Structural and Dynamical Specificity on Intracellular Signaling Networks

描述（由申请人提供）：细胞区分大量的内部和外部状态，它们以背景依赖性和历史偏向性的方式对这些状态作出反应，从而影响基本过程，例如分裂、修复和细胞死亡。为了调和潜在的大量的内部和外部状态，值得与组装大多数细胞内信号系统的组件的非常小的池区分，需要相当大的网络可塑性，这是通过共享组件或它们在多个复合物中的重复使用成为可能。我将通过模型来探索这种由实证研究提出的架构是否足以使信号真正诱导和塑造最终处理它们的网络。我设想这是通过竞争性招募共享信号组件的动态发生的，从而导致锁定“赢家网络”的自催化反馈。这种情况与预配置的网络随时准备处理它们专用的信号的观点形成鲜明对比。我认为，在基因表达动态反馈回路的背景下理解信号动态是很重要的。信号网络控制着基因的表达，而基因又控制着这些信号网络中的蛋白质水平。这种总体反馈包括慢时间尺度和快时间尺度（分别是基因表达和信号传导）。这种时间尺度的分离可能是细胞内学习和记忆的简单形式的基础。分子组分的广泛多效性传达了可塑性的优点，但也可能限制蛋白质相互识别的准确性。蛋白质识别特异性的降低会导致网络错误，即网络结构本身的波动。我将了解蛋白质-蛋白质识别中的错误如何影响细胞反应。我将通过简单模型的数学和数值研究来实现所提出的目标，这些模型捕获了组件重用、复合物形成和蛋白质-蛋白质识别。我的建议福尔斯完全符合国家卫生研究院的使命，因为组织的正常生理和许多系统性病理都取决于细胞对各种生长因子、细胞因子、激素和其他主要信号的反应。理解和治疗许多人类疾病的最大挑战之一是获得细胞如何对其环境作出反应的基本图像。随着我们对细胞信号处理的内部工作原理的详细了解的增加，这种理解将使我们能够通过提供特定疾病中出现的问题以及人类干预如何克服和规避这些错误来制定越来越有效的对抗和治疗疾病的策略。

DESCRIPTION (provided by applicant): Cells distinguish a large number of internal and external states to which they respond in a context- dependent and history-biased manner affecting fundamental processes such as division, repair and cell death. To reconcile the potentially large number of internal and external states worth distinguishing with the comparably small pool of components from which most intracellular signaling systems are assembled, requires considerable network plasticity which is made possible through the sharing of components or their reuse in multiple complexes. I will explore by means of models whether this architecture, suggested by empirical studies, is sufficient for enabling signals to actually induce and shape the networks that end up processing them. I envision this to occur by a dynamics of competitive recruiting of shared signaling components, leading to autocatalytic feedbacks that lock in a "winner network". Such a scenario is in marked contrast to a view in which pre-configured networks stand ready to process signals to which they are dedicated. I believe it is important to understand signaling dynamics in the context of a feedback loop with gene expression dynamics. Signaling networks control the expression of genes, which control the protein levels in these very signaling networks. This overall feedback comprises slow and fast time scales (gene expression and signaling, respectively). Such separation of time scales may underlie simple forms of intracellular learning and memory. Extensive pleiotropy of molecular components conveys advantages of plasticity, but may also limit the accuracy by which proteins recognize each other. Reduced protein recognition specificity causes network error, that is, fluctuations in the network structure itself. I will develop an understanding of how errors in protein-protein recognition affect cellular responses. I will accomplish the proposed aims through the mathematical and numerical investigation of simple models that capture component reuse, complex formation and protein-protein recognition. My proposal falls strongly within the mission of the NIH, since the normal physiology of tissues and many of their systemic pathologies hinges on the response of cells to a variety of growth factors, cytokines, hormones and other primary signals. One of the greatest challenges underlying the understanding and treatment of many human diseases is obtaining a fundamental picture of how cells react to their environment. As our detailed knowledge of the inner workings of cellular signal processing increases, such an understanding will allow us to develop more and more effective strategies for combating and curing diseases by providing a clear portrait of what goes wrong in particular diseases and how human intervention can overcome and circumvent such errors.