TR&D 1 - Generating Differential and Dynamic Networks

TR

• 批准号: 10629204
• 负责人: CHRIS SANDER
• 金额: $ 34.01万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-13 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629204
• 关键词: Adoption; Automobile Driving; Biological; Biology; Cell model; Cells; Cellular biology; Clinical; Complex; Data; Databases; Development; Disease; Drug Combinations; Ecosystem; Engineering; Fractionation; Genes; Genetic Transcription; Genomics; Goals; Human; Individual; Machine Learning; Maps; Mass Spectrum Analysis; Measurement; Measures; Methods; Modeling; Molecular; Network-based; Normal tissue morphology; Ontology; Organism; Pathway Analysis; Pathway interactions; Pharmaceutical Preparations; Phosphorylation; Positioning Attribute; Protein Interaction Mapping; Proteins; Proteomics; Regenerative Medicine; Resolution; Resources; Sampling; Speed; Techniques; Technology; Time; Tissues; Visualization; Work; biological systems; cancer therapy; cell type; combinatorial; cost; data portal; design; gene function; network models; new technology; novel; predictive modeling; programs; protein expression; response; single-cell RNA sequencing; technology research and development; tissue repair; tool

TR&D 1: GENERATING DIFFERENTIAL AND DYNAMIC NETWORKS – PROJECT SUMMARY Biological systems are incredibly diverse and dynamic, with hundreds of known cell types and states in a complex multicellular organism such as human. In contrast, molecular network and pathway maps typically show a single static view of all interactions for an organism, largely because of cost and technical limitations of gene and protein interaction mapping technologies. Recently, a range of breakthrough experimental advances is enabling networks to be mapped at much higher coverage and finer resolution in space and time than previously possible. New mass spectrometry technology can capture comprehensive changes in protein expression and phosphorylation at lower cost and higher speed, enabling measurement of differential network expression information in clinical samples and other contexts. Single-cell genomics, including single-cell RNA-seq (scRNA-seq), now achieves high resolution measurements of transcriptional state on a per cell basis over multiple time points. Finally, new high-resolution mass spectrometry workflows enable comprehensive interactome mapping in a sample at multiple time points and with spatial resolution across a tissue or within different cellular compartments. In this TR&D, we develop new computational technologies that take advantage of these qualitatively new data types to better understand and quantitatively model how networks function in differential biological conditions and to infer whole-cell dynamic network models. The goals of these technologies are to [​Aim 1] capture the molecular information flow from targeted perturbations to downstream cellular responses in fully data-driven predictive dynamic network models; [​Aim 2] functionally characterize mechanisms defining individual cell types and model the dynamics of developmental lineages; and [​Aim 3] visualize, analyze and predictively model differential changes in protein interactions across biological contexts, such as disease versus normal. Our technology research and development aims are motivated by a range of Driving Biomedical Projects (DBPs), including global mapping of protein interactions (DBPs 1-2,5) and single cell biology focused on understanding tissue development with engineering applications in regenerative medicine (DBPs 6,7). These aims will be supported by Technology Partnerships that will help us use gene function information from biological ontologies and databases (TPs 1,3) and scRNA-seq data portals (TP 5) to characterize differential and dynamic networks of cells, tissues and disease states. ​ ​ ​

TR&D 1：生成差分和动态网络-项目总结 生物系统是令人难以置信的多样性和动态的，在一个特定的环境中有数百种已知的细胞类型和状态。 复杂的多细胞生物，如人类。相比之下，分子网络和途径图通常 显示一个有机体所有相互作用的单一静态视图，主要是因为成本和技术限制， 基因和蛋白质相互作用作图技术。最近，一系列突破性的实验进展 使网络能够以更高的覆盖率和更精细的空间和时间分辨率进行映射， 以前可能的。新的质谱技术可以捕捉蛋白质的全面变化 以更低的成本和更高的速度表达和磷酸化，使得能够测量差异网络 在临床样品和其他环境中的表达信息。单细胞基因组学，包括单细胞 RNA-seq（scRNA-seq）现在可以在每个细胞的基础上实现转录状态的高分辨率测量 在多个时间点。最后，新的高分辨率质谱工作流程可实现全面的 在多个时间点在样品中的相互作用组映射，并且具有跨组织或组织内的空间分辨率 不同的细胞区室。 在本TR&D中，我们开发了新的计算技术，以利用这些定性的新数据 类型，以更好地理解和定量建模网络如何在不同的生物条件下发挥作用 并推断全细胞动态网络模型。这些技术的目标是[目标1]捕获 分子信息流从目标扰动到下游细胞反应， 预测动态网络模型; [目的2]功能上表征定义单个细胞类型的机制 并对发育谱系的动态进行建模;[目标3]可视化、分析和预测建模 在不同的生物背景下，如疾病与正常的蛋白质相互作用的差异变化。我们 技术研究和开发目标受到一系列驱动生物医学项目（DBP）的激励， 包括蛋白质相互作用的全球绘图（DBPs 1- 2，5）和专注于理解的单细胞生物学 组织开发与再生医学工程应用（DBPs 6，7）。这些目标将是 技术合作伙伴的支持，这将帮助我们使用来自生物学的基因功能信息， 本体和数据库（TP 1，3）和scRNA-seq数据门户（TP 5），以表征差异和 细胞、组织和疾病状态的动态网络。 ​ ​ ​