Collaborative Research : Stochastic Modeling and Estimation of Gene Transcription

描述（由申请人提供）：单个细胞对激活刺激的反应表现出随机变异性。在参与先天免疫反应的细胞中，这种可变性似乎非常重要。细胞对同样的刺激有不同的反应，例如，有些细胞增殖，有些细胞凋亡。我们的目标是开发工具来理解和准确建模真核细胞中基因转录和信号转导的随机现象。该提案的一个组成部分是在本科、研究生和研究生阶段进行跨学科培训，我们在这方面有经验，涉及NSF IGERT资助、凯克计算生物学中心以及与德克萨斯医学中心的联系。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。（二）对于低水平信号，波动的细胞膜受体结合激活分子的数量。我们计划：

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.