Analytical representation of protein distributions in stochastic models of gene expression

基因表达随机模型中蛋白质分布的分析表示

• 批准号: 1413111
• 负责人: Rahul Kulkarni
• 金额: $ 17万
• 依托单位: University of Massachusetts Boston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1413111&HistoricalAwards=false
• 关键词: Analytical; representation; protein; distributions; stochastic

One of the fundamental problems in biology is elucidating the molecular mechanisms that give rise to phenotypic variations among individuals in a population. It has been shown that phenotypic variations can arise without any underlying differences in the genotype or environmental factors. Such "nongenetic individuality" is observed in diverse cellular processes, ranging from bacterial persistence to HIV-1 viral infections, and is driven by randomness (noise) in the cellular levels of gene expression products such as mRNAs and proteins. To quantify the effects of noise in gene expression, recent single-cell experiments have obtained probability distributions characterizing protein levels across a population of cells. Correspondingly, there is a need to develop a general analytical framework for modeling and interpretation of the distributions obtained from such single-cell experiments that will lead to quantitative insights into how noise in gene expression is regulated. The goal of this project is to develop new approaches for obtaining analytical results for protein distributions in models of gene expression and its regulation. The project will contribute to a fundamental understanding of the role of noise in gene expression and its regulation in diverse cellular processes. The analysis requires tools and approaches from physics and applied mathematics and will be integrated with teaching efforts to effectively train students and future scientists in this fast-developing field of interdisciplinary research.Noise in gene expression is generally analyzed using coarse-grained stochastic models. However, obtaining exact analytical expressions for the corresponding protein distributions has been a challenging for all but the simplest models. The project research will involve integration of tools from queueing theory with approaches based on partitioning of Poisson processes to address such challenges. The approaches developed will be used to obtain both exact and approximate analytical results for models of gene expression and its regulation. The resulting analysis of models with regulatory mechanisms such as bursting, feedback and promoter-based regulation will lead to quantitative insights into noise characteristics of the basic building blocks of genetic circuits. The project research will also lead to analytical results that characterize the role of noisy inputs in regulating simple biochemical switches and suggest new approaches for estimating model parameters based on observations of noise. The analytical results derived in the project will have multiple applications ranging from synthetic biology to understanding phenotypic variation in clonal populations.

生物学的基本问题之一是阐明引起群体中个体表型变异的分子机制。已经表明，表型变异可以在基因型或环境因素没有任何潜在差异的情况下出现。这种“非遗传个体”在不同的细胞过程中观察到，从细菌持久性到HIV-1病毒感染，并且是由mRNA和蛋白质等基因表达产物的细胞水平的随机性（噪声）驱动的。为了量化基因表达中噪声的影响，最近的单细胞实验获得了表征细胞群中蛋白质水平的概率分布。相应地，需要开发一个通用的分析框架，用于对从此类单细胞实验中获得的分布进行建模和解释，这将有助于定量了解基因表达中的噪声是如何调节的。该项目的目标是开发新方法来获得基因表达及其调控模型中蛋白质分布的分析结果。该项目将有助于从根本上理解噪声在基因表达中的作用及其在不同细胞过程中的调节。该分析需要物理和应用数学的工具和方法，并将与教学工作相结合，以便在这个快速发展的跨学科研究领域有效地培训学生和未来的科学家。基因表达中的噪声通常使用粗粒度随机模型进行分析。然而，除了最简单的模型之外，获得相应蛋白质分布的精确分析表达式对于所有模型来说都是一个挑战。该项目研究将涉及将排队论工具与基于泊松过程划分的方法相集成，以应对此类挑战。开发的方法将用于获得基因表达及其调控模型的精确和近似分析结果。对具有突发、反馈和基于启动子的调节等调节机制的模型的分析将导致对遗传电路基本构建模块的噪声特征的定量见解。该项目研究还将得出分析结果，描述噪声输入在调节简单生化开关中的作用，并提出基于噪声观察估计模型参数的新方法。 该项目得出的分析结果将具有多种应用，从合成生物学到了解克隆群体的表型变异。