Spatiotemporal modeling of signal transduction in yeast

描述(申请人提供)：所有生物体都有能力检测并对外部刺激做出反应，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，持续的刺激通常会产生瞬时反应。这些反应包括基因转录图谱中特定的和可重复的变化。细胞反应的重复性是显著的，因为信号通路在嘈杂的环境中运行，这会导致通路活动的巨大波动。这一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，为研究随机(随机)过程和动态系统而开发的数学技术可以解释活细胞中信令网络的复杂瞬时行为。为了验证这一假设，我们将应用数学模型来描述酵母中观察到的信息素反应途径的行为。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性，相对简单，并且可以很容易地从遗传和药物上进行操作。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图谱中的瞬时变化。酵母途径模型的成功最终将带来改进的模型和对更复杂生物体中激素和神经递质信号行为的更深层次的理解。这个项目有三个目标：目标1：我们将测量能够跟踪转录瞬时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，使我们能够更准确地推断途径激活的变化，并应用这些改进的方法来测量AIMS 2和AIMS 3的时空行为。目标2：我们将使用数学模型来理解典型的信息素反应启动子调控转录的机制。我们将首先建立一个基于微分方程的确定性模型来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，该模型被重塑为随机模型，以了解特定路径噪声的来源和后果。最后，一种新设计的微流控装置将被用来确定在信息素梯度的存在下，通路激活和失活过程中的噪音是否会减少。目的3：我们将建立一个数学模型来描述激活Ste12p的信号通路组件的时空行为；这些组件将包括MAP激酶FusSp和激酶支架蛋白SteSp。这个模型将被用来研究途径调控的空间方面，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，时空模型与目标2中开发的信息素调节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.