Regulatory Networks in Cancer Initiation and Progression

描述（由申请人提供）：该项目将建立在现有的和新的合作基础上，包括正在进行的麻省理工学院合作部门的生物系统计算建模活动，它将代表癌症生物学家（细胞和分子生物学家以及遗传学家）与计算复杂的建模师之间的紧密合作，以分析体外和完整动物体内癌细胞的特性和行为，并生成可细化和便携式的癌症进展计算模型。该项目将专注于使用小鼠癌症进展模型来生成与癌症进展的各个步骤相关的基因和蛋白质的基因组规模数据集。这些数据集将被分析为可重复的模式。来自体内模型的过滤数据，以及来自它们的细胞培养模型，将被用作生成细胞程序计算模型的基础，这些计算模型有助于癌症进展的各个方面。这将包括细胞固有的程序，以及响应来自周围环境的癌细胞输入的程序。建模的初始集中领域将包括：细胞对生长因子的反应，细胞基质粘附和DNA损伤以及细胞极化和迁移。该计划的一个重要组成部分将是通过RNA干扰对体内和细胞模型系统进行扰动来测试计算模型，并在适当的情况下对携带癌症模型的小鼠进行基因工程。该测试将导致计算模型的验证和细化。这种数据生成和分析、计算建模和验证/改进的循环将不断重复，以开发出越来越强大和丰富的癌细胞在其环境中功能的模型。培训和教育熟悉不同方法的跨学科科学家及其有效整合将是该计划的一个组成部分，并将包括开发新课程和教育活动，以补充麻省理工学院已有的课程和教育活动。

DESCRIPTION (provided by applicant): The program will build on established and new collaborations, including ongoing activities in computational modeling of biological systems in cooperating MIT departments, and will represent a closely integrated collaborative effort between cancer biologists (cell and molecular biologists and geneticists) and computationally sophisticated modelers to analyze the properties and behavior of cancer cells in vitro and in intact animals and to generate refinable and portable computational models of cancer progression. The program will focus on the use of murine models of cancer progression to generate genome-scale data sets on the genes and proteins associated with the various steps in cancer progression. These data sets will be analyzed for reproducible patterns. Filtered data from the in vivo models, as well as from cell culture models derived from them, will be used as substrates for generation of computational models for cellular programs contributing to individual aspects of cancer progression. These will include both cell-intrinsic programs as well as programs responding to inputs to the cancer cells from their surroundings. Areas of initial concentration for modeling will include: cellular responses to growth factors, cell-matrix adhesion and DNA damage as well as cell polarization and migration. An important component of the program will be testing of the computational models by perturbation of the in vivo and cellular model systems by RNA interference and, where appropriate, genetic engineering of the mice harboring the cancer models. This testing will lead to validation and refinement of the computational models. This cycle of data generation and analysis, computational modeling and validation/refinement will be repeated to develop increasingly robust and rich models of cancer cells functioning in their environment. Training and education of interdisciplinary scientists conversant with the different approaches and their effective integration will be an integral part of the program and will include the development of new courses and educational activities to complement those already in place at MIT.