Regulatory Networks in Cancer Initiation and Progression

描述(申请人提供)：该计划将建立在已建立的和新的合作基础上，包括麻省理工学院合作部门在生物系统计算建模方面正在进行的活动，并将代表癌症生物学家(细胞和分子生物学家和遗传学家)和计算复杂的模型师之间紧密整合的合作努力，以分析癌细胞在体外和完整动物中的特性和行为，并生成可改进和可移植的癌症进展计算模型。该计划将专注于使用癌症进展的小鼠模型来生成基因组规模的数据集，这些数据集涉及与癌症进展的各个步骤相关的基因和蛋白质。这些数据集将被分析以寻找可重现的模式。来自体内模型以及从它们派生的细胞培养模型的过滤数据将被用作为有助于癌症进展的个别方面的细胞程序生成计算模型的底物。这些程序既包括细胞固有的程序，也包括对来自周围癌细胞的输入做出反应的程序。用于建模的初始浓度区域将包括：细胞对生长因子的反应、细胞-基质黏附和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。这项测试将导致对计算模型的验证和改进。这个数据生成和分析、计算建模和验证/改进的循环将被重复，以开发出在其环境中发挥作用的日益健壮和丰富的癌细胞模型。对熟悉不同方法及其有效整合的跨学科科学家的培训和教育将是该计划不可或缺的一部分，并将包括开发新的课程和教育活动，以补充麻省理工学院现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.