Multiscale Modeling of Subcellular Structure and its Effects on Gene Expression and Regulation

亚细胞结构的多尺度建模及其对基因表达和调控的影响

• 批准号: 0920886
• 负责人: Samuel Isaacson
• 金额: $ 27.25万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0920886&HistoricalAwards=false
• 关键词: Multiscale; Modeling; Subcellular; Structure; its

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This project will develop mathematical models to study the influence of subcellular architecture on the dynamics of gene expression and regulation in eukaryotes. Detailed three-dimensional stochastic reaction-diffusion models that incorporate the exclusion of nuclear volume by chromatin and the sliding of transcription factors along DNA will be constructed. Systems of spatially-continuous Smoluchowski diffusion-limited reaction equations (SDLR) and lattice-based reaction-diffusion master equations (RDME) are the two primary stochastic reaction-diffusion models that have been used to study systems in which both the stochasticity in the chemical reaction process and the spatial diffusion of molecules is important. We will develop extensions to the RDME and SDLR systems of equations to account for the influence of chromatin on the motion of proteins and messenger ribonucleoproteins (mRNPs) within the nucleus of cells. A multiscale approach will be developed in which an initial model that explicitly represents chromatin fibers over length scales significantly smaller than the size of the nucleus will be constructed. This model will then be coarse grained, and combined with existing experimental imaging data to estimate parameters in effective whole-nucleus scale RDME and SDLR models. These new coarse grained models will be used to study the influence of global nuclear substructure on the time needed for gene regulatory proteins, upon entering the nucleus, to find specific DNA binding sites. Methods will also be developed for improving the accuracy of the RDME in approximating the SDLR model, and for exactly simulating the stochastic processes described by the SDLR model in the complex geometries that are present in cells. A rigorous analytical and numerical study of the predictions that the two models make for general multi-particle systems and for specific biological systems with realistic geometries will be undertaken.To better comprehend how organisms function, respond to environmental stimuli, and to aid in treating disease, it is necessary to understand, predict, and control the behavior of individual cells. Each cell contains numerous complex dynamical processes involving proteins undergoing biochemical reactions that play a major role in cell to cell communication, cell growth and division, heart and nervous system development and function, and the development and progression of cancer. Understanding the ways that these processes activate and inactivate genes inside individual cells is fundamental to determining how these processes influence cell function. In this work we will develop explicit mathematical models of how proteins within a cell find the specific genes that they activate or inactivate. These models will allow the quantitative study of how the interior physical structure of cells influences this process. New mathematical equations will be developed to model the movement of proteins within cells containing realistic cellular substructures. New computational methods will be developed to efficiently solve these mathematical equations in order to better predict and control the dynamical processes that influence cell behavior.

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资金。该项目将开发数学模型来研究亚细胞结构对真核生物基因表达和调控动态的影响。 将构建详细的三维随机反应扩散模型，其中包含染色质对核体积的排除以及转录因子沿 DNA 的滑动。空间连续 Smoluchowski 扩散限制反应方程 (SDLR) 和基于晶格的反应扩散主方程 (RDME) 是两种主要的随机反应扩散模型，用于研究化学反应过程的随机性和分子空间扩散都很重要的系统。我们将开发 RDME 和 SDLR 方程组的扩展，以解释染色质对细胞核内蛋白质和信使核糖核蛋白 (mRNP) 运动的影响。将开发一种多尺度方法，其中将构建一个初始模型，该模型在显着小于细胞核尺寸的长度尺度上明确表示染色质纤维。然后，该模型将被粗粒度化，并与现有的实验成像数据相结合，以估计有效的全核尺度 RDME 和 SDLR 模型中的参数。这些新的粗粒度模型将用于研究整体核亚结构对基因调控蛋白进入细胞核后找到特定 DNA 结合位点所需时间的影响。还将开发方法来提高 RDME 在逼近 SDLR 模型时的准确性，并精确模拟细胞中存在的复杂几何形状中 SDLR 模型描述的随机过程。将对这两个模型对一般多粒子系统和具有现实几何形状的特定生物系统所做的预测进行严格的分析和数值研究。为了更好地理解生物体如何发挥作用、对环境刺激做出反应并帮助治疗疾病，有必要了解、预测和控制单个细胞的行为。每个细胞都包含许多复杂的动态过程，涉及蛋白质进行生化反应，这些反应在细胞间通讯、细胞生长和分裂、心脏和神经系统发育和功能以及癌症的发生和进展中发挥着重要作用。了解这些过程激活和失活单个细胞内基因的方式对于确定这些过程如何影响细胞功能至关重要。在这项工作中，我们将开发明确的数学模型来解释细胞内的蛋白质如何找到它们激活或失活的特定基因。这些模型将允许定量研究细胞的内部物理结构如何影响这一过程。 将开发新的数学方程来模拟包含真实细胞亚结构的细胞内蛋白质的运动。将开发新的计算方法来有效地求解这些数学方程，以便更好地预测和控制影响细胞行为的动态过程。