Reliable Signal Transduction

 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可靠信息传输，可能受到生物噪声的不利影响。我们建议通过将噪声分解为信号传导过程时间尺度上的内在（反应噪声）或外在（细胞间变异性）噪声源来解决可靠的信号转导。通过对“噪声类型”的透镜来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哺乳动物信号网络在大量噪声环境下的功能。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，而其他细胞机制则用于克服外在噪声。这一假设将通过三个噪声缓解机制：网络图案，动态信号和集体反应，以确定其特定的适用性，以减轻内在和外在的噪声源的系统调查进行测试。我们提出了以下目标：1）识别网络级反馈，防止信号退化，由于固有的噪声在肥大细胞模型。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，三个以前未研究的网络图案，是重要的振荡信号通过该途径的传输。利用这一途径的振荡性质，我们将确定这三个网络图案的能力，以具体减轻固有噪声。2)本文提出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，我们证明了Erk信令网络传输动态信号的能力大大增加了其信息传输容量。我们建议通过分析不同噪声源对信息传输容量的影响来确定通过动态信令网络增加信息传输容量的原因。3)证明外在和内在噪声对噪声群体剂量反应曲线的影响。由于信号网络中的非线性，噪声细胞群体的平均响应可能不同于理想化的无噪声单细胞响应。我们将结合联合收割机的计算建模，单细胞动态测量的Ca 2+和Erk的ATP和EGF的反应，分别，以确定影响噪声对人口水平的剂量反应曲线。拟议的研究将提供关键的见解，内在和外在噪声源对信号转导的影响，以及细胞如何最大限度地减少噪声的不利影响。了解细胞如何在高噪声状态下发挥作用将具有重要的生物医学意义。信号网络的药理学操作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可变性，这可能对治疗的功效有害。对噪音缓解机制的深入了解可能会导致新的策略，这些策略可以提高许多现有疗法的疗效，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.