Reliable Signal Transduction

 描述(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可靠信息传输，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成信号过程的时间尺度内的内部(反应噪声)或外部(细胞间变异性)噪声来解决可靠的信号转导问题。通过从“噪声类型”的角度剖析信号网络中的噪声抑制机制，我们期望对哺乳动物信号网络在大量噪声条件下如何工作有更深入的了解。指导这项研究的中心假设是，某些细胞机制更适合于减轻内在噪声，而其他机制则用于克服外部噪声。这一假设将通过对三种噪声缓解机制的系统研究来检验：网络主题、动态信号和集体反应，以确定它们对缓解内在和外部噪声源的具体适用性。我们提出了以下目标：1)识别网络级反馈，以防止由于肥大小区模型中的固有噪声而导致的信号劣化。我们最近发现了FceRI受体下游的一条新途径。我们的初步数据表明，三个以前没有研究过的网络基序对通过该途径传递振荡信号是重要的。利用这一途径的振荡性质，我们将确定三个网络基序特定减轻内在噪声的能力。2)发展了一种新的统计方法来分析动态信号的信息传输能力。利用这种方法，我们证明了ERK信令网络传输动态信号的能力大大增加了它的信息传输能力。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令网络提高信息传输能力的原因。3)研究外在噪声和本征噪声对噪声人群剂量效应曲线的影响。由于信令网络中的非线性，有噪声小区群的平均响应可能不同于理想化的无噪声单个小区响应。我们将结合计算模型、单细胞动态测量Ca~(2+)和Erk对ATP和EGF的响应，来确定噪声对群体水平剂量响应曲线的影响。这项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以及细胞如何将噪音的不利影响降至最低的关键见解。了解细胞如何在高噪音环境中发挥作用，将具有重要的生物医学意义。药物操纵信号网络是一种常见的治疗策略。单细胞研究表明，生物噪声导致细胞反应的高度变异性，这可能对治疗效果不利。对噪音缓解机制的洞察可能会导致新的策略，这些策略可以提高许多现有治疗方法的有效性，这些疗法受到细胞反应变异性的影响。

 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.