Reliable Signal Transduction

可靠的信号传导

• 批准号: 8886713
• 负责人: Roy Wollman
• 金额: $ 39.09万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8886713
• 关键词: Address; Adverse effects; Affect; Automation; Back; Biochemical Reaction; Biological; Biological Models; Biosensor; Cell model; Cells; Computer Analysis; Computer Simulation; Data; Decision Making; Diamond; Dose; EGF gene; Environment; Exocytosis; F-Actin; Feedback; Goals; Health; Human; Information Theory; Investigation; Lead; Maps; Measurement; Measures; Membrane; Methods; Microscopy; Modeling; Nature; Noise; Output; Paper; Pathway interactions; Population; Reaction; Research; Sample Size; Secretory Vesicles; Signal Transduction; Source; Statistical Methods; Structure; Testing; Theoretical model; Therapeutic; Time; Treatment Efficacy; Uncertainty; Work; base; cofilin; insight; lens; mast cell; new technology; prevent; public health relevance; receptor; response; signal processing; tool; transmission process

 DESCRIPTION (provided by applicant): The core function of signal transduction networks, the reliable transmission of information from cellular receptors to downstream effectors, can be adversely affected by biological noise. We propose addressing reliable signal transduction through the decomposition of noise into sources that are either intrinsic (reaction noise) or extrinsic (cell to cell variability) noise to the time scale of the signaling process. By dissectin noise mitigation mechanisms in signaling networks through a lens of `types of noise', we anticipate to gain deeper understanding into how mammalian signaling networks function under a regime of substantial noise. The central hypothesis guiding this research is that certain cellula mechanisms are more suitable in mitigating intrinsic noise while others serve to overcome extrinsic noise. This hypothesis will be tested through a systematic investigation of three noise mitigation mechanisms: network motifs, dynamic signals, and collective responses to determine their specific suitability to mitigate intrinsic and extrinsic noise sources. We propose the following aims: 1) To identify network-level feedbacks that prevent signal degradation due to intrinsic noise in a Mast cell model. We recently discovered a new pathway downstream of the FceRI receptor. Our preliminary data indicates that three previously unstudied network motifs that are important to the transmission of an oscillatory signal through the pathway. Using the oscillatory nature of this pathway, we will determine the ability of the three network motifs to specifically mitigate intrinsic noise. 2) We have developed a new statistical method to analyze the information transmission capacity of dynamic signals. Using this method we showed that the ability of the Erk signaling network to transmit dynamic signals substantially increases its information transmission capacity. We propose to determine the cause for increased information transmission capacity through dynamic signaling networks by analyzing the effect of different noise sources have on information transmission capacity. 3) To demonstrate the effect of extrinsic and intrinsic noise on the dose response curve of a noisy population. Due to nonlinearities in signaling networks the average response of a population of noisy cells could differ from the idealized noiseless single cell response. We will combine computational modeling, single cell dynamic measurement of Ca2+ and Erk response to ATP and EGF, respectively, to determine the effect noise has on the population level dose response curve. The proposed research will deliver key insights into the effects of intrinsic and extrinsic noise sources on signal transduction and how cells minimize the adverse effects of noise. Understanding how cells can function in regime with high noise will have important biomedical implications. Pharmacological manipulation of signaling networks is a common therapeutic strategy. Single cells studies show that biological noise causes high variability in cellular response that can be detrimental to the efficacy of the treatment. Insights into noise mitigation mechanisms will likely lead to new strategies that can increase the efficacy of many existing therapies that suffer from cellular response variability.

 描述（由应用程序提供）：信号传输网络的核心功能，从蜂窝接收器到下游效果的可靠传输可能会受到生物噪声的不利影响。我们建议通过噪声分解为固有的（反应噪声）或外部（细胞变异性）噪声到信号传导过程的时间尺度的源来解决可靠的信号转移。通过通过“噪声类型”的镜头中的剖面素噪声缓解机制，我们预计将对哺乳动物信号网络在实质性噪声状态下的功能有了更深入的了解。指导这项研究的中心假设是，某些细胞机制更适合缓解固有噪声，而另一些则用于克服外部噪声。该假设将通过对三种噪声机制的系统研究进行检验：网络图案，动态信号和集体响应，以确定它们的特定适用性，以减轻固有和外在的噪声源。我们提出以下目的：1）确定网络级反馈，以防止肥大细胞模型中固有噪声引起的信号降解。我们最近发现了FCERI受体下游的新途径。我们的初步数据表明，三个先前未研究的网络基序对于通过途径传输振荡信号很重要。使用该途径的振荡性质，我们将确定三个网络基序专门减轻固有噪声的能力。 2）我们开发了一种新的统计方法来分析动态信号的信息传输能力。使用此方法，我们表明，ERK信号网络传输动态信号的能力大大提高了其信息传输能力。我们建议通过分析不同噪声源对信息传输能力的影响来确定通过动态信号网络增加信息传输能力的原因。 3）证明外在噪声和内在噪声对噪声群体剂量响应曲线的影响。由于信号网络中的非线性，噪声细胞群的平均响应可能与理想化的无噪声单细胞反应不同。我们将分别结合计算建模，CA2+和ERK对ATP和EGF的响应的单细胞动态测量，以确定噪声对种群水平剂量响应曲线的影响。拟议的研究将对固有和外在噪声源对信号传递的影响以及细胞如何最大程度地减少噪声的不利影响的影响。了解细胞如何在高噪声方面在制度中起作用将具有重要的生物医学意义。信号网络的药理操作是一种常见的治疗策略。单细胞研究表明，生物噪声会导致细胞反应的高度差异，这可能会损害治疗的有效性。对降解机制的洞察力可能会导致新的策略，从而提高许多患有细胞反应变异性的现有疗法的效率。