Quant. methods for systems-level analyses of regulation & signaling dynamics

描述(由申请人提供)：生物体面临的一个基本系统层面的挑战是调节波动环境中的细胞生长。环境中养分供应的突然变化，或压力因子和化合物的存在，通常需要细胞生长的快速调整。细胞中的生长控制和其他功能是由一连串的细胞事件实例化的，表示为信号网络。我们将开发直接适用于生物学家的统计方法和算法，以量化已知信号网络指定的调节机制和动力学，提炼假想的信号动力学，并进行预测。我们的综合方法通过结合对在互补监管水平上运行的机制的实验研究来量化信令网络的影响，并利用现代高通量和测序技术。我们将从实验上验证、改进和改进驱动细胞生长的调控和信号动力学的图景。我们的方法可以推广到其他模式生物，从细菌到人类，以及其他功能和疾病。 目的1.开发理论、模型和算法，用于量化已知信令网络在多个监管水平上的影响，并从协调的实验数据中提炼信号动力学。(1a)多个协调细胞响应的去噪。(1B)鉴定在已知信号和代谢网络上具有重要作用的分子信号的结合基序、蛋白质和代谢物的协调动力学。(1C)从多个监管级别的数据中提炼给定的信令网络。(1D)基于生物常数的一致性估计。(1E)开源软件和网络工具。 目的2.发展预测酵母和哺乳动物肿瘤系统中细胞增殖的方法。(2)通过结合核小体运动(Solexa-seq)、蛋白质-DNA结合(ChIP-seq)、基因表达(RNA-seq)、蛋白质丰度(质谱仪)的时间进程来量化协调调控动态。(2B)在DNA可及性的情况下，预测协调转录和翻译的结合基序图。(2C)通过结合四个调控水平上的协调时间进程来预测环境变化(营养、压力和药物)引起的细胞生长动态：基因表达(RNA-seq)、蛋白质丰度(质谱)、蛋白质-DNA结合(ChIP-seq)、代谢物浓度(浊度)。(2D)改进癌症干细胞中细胞分化和生长的系统水平信号机制。

DESCRIPTION (provided by applicant): A fundamental systems-level challenge for living organisms is the regulation of cellular growth in a fluctuating environment. Sudden changes in nutrient availability in the environment or the presence of stress factors and chemical compounds typically require rapid adjustments of cellular growth. Growth control and other functions in the cell are instantiated by cascades of cellular events, represented as signaling networks. We will develop statistical methods and algorithms of direct utility to biologists for quantifying regulation mechanisms and dynamics specified by a known signaling network, for refining hypothesized signaling dynamics, and for making predictions. Our integrative approach quantifies the effects of signaling networks, by combining experimental studies of mechanisms that operate at complementary levels of regulation, and takes advantage of modern high-throughput and sequencing technologies. We will experimentally validate, refine and improve the portrait of regulation and signaling dynamics driving cellular growth. Our approach is generalizable to other model organisms, from bacteria to human, and to other functions and diseases. Aim 1. Develop theory, models, and algorithms for quantifying the effects of known signaling networks at multiple levels of regulation, and for refining signaling dynamics from coordinated experimental data. (1a) De-noising of multiple coordinated cellular responses. (1b) Identification of coordinated dynamics of binding motifs, proteins and metabolites with a significant role in carrying molecular signals on known signaling and metabolic networks. (1c) Refinement of a given signaling network from data at multiple regulation levels. (1d) Consistent estimation based on biological constants. (1e) Open source software and web tools. Aim 2. Develop methods to predict cellular proliferation in yeast and mammalian cancer systems. (2a) Quantification of coordinated regulation dynamics by combining time-courses on nucleosome movements (solexa-seq), protein-DNA binding (ChIP-seq), gene expression (RNA-seq), protein abundance (mass-spec). (2b) Map of binding motifs that predict coordinated transcription and translation, given DNA accessibility. (2c) Prediction of cellular growth dynamics due to environmental changes (nutrients, stress and drugs) by combining coordinated time-courses at four levels of regulation: gene expression (RNA-seq), protein abundance (mass-spec), protein-DNA binding (ChIP-seq), metabolite concentrations (turbidostat). (2d) Refinement of systems-level signaling mechanisms of cellular differentiation and growth in cancer stem cells.