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制定量模型，已经有点不太好探索。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组装起来，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。涉及不同的相互作用的细胞群和/或肿瘤的效应的时间过程 在漫长的癌症潜伏期中，进展是一个相对较晚的阶段，将受到强调。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募集的“开启”而从休眠中出现的现象将是一个主要焦点。 该团队将在迭代模式下工作：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;使用基因工程小鼠的实验;高度发达的 成像方法;以及关于人类癌症发病率或死亡率的文献结果。个别项目中有几个将涉及电离辐射，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具有很高的信息性。数学方法将包括以下内容：经典 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，每个代表作为一个实体，可以相互作用与其他细胞和其微环境根据相对简单的规则，与复杂的整体 这些规则产生的行为;概率“随机过程”模型，因为大多数癌症演变的新阶段起源于单个细胞，有根除或意外灭绝的风险;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。

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.