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

描述（由申请人提供）：生物体的一个基本系统级挑战是在波动环境中调节细胞生长。环境中养分供应的突然变化或应激因子和化合物的存在通常需要快速调整细胞生长。细胞中的生长控制和其他功能是由细胞事件的级联来实例化的，这些事件被表示为信号网络。我们将开发统计方法和算法的直接效用的生物学家量化的调节机制和动态指定的一个已知的信号网络，细化假设的信号动态，并作出预测。我们的综合方法量化信号网络的影响，通过结合实验研究的机制，在互补水平的监管，并利用现代高通量和测序技术。我们将通过实验验证，完善和改善调控和信号动力学驱动细胞生长的肖像。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。 目标1.开发理论，模型和算法，用于量化已知信号网络在多个调节水平的影响，并从协调的实验数据中精炼信号动力学。(1a)多个协调细胞响应的去噪。(1b)识别结合基序，蛋白质和代谢物的协调动力学，在已知的信号传导和代谢网络上携带分子信号方面具有重要作用。(1c)根据多个监管级别的数据对给定信令网络进行细化。(1d)基于生物常数的一致性估计。(1e)开源软件和网络工具。 目标2.开发预测酵母和哺乳动物癌症系统中细胞增殖的方法。(2a)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）的时间过程来定量协调调节动力学。(2b)结合基序的地图，预测协调转录和翻译，给定DNA的可及性。 (2c)通过在四个调控水平上结合协调的时间过程来预测由于环境变化（营养素、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（turbidostat）。(2d)癌症干细胞分化和生长的系统水平信号机制的完善。 公共卫生相关性：从单细胞到多细胞生物，细胞生长是高度保守的。它在高等生物中的破坏在从病毒感染到癌症的各种疾病中发挥作用。我们提供统计方法来描述在多个监管层面上运作的协调机制的增长。这种机制的观点将能够设计可以诱导感兴趣的细胞反应的扰动，包括降低细胞增殖的速度和分化干细胞的期望表型性状。

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. PUBLIC HEALTH RELEVANCE: Cellular growth is highly conserved from unicellular to multicellular organisms. Its disruption in higher organisms plays a role in a variety of disorders from viral infection to cancer. We provide statistical methods to characterize growth in terms of coordinated mechanisms that operate at multiple levels of regulation. This mechanistic perspective will enable the design of perturbations that can induce cellular responses of interest, including reduced speed of cellular proliferation, and desired phenotypical traits of differentiating stem cells.