Regulatory Network Differences/Diverse Tumor Subtypes/Design Combinatorial Therap

这个子项目建立在桑德实验室最近的实验和计算工作的基础上， 系统的方法来推导癌细胞和组织的预测计算模型。这 系统生物学方法侧重于高度约束（参数稀疏）的计算模型， 从一系列系统的药物-药物扰动实验中获得丰富的观察读数。这种方法 （CoPIA -组合扰动相互作用分析）因此处于最佳方法点之间 一方面，纯统计网络模型（其通常不捕获物理化学动力学 生物过程）和生化动力学的大型微分方程模型（通常缺乏 足够的参数以真实地模拟细胞生物学过程）。该项目将从详细的 在胶质母细胞瘤亚型和药物抗性的模型系统中的扰动实验和建模 黑色素瘤（与分项目1协同作用）。 该项目的一个重点将是推动先进测量技术的应用 以及开发新的、更有效的网络模型构建算法（“反向 工程“）和优化。这两个方面的工作已经开始，有一年的种子期 MSKCC实验治疗中心（ETC）Sander集团已经建立了一个 Zuckerman研究中心的实验室（2008年），并实施了最佳生长条件和药物 用于胶质母细胞瘤衍生的肿瘤球和黑素瘤细胞系的微扰方案。试点 使用反相蛋白质阵列进行高通量数据采集的实验（RPPA，与 戈登米尔斯，医学博士安德森）是在2009年6月的进展。 航空业的发展已开始集中在几条有前途的航线上。一个挑战是扩大模型 构造具有数百个或更多节点的系统，对应于非常大量的模型 配置.我们目前正在探索统计物理学的方法，使这一过程易于管理， 通过在离散边缘值上更有效地推导概率分布，而不是更多地 昂贵的枚举集特定的解决方案。作为替代方案，我们正在探索其他算法 方法，如模块分解，从相关分析和遗传算法开始解决问题， 优化算法此外，我们设计了一种方法，将安全的先验知识， 建模过程。计算方法的不断发展是线程运行的关键 所有的具体目标。 该子项目的最终临床目标是设计有效的组合疗法， 肿瘤亚型的多样性和治疗敏感性与耐药性肿瘤的差异， 细胞系和小鼠模型中的组合药物方案，以及临床试验的设计。

This subproject builds on recent experimental and computafional work in the Sander lab to further develop a systematic approach to the derivation of predictive computational models for cancer cells and tissues. This systems biology approach focuses on highly constrained (parameter-sparse) computational models derived from a series of systematic drug-drug perturbation experiments with rich observational readout. This approach (CoPIA - combinatorial perturbation-interaction analysis) is thus at an optimal methodological point between purely statistical network models on the one hand (that typically do not capture the physico-chemical dynamics of biological processes) and large differential equation models of biochemical kinefics (that typically lack sufficient parameters to realistically simulate cell biological processes). The project will start with detailed perturbafion experiments and modeling in model systems for glioblastoma subtypes and for drug resistance in melanoma (synergy with sub-project 1). A key focus of the project will be in pushing fon/vard the applicafion of advanced measurement technologies and the development of novel, more efficient algorithmic approaches for network model construction ('reverse engineering') and opfimization. Work on both of these tracks has already been initiated with one-year seed funding from the MSKCC Experimental Therapeufics Center (ETC). The Sander group has established a laboratory in the Zuckerman Research Center (2008) and implemented opfimal growth conditions and drugdrug perturbafion protocols for both glioblastoma-derived tumorspheres and melanoma cell lines. Pilot experiments for high-throughput data acquisifion using reverse phase protein arrays (RPPA, collaborafion with Gordon Mills, MD Anderson) are in progress in June 2009. Algorithmic development has begun to focus on several promising routes. One challenge is to scale up model construction to systems with several hundred or more nodes, corresponding to a very large number of model configurations. We are currently exploring methods of statistical physics to make this process manageable, with the much more efficient derivation of probability distribufions over discrete edge values rather than more expensive enumerafion of sets particular solufions. As an alternative, we are exploring other algorithmic approaches, such as modular decomposition, starting solutions from correlafion analysis and genefic opfimizafion algorithms. In addifion, we have designed an approach to incorporate secure prior knowledge into the modeling process. Confinuous development of the computational approaches is a key thread running through all of the Specific Aims. The ulfimate clinical aim of this sub-project is the design of effecfive combinatorial therapies that address diversity in tumor subtypes and differences in therapy-sensitive versus resistant tumors, validation of the combinatorial drug protocols in cell lines and mouse models, and the design of clinical trials.