Collaborative Research : Stochastic Modeling and Estimation of Gene Transcription

描述（由申请人提供）：单个细胞在其对激活刺激的反应中显示随机变异性。在参与先天免疫反应的细胞中，这种变异性似乎非常重要。细胞对相同的刺激有不同的反应，例如，一些细胞增殖，一些细胞凋亡。我们的目标是开发工具，用于理解和精确建模的随机现象，在真核细胞中的基因转录和信号转导。该提案的一个组成部分是本科生，研究生和研究生水平的跨学科培训，我们在这方面有经验，涉及NSF IGERT赠款，凯克计算生物学中心和德克萨斯州医学中心的推广。真核细胞中随机性的主要来源是：（i）吸引RNA聚合酶II的转录复合物的组装。(ii)对于低水平的信号，细胞膜受体结合活化分子的数量波动。我们计划： 1.在单细胞、核和分子水平上识别基因转录和调控中随机效应的来源，并建立这些效应的数学模型。 2.研究这些模型的数学性质：（a）寻找随机解，（B）发展极限理论，（c）研究模型的定性性质。 3.开发模型预测的计算算法。实现这些算法的计算机程序。 4.应用贝叶斯和非贝叶斯统计方法来估计参数并对这些参数进行推断，并评估模型的拟合优度，以进行复杂计算机模型的推断。 我们选择的生物系统是由3个途径组成的，涉及NF κ B家族的转录因子在哺乳动物的先天免疫中起决定性作用。这三个方面是：（i）典型的，（ii）的RIG-IMAVS-，和（iii）非典型的途径，激活不同的刺激，并作为信息模型的随机性的计算分析。我们将通过使用荧光融合蛋白，在一个单一的mRNA分子的分辨率，染色质交换使用光漂白和荧光寿命测量的转录分析，扩展这些途径的理解。在我们的方法中，生物实验的动机是建模和估计所需的数据，数学方法是基于观察到的生物模型行为。

DESCRIPTION (provided by applicant): Individual cells display stochastic variability in their responses to activating stimuli. In cells taking part in the innate immune response this variability seems very important. Cells react differently to the same stimulus, e.g. some proliferate some move to apoptosis. Our aim is to develop tools for understanding and accurate modeling of stochastic phenomena in gene transcription and signal transduction in eukaryotic cells. An integral part of the proposal is interdisciplinary training at the undergraduate, graduate and postgraduate level, in which we have experience, involving NSF IGERT grants, Keck Center for Computational Biology and outreach to Texas Medical Center. The primary sources of stochasticity in eukaryotic cells are: (i) Assembly of the transcription complexes attracting RNA Polymerase II. (ii) For low levels of signal, fluctuations in the number of cell membrane receptors binding activating molecule. We are planning to: 1. Identify sources of stochastic effects in gene transcription and regulation on single-cell, nuclear and molecular level and develop mathematical models of these effects. 2. Investigate the mathematical properties of these models by: (a) Finding stochastic solutions, (b) Developing limit theory, (c) Investigating qualitative properties of the models. 3. Develop computational algorithms for model predictions. Implement computer programs for these algorithms. 4. Apply Bayesian and non-Bayesian statistical methodologies for estimating parameters and making inferences about these parameters, and assess the goodness of fit of the models, for inference with complex computer models. The biological system we chose is constituted by 3 pathways involving NFKB family of transcription factors playing a decisive role in innate immunity in mammals. These three are: (i) the canonical, (ii) the RIG-IMAVS-, and (iii) the non-canonical pathways, activated by distinct stimuli, and serve as informative models for computational analysis of stochasticity. We will extend the understanding of these pathways by using fluorescent fusion proteins, analysis of transcription at a single mRNA molecule resolution, chromatin exchange using photobleaching and fluorescence lifetime measurements. In our approach, the biological experiments are motivated by data needed for modeling and estimation, and mathematical methods are based on the observed biological model behavior.