The temporal reconfiguration of regulatory networks: From yeast to cancer

调控网络的时间重构：从酵母到癌症

• 批准号: 7931962
• 负责人: AVIV REGEV
• 金额: $ 82.2万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7931962
• 关键词: Ascomycota; Cells; Complex; Computing Methodologies; Cues; Data Set; Development; Devices; Epigenetic Process; Evolution; Genetic; Genetic Transcription; Genomics; Growth; Hematopoiesis; Malignant Neoplasms; Measures; Molecular; Mutation; Nutritional; Organism; Phylogeny; Process; Research; Signal Transduction; System; Work; Yeasts; cancer cell; computer framework; cost; flexibility; information processing; novel; response

Molecular networks are the information processing devices of cells and organisms, transforming signals into coherent cellular responses. Networks are remarkably flexible and can re-configure in an adaptive response to perturbation. This ability is apparent at all levels - from fast epigenetic changes in response to environmental signals or developmental cues, to re-organization to accommodate pathological changes in cancer, to genetic changes underlying network evolution under selection. Reconfiguration is essential to networks' function as well as to their ability to evolve new functionality. We understand little, however, about how specific genetic and epigenetic changes allow novel functions to emerge in complex networks. Genomics has recently made it possible to collect massive datasets about temporally changing systems. Among molecular systems, regulatory networks controlling gene transcription are the most accessible for systems-scale analysis. The availability of scalable, cost-effective genomics approaches to systematically perturb and measure all levels of a transcriptional response along with sophisticated computational methods offer an extraordinary opportunity to study network function. We propose to develop a novel integrated experimental and computational framework to systematically decipher how regulatory networks assume novel adaptive functions through fast epigenetic changes or slow genetic changes. We will distinguish two types of temporal processes. For linear trajectories we will study epigenetic reconfiguration following a nutritional change, and genetic reconfiguration in yeast and cancer cells under selection. For lineages we will characterize the development of novel transcriptional states in the hematopoiesis ontogeny and the evolution of regulatory networks in the Ascomycota phylogeny. The work will unify disparate problems - including how cell adapts to changing growth conditions, how cancer develops, and how species evolve - under a single theoretical and methodological framework. It will help establish a new paradigm for genomics research by moving us from a static snapshot view to a fully dynamic perspective on molecular processes.

分子网络是细胞和生物体的信息处理装置，将信号转化为 连贯的细胞反应网络是非常灵活的，可以重新配置，以适应 扰动这种能力在各个层面都很明显--从对环境变化的快速表观遗传变化， 信号或发育线索，重组以适应癌症的病理变化，遗传 改变了选择下的网络进化。重构对于网络的功能至关重要， 以及它们发展新功能的能力。然而，我们对特定的基因是如何产生的知之甚少。 而表观遗传学的变化使得复杂网络中出现新的功能。 最近，基因组学使得收集关于随时间变化的系统的大量数据集成为可能。 在分子系统中，控制基因转录的调控网络是最容易被理解的。 系统规模分析可扩展的、具有成本效益的基因组学方法的可用性， 用复杂的计算方法干扰和测量转录反应的所有水平沿着 提供了一个学习网络功能的绝佳机会。 我们建议开发一种新的综合实验和计算框架， 解读调控网络如何通过快速的表观遗传变化或缓慢的 基因变化我们将区分两种类型的时间过程。对于线性轨迹，我们将研究 营养变化后的表观遗传重构，以及酵母和癌细胞中的遗传重构 在选择中。对于谱系，我们将描述新的转录状态的发展， 造血个体发育和子囊菌门发育中调控网络的进化。这项工作将 统一不同的问题-包括细胞如何适应不断变化的生长条件，癌症如何发展， 物种如何进化-在一个单一的理论和方法框架下。这将有助于建立一个新的 通过将我们从静态快照视图移动到完全动态的视角， 分子过程