Structural and Dynamical Specificity in Intracellular Signaling Networks

细胞内信号网络的结构和动态特异性

• 批准号: 7224411
• 负责人: Eric J Deeds
• 金额: $ 4.68万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7224411
• 关键词: Acetylation; Affect; Architecture; Back; Biological Assay; Blur; Cell Death; Cells; Complement; Complex; Disease; Environment; Exhibits; Feedback; Gene Expression; Goals; Growth Factor; Hormones; Intervention; Investigation; Knowledge; Learning; Malignant Neoplasms; Memory; Mission; Modeling; Molecular; Molecular Models; Nature; Numbers; Pathology; Pathway interactions; Peptides; Phosphorylation; Phosphotransferases; Physics; Physiology; Portraits; Post-Translational Protein Processing; Process; Proteins; Recording of previous events; Recruitment Activity; Resources; Role; Shapes; Signal Pathway; Signal Transduction; Signaling Protein; Specificity; Structure; System; Time; Tissues; Ubiquitination; United States National Institutes of Health; Work; base; cell transformation; computer framework; computerized data processing; cytokine; falls; feeding; human disease; molecular recognition; pleiotropism; repaired; research study; response

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.

描述（由申请人提供）：细胞区分他们以背景依赖和历史偏见的方式响应的大量内部和外部状态，影响了基本过程，例如分裂，修复和细胞死亡。为了调和可能与大多数细胞内信号系统组装的组件相对小的组件池的潜在内部和外部状态，需要相当大的网络可塑性，这是通过在多个复合物中共享组件或它们的再利用而实现的。我将通过模型探索这种体系结构是否足以使信号能够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信号组件的竞争招募动态来实现的，从而导致锁定在“赢家网络”中的自催化反馈。这种情况与预先配置的网络准备好处理其专用的信号的观点形成了鲜明的对比。我认为，在具有基因表达动力学的反馈循环的背景下，了解信号动力学很重要。信号网络控制基因的表达，该基因控制这些非常信号网络中的蛋白质水平。该总体反馈包括缓慢和快速的时间尺度（分别为基因表达和信号传导）。时间尺度的这种分离可能是细胞内学习和记忆的简单形式的基础。分子成分的广泛的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。降低的蛋白质识别特异性会导致网络误差，即网络结构本身的波动。我将了解蛋白质蛋白识别中的错误如何影响细胞反应。我将通过对捕获组件再利用，复杂形成和蛋白质 - 蛋白质识别的简单模型的数学和数值研究来实现所提出的目标。我的提议强烈属于NIH的任务，因为组织的正常生理及其许多全身性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获得细胞对环境的反应的基本情况。随着我们对细胞信号处理内部工作的详细了解增加了，这种理解将使我们能够通过提供明确的肖像来制定越来越有效的策略来打击和治愈疾病，以了解特定疾病中出了什么问题以及人类干预如何克服和解决此类错误。