Intercellular Interactions Modulate Carcinogenesis Course: A Dynamic System Study

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-和现代统计学/计算机科学方法。从非常早期阶段到临床疾病的癌症时间发展的机械定量模型，已经稍微不太好地探索。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、机械的致癌模型，这些模型的参数化非常简单，它们具有预测能力。最重要的目标是使模型更加现实和可信的考虑在致癌过程中的细胞间相互作用的影响。将强调涉及不同的，相互作用的细胞群和/或肿瘤进展的影响的时间过程，在漫长的癌症潜伏期的相对较晚的阶段。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;高度发达的成像方法;以及人类癌症发病率或死亡率文献中的结果。有几个个别项目将涉及电离辐射，这是一种致癌物质，其作用在非常短的时间和长度范围内以及在整个人的一生和整个生物体中得到了非常好的表征，因此它对一般癌症的发展提供了大量信息。数学方法将包括以下内容：经典的数学生物学方法与系统的普通或偏微分方程;现代离散和混合离散-连续模型的基础上处理许多细胞，每一个代表作为一个实体，可以相互作用与其他细胞和与其微环境根据比较简单的规则，与复杂的整个系统的行为所产生的这些规则;概率“随机过程”模型，因为大多数癌症演变的新阶段起源于有根除或意外灭绝风险的单个细胞;以及多时间尺度计算建模，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.