Collaborative Research: Semiparametric ODE Models for Complex Gene Regulatory Networks

合作研究：复杂基因调控网络的半参数 ODE 模型

• 批准号: 1418202
• 负责人: Guang Cheng
• 金额: $ 4.6万
• 依托单位: Purdue University
• 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: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418202&HistoricalAwards=false
• 关键词: Collaborative; Research; Semiparametric; ODE; Models

Gene regulation plays a fundamental role in cellular activities and functions, such as growth, division, and responses to environmental stimuli. The regulatory interactions among genes and their expression products (RNAs and proteins) intertwine into complex and dynamic gene regulatory networks (GRNs) in cells. Recent technical breakthrough has enabled large-scale experimental studies of GRNs. A central question in GRN analysis is to elucidate network topologies and dynamics that give rise to biological properties at study. However, the magnitude and complexity of these network data pose serious challenges in extracting useful information from within. This project aims to develop statistical and computational tools to reveal underlying structure, dynamics, and functionality of GRNs. New statistical theory and inference methods will be developed to tackle theoretical and computational challenges in modeling and analyzing large-scale GRNs. Results from this research will establish a novel framework to dissect dynamical and complex biological networks, and particularly a GRN that regulates cell proliferation in our case study.Traditional statistical analysis of GRNs typically assumes that interactions between network nodes can be described by linear functions or low-order polynomials. However, biological processes are usually complex and molecular interactions between network nodes may not be accurately described by simple functions. The main goal of this project is to develop novel and flexible statistical approaches to dissect and reconstruct GRNs by learning nonlinear interactions from time-course experimental data, with either continuous- or discrete-valued gene expression. Specifically, we will develop new modeling and analysis approaches to study GRNs using semiparametric ordinary differential equations (ODEs), and will develop state of art computational tools to characterize the structures and dynamics of GRNs, to help scientists address crucial cellular systems regulated by GRNs. The project has two parts. The first part focuses on Methods and Theory, consisting of three aims: (1) to develop new and automated statistical procedures for studying local patterns and dynamic structures in large and complex GRNs; (2) to establish valid statistical inferences on topological features and regulatory interactions of GRNs; and (3) to develop efficient computational algorithms and software for analyzing large-scale GRNs. Developed methods from this research will provide valuable tools for modeling the topologies and dynamics of GRNs using ODEs. In the second part, we will focus on real data applications. Specifically, we will apply newly developed tools in the first part to analyze a retinoblastoma (Rb)-E2F gene network, which plays a key role in controlling cell proliferation and the gene regulation within.

基因调控在细胞活动和功能中发挥着重要作用，例如生长、分裂和对环境刺激的反应。基因及其表达产物（RNA 和蛋白质）之间的调控相互作用交织成细胞中复杂且动态的基因调控网络 (GRN)。最近的技术突破使得 GRN 的大规模实验研究成为可能。 GRN 分析的一个核心问题是阐明在研究中产生生物特性的网络拓扑和动力学。然而，这些网络数据的规模和复杂性对从内部提取有用信息提出了严峻的挑战。该项目旨在开发统计和计算工具来揭示 GRN 的底层结构、动力学和功能。将开发新的统计理论和推理方法来解决大规模 GRN 建模和分析中的理论和计算挑战。这项研究的结果将建立一个新的框架来剖析动态和复杂的生物网络，特别是在我们的案例研究中调节细胞增殖的 GRN。GRN 的传统统计分析通常假设网络节点之间的相互作用可以通过线性函数或低函数来描述-阶多项式。然而，生物过程通常很复杂，网络节点之间的分子相互作用可能无法通过简单的函数准确描述。该项目的主要目标是开发新颖且灵活的统计方法，通过从时间过程实验数据中学习非线性相互作用（连续值或离散值基因表达）来剖析和重建 GRN。具体来说，我们将开发新的建模和分析方法来使用半参数常微分方程（ODE）研究GRN，并将开发最先进的计算工具来表征GRN的结构和动力学，以帮助科学家解决由GRN调节的关键细胞系统。该项目有两个部分。第一部分侧重于方法和理论，包括三个目标：（1）开发新的自动化统计程序，用于研究大型复杂 GRN 中的局部模式和动态结构； (2) 对GRN的拓扑特征和调控相互作用建立有效的统计推论； (3) 开发用于分析大规模 GRN 的高效计算算法和软件。这项研究开发的方法将为使用 ODE 对 GRN 的拓扑和动力学建模提供有价值的工具。在第二部分中，我们将重点关注真实的数据应用。具体来说，我们将在第一部分中应用新开发的工具来分析视网膜母细胞瘤（Rb）-E2F基因网络，该网络在控制细胞增殖及其内部基因调控方面发挥着关键作用。