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（由申请人提供）：信号转导网络的核心功能，即从细胞受体到下游效应器的信息的可靠传输，可能会受到生物噪声的不利影响。我们建议通过将噪声分解为源来解决可靠的信号转导问题。通过“噪声类型”的视角剖析信号网络中的噪声缓解机制，我们期望能够更深入地了解信号传导过程时间尺度的内在（反应噪声）或外在（细胞间变异）噪声。了解哺乳动物信号网络如何在大量噪音下发挥作用，指导这项研究的核心假设是某些细胞机制更适合减轻内在噪音，而其他机制则有助于克服外在噪音。我们提出了三种噪声缓解机制：网络基序、动态信号和集体响应，以确定它们减轻内在和外在噪声源的具体适用性：1）识别网络级反馈，以防止由于内在噪声而导致的信号退化。肥大细胞我们最近发现了 FceRI 受体下游的一个新通路。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该通路传递振荡信号非常重要，我们将确定该通路的振荡特性。 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，使用这种方法我们发现 Erk 信号网络传输动态信号的能力显着增加。它是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提高信息传输能力的原因3）论证外在和内在噪声对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响应不同，我们将分别结合计算模型、Ca2+ 的单细胞动态测量和 Erk 对 ATP 和 EGF 的响应。 ,确定噪声对群体水平剂量反应曲线的影响。拟议的研究将提供关于内在和外在噪声源对信号转导的影响的重要见解，以及细胞如何最大限度地减少噪声的不利影响。信号网络的药理学操作是一种常见的治疗策略，表明生物噪声会导致细胞反应的高度可变性，这可能不利于治疗效果。可能会导致可以提高功效的新策略许多现有疗法都存在细胞反应变异性。