Center for Systematic Modeling of Cancer Development

本研究的总体目标是建立一个多尺度的乳腺癌研究模型平台， 特别强调肿瘤起始细胞（TIC）的作用。该建模平台将主要包括 两个密切相关的组成部分：生物实验和数学计算建模。为 实验部分，我们寻求使用新开发的实验和成像方法来识别， 定位、纯化和表征TIC。将设计进一步的实验来发现空间定位 和运动，以及乳腺癌TIC的基因表达和细胞信号传导的特异性变化。组合 功能基因组学和数据挖掘策略将使我们能够表征新的生长调节剂。此外，本发明还 我们结合实验和系统生物学的方法将使我们能够评估对实验的反应， 可以以以前不可能的方式特异性抑制或杀死TIC的治疗剂。的数学 建模部分，我们将开发生物信息学和生物成像模型，以整合和分析数据 从生物实验中产生，并利用从数据分析中获得的信息， 建立计算机模型的知识，以模拟TIC行为、癌细胞凋亡、细胞迁移、周期和药物 治疗反应。除了提供一个基本的框架，了解乳腺癌的机制 癌症干细胞进化的模型也可以产生假设或实验设计。更重要的是， 这些模型将使人们能够预测正在调查的生物状态，并预测自然界如何 进程将在各种情况下表现。这两个组件之间的迭代反馈将改进我们的 平台进一步。最终目标是建立一个乳腺癌生物学的综合建模平台， 可以忠实地模拟体内过程，足以作为h5TDOthesis生成和筛选工具， 在遥远的将来，作为评估临床程序及其预期结果的工具。

The overall goal ofthis proposal is to build a multi-scale modeling platform for investigation ofthe breast cancer, with special emphasis on the roles ofthe tumor-initiating cells (TIC). This modeling platform will mainly consist of two closely related components: biological experiments and mathematical computational modeling. For the experiment component, we seek to use newly developed experimental and imaging methodologies to identify, localize, purify and characterize TIC. Further experiments will be designed to discover the spatial localization and movement, and specific changes in gene expression and cellular signaling of breast cancer TIC. Combined functional genomics and data mining strategies will allow us to characterize novel growth regulators. Further, our combined experimental and systems biology approach will allow us to evaluate responses to experimental therapeutics that may inhibit or kill TIC specifically in a manner not possible before. For the mathematical modeling component, we will develop bioinformatics and bio-imaging models to integrate and analyze the data generated from biological experiments, and make use ofthe information obtained from data analysis, biological knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cycle and drug treatment response. Besides providing a basic framework for understanding the mechanism underlying breast cancer stem cell evolution, the models can also give birth to hypotheses or experimental design. More important, these models will allow one to predict the biological state under investigation and predict how the natural process will behave in various circumstances. Iterative feedback between these two components will refine our proposed platform further. The ultimate goal is an integrated modeling platform of breast cancer biology that can mimic in vivo processes faithfully enough to serve as a h5TDOthesis-generation and screening tool, and in the distant future, as a tool for evaluating clinical procedures and their expected outcomes.