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，它们以上下文相关和历史偏见的方式对这些状态做出反应，影响基本过程，如分裂、修复和细胞死亡。为了协调潜在的大量值得区分的内部和外部状态与组装大多数细胞内信号系统的相对较小的组件池，需要相当大的网络可塑性，这是通过共享组件或在多个复合体中重用组件来实现的。我将通过模型来探索经验性研究所建议的这种架构是否足以使信号能够实际诱导和塑造最终处理它们的网络。我设想，这将通过竞争性招募共享信令组件的动态机制来实现，从而导致自动催化反馈，从而锁定一个“赢家网络”。这种情况与预先配置的网络随时准备处理它们所专门处理的信号的观点形成鲜明对比。我认为，在具有基因表达动力学的反馈环的背景下理解信号动力学是很重要的。信令网络控制着基因的表达，这些基因控制着这些信令网络中的蛋白质水平。这种总体反馈包括慢和快时间尺度(分别是基因表达和信号)。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。分子组成的广泛多效性传达了可塑性的优势，但也可能限制蛋白质相互识别的准确性。蛋白质识别特异性降低会导致网络错误，也就是网络结构本身的波动。我将对蛋白质-蛋白质识别中的错误如何影响细胞反应有所了解。我将通过对简单模型的数学和数值研究来实现拟议的目标，这些模型捕捉到了组件重复使用、复杂的形成和蛋白质-蛋白质识别。我的建议完全属于美国国立卫生研究院的任务，因为组织的正常生理及其许多系统病理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了解细胞如何对环境做出反应的基本情况。随着我们对细胞信号处理内部工作原理的详细了解的增加，这种理解将使我们能够开发出更有效的抗击和治疗疾病的战略，通过提供特定疾病中的问题以及人类干预如何克服和规避这些错误的清晰图景。

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.