A Systems Approach to Elucidate Mechanisms od Drug Activity and Sensivity

阐明药物活性和敏感性机制的系统方法

• 批准号: 8711772
• 负责人: ANDREA CALIFANO
• 金额: $ 32.49万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-24 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8711772
• 关键词: Algorithms; Apoptosis; Apoptotic; B-Lymphocytes; Binding; Biochemical; Biological Assay; Burkitt Lymphoma; Cell Cycle; Cell Cycle Arrest; Cell Line; Cells; Communities; Complement; Complementary DNA; Data; Disease; Dissection; FDA approved; Gene Proteins; Genes; Genomics; Glioma; Goals; Graph; Human; In Vitro; Individual; Instruction; MCF7 cell; Malignant neoplasm of prostate; Mammalian Cell; Maps; Mediating; Medicine; Methodology; Methods; Modeling; Molecular Genetics; Molecular Profiling; Mus; Nature; Non-Small Cell Adenocarcinoma; Pathologic; Pharmaceutical Preparations; Phenotype; Physiological; Process; Property; Public Domains; Publishing; RNA Interference; Regulation; Regulatory Pathway; Resistance; Structure of germinal center of lymph node; System; T-Lymphocyte; Tissue Sample; base; cell type; data modeling; drug sensitivity; genome-wide; human embryonic stem cell; in vivo; inhibitor/antagonist; malignant breast neoplasm; nerve stem cell; novel; novel strategies; pluripotency; programs; response; small molecule; stressor; tumor

The LINCS program offers a unique window on the mechanisms of response to small molecule and single gene perturbations by a diverse set of well-characterized cell lines. We propose to develop new algorithms for the analysis of the large-scale molecular profile data that will be made available by this program. These algorithms will help elucidate how response to small-molecule and biochemical perturbations is mediated by the genetic and molecular context of the cell and establish a predictive framework for the dissection of synergistic (i.e., non additive) perturbations. To achieve these goals, a new class of integrative methodologies is required, specifically tailored to the LINCS centers' data. Indeed, given the sparse, rather than genome-wide nature of these data, existing algorithms will not be directly applicable and will require either substantial customization or total rethinking. We have developed a repertoire of experimentally validated algorithms, such as ARACNe, for the dissection of transcriptional interactions (1, 2) and MINDy for post-translational interactions (3, 4) in mammalian cells. These are complements by methods for the integration of individual regulatory layers within multi-layer networks (5, 6), or interactomes for short. We have also pioneered algorithms (MARINa and IDEA) for interrogating interactomes to identify Master Regulator and Master Integrator genes, causally related to the presentation of pathologic (7-9) or physiologic phenotypes (5), and to elucidate compound Mechanism of Action (MoA) (6). Building on this extensive methodological base, we will introduce novel approaches, specifically tailored to the LINCS data, to (a) elucidate the MoA and activity of small molecule and RNAi/cDNA mediated perturbations (b) identify genes that mediate sensitivity or resistance to compound activity, and (c) identify gene-gene, gene-compound, and compound-compound interactions whose modulation can synergistically induce a desired endpoint phenotype (e.g., apoptosis, cell cycle arrest, loss of pluripotency, etc.). A specific emphasis of this proposal is on the use of LINCS in vitro signatures to predict such compound-related properties (MoA, activity, sensitivity, synergy, etc.) in vivo.

Lincs计划提供了一个独特的窗口 基因扰动通过各种表征良好的细胞系。我们建议开发新算法 为了分析该程序将提供的大规模分子剖面数据。这些 算法将有助于阐明对小分子和生化扰动的反应如何由 细胞的遗传和分子环境，并建立了解剖的预测框架 协同（即非添加剂）扰动。为了实现这些目标，是新的综合性类别 需要方法，专门针对Lincs中心的数据量身定制。确实，考虑到稀疏，相反 除了这些数据的全基因组性质，现有算法不会直接适用，并且需要 实质性定制或全面重新思考。 我们已经开发了通过实验验证的算法（例如Aracne）的曲目 哺乳动物细胞中转录相互作用的转录相互作用（1，2）和Mindy用于翻译后相互作用（3，4）。 这些是通过在多层中整合单个调节层的方法的补充 网络（5、6）或简称相互作用。我们还开创了算法（码头和想法） 询问相互作用体以识别主调节器和主体集成因子基因，与该基因相关 病理学（7-9）或生理表型（5）的表现，并阐明复合机制 动作（MOA）（6）。在这个广泛的方法基础的基础上，我们将介绍新颖的方法， 专门针对Lincs数据量身定制的，以（a）阐明小分子的MOA和活性 RNAi/cDNA介导的扰动（B）鉴定介导对化合物敏感性或抗性的基因 活性，（c）识别基因基因，基因化合物和化合物化合物相互作用 调节可以协同诱导所需的端点表型（例如，细胞凋亡，细胞周期停滞，丧失 多能等）。该建议的特定重点是使用lincs在体外特征中预测 这种与化合物相关的特性（MOA，活性，灵敏度，协同等）在体内。