Spatiotemporal modeling of signal transduction in yeast

描述（由申请人提供）：所有生物都有能力检测和响应外部刺激，如气味、信息素、激素和神经递质。这些信号系统的一个共同特征是，持续的刺激往往会产生短暂的反应。这些反应包括基因转录谱中特定的和可重复的变化。考虑到信号通路在嘈杂的环境中运行，从而产生通路活动的大波动，细胞反应的可重复性是显著的。本建议的目的是了解通路波动的起源和后果，特别是空间线索在通路激活和细胞内信号传播中的作用。我们的假设是，用于研究随机过程和动力系统的数学技术可以解释活细胞中信号网络的复杂瞬态行为。为了验证这一假设，我们将应用数学模型来描述在酵母中观察到的信息素反应途径的行为。这种酵母信息素途径非常适合于这一目的，因为它具有非常好的特征，相对简单，并且可以很容易地在遗传和药理学上进行操作。此外，我们最近开发了新的实验方法，使我们能够准确地测量基因转录谱的瞬时变化。酵母途径建模的成功将最终导致模型的改进和对更复杂生物体中激素和神经递质信号行为的更深入的理解。该项目有三个目标：目标1：我们将测量能够跟踪转录瞬态变化的短寿命荧光蛋白的功能参数。基于这些参数，我们将开发报告蛋白的数学描述，这将使我们能够更准确地推断通路激活的变化，并应用这些改进的方法来测量目标2和目标3中的时空行为。目的2：我们将使用数学模型来理解从一个典型的信息素响应启动子调节转录的机制。我们将首先建立一个基于微分方程的确定性模型，以研究信号通路如何调节信息素诱导的转录因子（Ste12p）的活性。然后将该模型重铸为随机模型，以了解路径特定噪声的起源和后果。最后，将使用新设计的微流体装置来确定信息素梯度存在时信号通路激活和失活中的噪声是否会降低。目标3：我们将开发一个数学模型来描述激活Ste12p的信号通路组分的时空行为；其中包括MAP激酶FusSp和激酶支架蛋白SteSp。该模型将用于研究通路调控的空间方面，并确定信息素梯度如何影响这两个关键信号蛋白的空间定位。最后，时空模型与Aim 2中开发的信息素调控启动子的时间模型相关联，以准确预测蛋白质定位如何随时间影响转录诱导。

DESCRIPTION (provided by applicant): All organisms have the capacity to detect and respond to external stimuli such as odors, pheromones, hormones, and neurotransmitters. A common feature of these signaling systems is that a sustained stimulus will often produce a transient response. These responses include specific and reproducible changes in the gene transcription profile. The reproducibility of cellular responses is remarkable given that signaling pathways operate in noisy environments, which generate large fluctuations in pathway activity. The objectives of this proposal are to understand the origins and consequences of pathway fluctuations, and in particular the role of spatial cues in pathway activation and signal propagation within the cell. Our hypothesis is that mathematical techniques developed for studying stochastic (random) processes and dynamical systems can explain the complex transient behaviors of signaling networks in a living cell. To test the hypothesis we will apply mathematical models that describe observed behaviors of the pheromone response pathway in yeast. This yeast pheromone pathway is well suited for this purpose because it is extremely well characterized, relatively simple, and can be easily manipulated genetically and pharmacologically. Additionally, we have recently developed novel experimental approaches that will allow us to accurately measure transient changes in the gene transcription profile. Success in modeling the yeast pathway will eventually lead to improved models and a deeper understanding of hormone and neurotransmitter signaling behavior in more complex organisms. This project as three aims: Aim 1: We will measure functional parameters of short-lived fluorescent proteins capable of tracking transient changes in transcription. Based on these parameters we will develop mathematical descriptions of the reporter proteins that will enable us to more accurately infer changes in pathway activation, and apply these improved methods to measure spatiotemporal behavior in Aims 2 and 3. Aim 2: We will use mathematical models to understand mechanisms that regulate transcription from a prototypical pherpmone-responsive promoter. We will first develop a deterministic model based on differential equations to investigate how the signal pathway regulates activity of the pheromone-induced transcription factor (Ste12p). The model is then recast as a stochastic model to understand the origins and consequences of pathway-specific noise. Finally, a newly designed micro fluidics device will be used to determine if the noise in pathway activation and deactivation decreases in the presence of a pheromone gradient. Aim 3: We will develop a mathematical model to describe the spatiotemporal behavior of signaling pathway components that activate Ste12p; these will include the MAP kinase FusSp and the kinase scaffold protein SteSp. This model will be used to investigate spatial aspects of pathway regulation and determine how pheromone gradients affect the spatial localization of these two key signaling proteins. Finally, the spatiotemporal model is linked to a temporal model of the pheromone-regulated promoter developed in Aim 2 to accurately predict how protein localization affects transcriptional induction over time.