Intercellular Interactions Modulate Carcinogenesis Course: A Dynamic System Study

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。涉及多种相互作用的细胞群和/或肿瘤的效应的时间过程

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.