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，以进一步开发 推导癌细胞和组织预测计算模型的系统方法。这 系统生物学方法侧重于推导出高度受限(参数稀疏)的计算模型 来自一系列系统的药物-药物微扰实验，具有丰富的观测读数。这种方法 (COPIA-组合扰动-相互作用分析)因此处于最佳方法点 一方面，纯统计网络模型(通常不能捕捉到物理化学动力学 生物过程)和生化运动学的大型微分方程式模型(通常缺乏 足够的参数来真实地模拟细胞生物学过程)。该项目将从详细的 模型系统中胶质母细胞瘤亚型和耐药性的摄动实验和建模 黑色素瘤(与分项目1的协同作用)。 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方法(反转 工程学‘)和最优化。这两个赛道的工作已经开始，种子期限为一年 资金来自MSKCC实验治疗中心(ETC)。桑德集团已经建立了一个 Zuckerman研究中心的实验室(2008)，并实施了最佳生长条件和药物 针对胶质母细胞瘤来源的肿瘤球体和黑色素瘤细胞系的扰动方案。引航员 反相蛋白质阵列(RPPA)用于高通量数据采集的实验 戈登·米尔斯，医学博士安德森)正在进行中，2009年6月。 算法开发已经开始专注于几条有希望的路线。其中一个挑战是扩大模型 构建到具有数百个或更多节点的系统，对应的模型数量非常多 配置。我们目前正在探索统计物理的方法，以使这一过程易于管理， 更有效地导出离散边值上的概率分布，而不是更多 昂贵的集合特定解决方案的枚举。作为替代方案，我们正在探索其他算法 方法，如模块分解，从相关性分析和生成开始解决方案 优化算法。此外，我们还设计了一种方法，将安全的先验知识整合到 建模过程。计算方法的不断发展是一个关键的线程运行 通过所有的具体目标。 这个子项目的临床目标是设计有效的组合疗法，以解决 肿瘤亚型的多样性和治疗敏感与耐药肿瘤的差异，验证 细胞系和小鼠模型的组合药物方案，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.