

描述（由申请人提供）：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——和现代统计学/计算机科学方法。癌症时间发展的机制定量模型，从非常早期的阶段到临床疾病，一直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，研究动态的、机械的致癌模型，这些模型是如此简约地参数化，以至于具有预测能力。最重要的目标是通过考虑到癌变过程中细胞间相互作用的影响，使模型更加现实和可信。将强调涉及多种相互作用的细胞群和/或肿瘤进展的效应的时间过程，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普遍存在的微观肿瘤休眠现象，以及由于血液供应补充的“开启”而从休眠中出现的现象，将是一个主要的焦点。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；基因工程小鼠实验；高度发达的成像方法；以及关于人类癌症发病率和死亡率的文献。个别项目中有几个将涉及电离辐射，这是一种致癌物，其作用在非常短的时间和长度范围内，以及在整个人类一生和整个生物体中都得到了非常好的表征，因此它对癌症的发展具有很高的信息。数学方法将包括以下内容：常用或偏微分方程系统的经典数学生物学方法；现代离散和混合离散-连续模型基于对许多细胞的处理，每个细胞都表示为一个实体，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，这些规则产生复杂的全系统行为；需要概率“随机过程”模型，因为大多数癌症进化的新阶段被认为起源于有被消灭或意外灭绝风险的单个细胞；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会影响更长的时间尺度的癌症演变，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.