Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for Studyin

时空变化的分子相互作用网络：研究的新范式

• 批准号: 7865088
• 负责人: Eric P Xing
• 金额: $ 46.09万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7865088
• 关键词: 3-Dimensional; Accounting; Address; Adopted; Algorithms; Architecture; Automobile Driving; Back; Behavior; Belief; Binding; Biochemical; Biological; Biological Markers; Biological Process; Case Study; Cell Cycle; Cell Differentiation process; Cell Line; Cell physiology; Characteristics; Clinical; Collaborations; Collection; Complex; Computational algorithm; Computer software; Computing Methodologies; Data; Data Set; Databases; Dependency; Development; Developmental Process; Diagnosis; Diagnostic; Diagnostic Neoplasm Staging; Disease; Disease Progression; Documentation; Drosophila genus; Drug Delivery Systems; Embryo; Engineering; Event; Evolution; Exhibits; Foundations; Gene Expression; Gene Expression Regulation; Gene Proteins; Gene Targeting; Genes; Genetic; Genome; Graph; Hand; Heel; Human; Imagery; Immune response; Indium; Individual; Internet; Investigation; Knowledge; Laboratories; Lead; Learning; Length; Light; Literature; Location; Machine Learning; Measurement; Measures; Mediating; Medical; Methodology; Methods; Mining; Modality; Modeling; Molecular; Molecular Genetics; Molecular Profiling; Nature; Network-based; Ontology; Organism; Pathogenesis; Pathologic Processes; Pathway Analysis; Pathway interactions; Pattern; Performance; Pharmaceutical Preparations; Physiological Processes; Play; Problem Formulations; Process; Property; Proteins; Publications; Publishing; RNA Interference; Regulation; Regulator Genes; Regulatory Pathway; Reporting; Research; Role; Saccharomyces cerevisiae; Sampling; Scheme; Science; Seminal; Series; Signal Transduction; Signal Transduction Pathway; Simulate; Software Tools; Solutions; Source; Staging; Stimulus; Structure; System; Systems Biology; Techniques; Technology; Terminology; Testing; Time; Time Study; Tissues; Trees; Ursidae Family; Validation; Variant; Visual; Work; base; biological systems; cell behavior; combinatorial; computer based statistical methods; cost; design; driving force; environmental change; fitness; gene function; gene interaction; grasp; heuristics; improved; in vivo; innovation; insight; interest; knockout gene; malignant breast neoplasm; mathematical model; novel; peer; prevent; programs; promoter; protein protein interaction; public health relevance; research study; response; scale up; software systems; sound; success; tomography; tool; trait; trend; tumor progression; user friendly software; yeast two hybrid system

DESCRIPTION (provided by applicant): A major challenge in systems biology is to quantitatively understand and model the dynamic topological and functional properties of cellular networks, such as the spatial-temporally specific and context-dependent rewiring of transcriptional regulatory circuitry and signal transduction pathways that control cell behavior. Current efforts to study biological networks have primarily focused on creating a descriptive analysis of macroscopic properties. Such simple analyses offer limited insights into the remarkably complex functional and structural organization of a biological system, especially in a dynamic context. Furthermore, most existing techniques for reconstructing molecular networks based on high-throughput data ignore the dynamic aspect of the network topology and represent it as an invariant graph. To our knowledge the network itself is rarely considered as an object that is changing and evolving. In this proposal, we aim to develop principled machine learning algorithms that reverse engineer the temporally and spatially varying interactions between biological molecules from longitudinal or spatial experimental data. Our approaches will take into account biological prior information such as transcriptional factor binding targets, gene knockout experiments, gene ontology, and PPI. Contrary to traditional co-expression studies, our methods unfold the rewiring networks underlying the entire span of the biological process. This will make it possible to discover and trace transient molecular interactions, modules, and pathways during the progression of the process. We will also develop a Bayesian formalism to model and infer the "dynamic network tomography" - the meta-states that determine each molecule's function and relationship to other molecules, thereby driving the evolution of the network topology, possibly in response to internal perturbations or environmental changes. Using these new tools, we will carry out a case study on time series gene expression data from organotypic models of breast cancer progression/reversal to gain insight into the mechanisms that drive the temporal rewiring of gene networks during this process. Finally we will also deliver a software platform offering the tools developed in this project to the public. So far, there has not been work done to consider temporally and spatially varying biological interactions under a unified framework. Our proposed work represents an initial foray into this important problem. Our proposed work represents a significant step forward over the current methodology. We envisage a new paradigm that facilitates: 1) Statistical inference and learning of gene networks that are evolving over space and time, possibly in response to various stimuli and possibly mediating genome-environmental interactions. 2) Thorough exploration of the underlying functional underpinnings that drive the network rewiring, dynamic trajectory, and trend of functional evolution. 3) Uncovering transient events taking place in the dynamic systems, building predictive understanding of the mechanisms of gene regulation, network formation, and evolution. 4) Fast and accurate computational algorithms, with stronger statistical guarantee and greater scalability and robustness in large-scale dynamic network analysis. 5) A full spectrum of convenient software packages and user interfaces for dynamic network analysis, available to the public.

描述（由申请人提供）：系统生物学的一个主要挑战是定量理解和模拟细胞网络的动态拓扑和功能特性，例如时空特异性和上下文依赖性的转录调节电路和控制细胞行为的信号转导途径的重新布线。目前对生物网络的研究主要集中在对宏观特性进行描述性分析。这种简单的分析对生物系统中非常复杂的功能和结构组织提供了有限的见解，特别是在动态环境中。此外，大多数现有的基于高通量数据重建分子网络的技术忽略了网络拓扑的动态方面，并将其表示为不变图。据我们所知，网络本身很少被认为是一个不断变化和发展的对象。在本提案中，我们的目标是开发有原则的机器学习算法，从纵向或空间实验数据中对生物分子之间在时间和空间上变化的相互作用进行逆向工程。我们的方法将考虑到生物学的先验信息，如转录因子结合靶点、基因敲除实验、基因本体和PPI。与传统的共表达研究相反，我们的方法揭示了整个生物过程背后的重新布线网络。这将使发现和追踪瞬态分子相互作用、模块和过程进展中的途径成为可能。我们还将开发一个贝叶斯形式化模型来模拟和推断“动态网络断层扫描”——决定每个分子的功能和与其他分子的关系的元状态，从而推动网络拓扑结构的演变，可能是为了响应内部扰动或环境变化。利用这些新工具，我们将对来自乳腺癌进展/逆转的器官型模型的时间序列基因表达数据进行案例研究，以深入了解在此过程中驱动基因网络时间重新连接的机制。最后，我们还将提供一个软件平台，向公众提供在这个项目中开发的工具。到目前为止，还没有在一个统一的框架下考虑时间和空间变化的生物相互作用。我们提出的工作是对这一重要问题的初步尝试。我们提出的工作是在现有方法基础上向前迈出的重要一步。我们设想了一种新的范式，它可以促进：1)随着空间和时间的变化而进化的基因网络的统计推断和学习，可能是对各种刺激的反应，也可能是介导基因组与环境的相互作用。2)深入探索网络重新布线的功能基础、功能演化的动态轨迹和趋势。3)揭示发生在动态系统中的瞬时事件，建立对基因调控、网络形成和进化机制的预测性理解。4)计算算法快速准确，具有更强的统计保证，在大规模动态网络分析中具有更强的可扩展性和鲁棒性。5)为公众提供了一整套方便的软件包和用户界面，用于动态网络分析。