The temporal reconfiguration of regulatory networks: From yeast to cancer

分子网络是细胞和生物体的信息处理设备，将信号转化为 一致的细胞反应。网络非常灵活，可以重新配置以适应 微扰。这种能力在所有层面上都很明显--从对环境的快速表观遗传变化 信号或发育信号，重组以适应癌症的病理变化，遗传 在选择下更改底层网络演进。重新配置对于网络的功能是必不可少的 以及它们进化新功能的能力。然而，我们对具体的遗传基因了解很少 表观遗传变化允许在复杂的网络中出现新的功能。 基因组学最近使收集有关时间变化的系统的海量数据集成为可能。 在分子系统中，控制基因转录的调控网络是最容易获得的 系统尺度分析。可扩展的、经济高效的基因组学方法的可用性 干扰和测量转录反应的所有水平，以及复杂的计算方法 提供了学习网络功能的绝佳机会。 我们建议开发一种新的集成实验和计算框架，以系统地 解读调控网络如何通过快速或缓慢的表观遗传变化承担新的适应功能 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组，以及酵母和癌细胞中的基因重组 在选择下。对于谱系，我们将描述新的转录状态的发展 子囊菌系统发育中的造血个体发育和调控网络的进化。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，以及 物种如何进化--在单一的理论和方法框架下。这将有助于建立一个新的 通过将我们从静态的快照视图转移到完全动态的视角来实现基因组研究的范式 分子过程。

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.