Collaborative Research: Semiparametric ODE Models for Complex Gene Regulatory Networks

基因调控在细胞的生长、分裂和对环境刺激的反应等活动和功能中起着基础性的作用。在细胞中，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复杂而动态的基因调控网络(GRN)。最近的技术突破使大规模的GRN实验研究成为可能。GRN分析中的一个中心问题是阐明在研究中引起生物学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给从内部提取有用信息带来了严重的挑战。该项目旨在开发统计和计算工具，以揭示全球资源分配网络的基本结构、动态和功能。将开发新的统计理论和推理方法，以解决大规模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。传统的GRN统计分析通常假设网络节点之间的相互作用可以用线性函数或低次多项式来描述。然而，生物过程通常是复杂的，网络节点之间的分子相互作用可能不能用简单的函数来准确描述。这个项目的主要目标是开发新的和灵活的统计方法，通过从具有连续或离散值基因表达的时间过程实验数据中学习非线性相互作用来解剖和重建GRN。具体地说，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组(ODE)来研究GRN，并将开发最先进的计算工具来表征GRN的结构和动力学，以帮助科学家解决受GRN调控的关键细胞系统。该项目包括两个部分。第一部分集中于方法和理论，包括三个目标：(1)开发新的自动化统计程序来研究大型和复杂的GRN的局部模式和动态结构；(2)建立关于GRN的拓扑特征和调控相互作用的有效统计推断；(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常微分方程组对GRN的拓扑和动力学建模提供有价值的工具。在第二部分中，我们将重点介绍实际数据应用。具体地说，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和内部的基因调控方面发挥着关键作用。

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.