TR&D 1 - Generating Differential and Dynamic Networks

TR

• 批准号: 10401269
• 负责人: CHRIS SANDER
• 金额: $ 33.9万
• 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/10401269
• 关键词: Adoption; Automobile Driving; Biological; Biology; Cell model; Cells; Cellular biology; Clinical; Complex; Data; Databases; Development; Disease; Drug Combinations; Ecosystem; Engineering; Fractionation; Gene Proteins; 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; 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：生成差异和动态网络 - 项目摘要 生物系统非常多样化和动态，在一个中有数百种已知细胞类型和状态 复杂的多细胞生物，例如人类。相反，分子网络和途径图通常是 显示出有机体的所有互动的单一静态视图，主要是因为成本和技术限制 基因和蛋白质相互作用映射技术。最近，一系列突破性的实验进步 是否可以在更高的覆盖范围和时间和时间上映射网络，而不是 以前可能。新的质谱技术可以捕获蛋白质的全面变化 以较低的成本和较高的速度表达和磷酸化，以实现差异网络的测量 在临床样本和其他情况下的表达信息。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值 在多个时间点上。最后，新的高分辨率质谱工作流程可实现全面 在多个时间点中的样品中的相互作用组映射，以及跨组织或内部的空间分辨率 不同的细胞室。 在此TR＆D中，我们开发了新的计算技术来利用这些质量的新数据 更好地理解和定量建模网络如何在差异生物学条件下起作用的类型 并推断全细胞动态网络模型。这些技术的目标是[AIM 1]捕获 分子信息流从靶向扰动到完全数据驱动的下游细胞反应 预测动态网络模型； [AIM 2]在功能上表征定义单个细胞类型的机制 并建模发展谱系的动力学； [AIM 3]可视化，分析和预测模型 蛋白质相互作用的差异变化，例如疾病与正常情况。我们的 技术研发目标是由一系列驾驶生物医学项目（DBP）的动机， 包括蛋白质相互作用（DBPS 1-2,5）的全局映射和专注于理解的单细胞生物学 在再生医学中使用工程应用的组织开发（DBPS 6,7）。这些目标将是 得到技术合作伙伴关系的支持，这些伙伴关系将帮助我们使用生物学的基因功能信息 本体和数据库（TPS 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。 になったんです。英语：您可以做的第一件事就是找到最好的方法。 になったんです。英语：您可以做的第一件事就是找到最好的方法。 になったんです。英语：您可以做的第一件事就是找到最好的方法。