TR&D 1 - Generating Differential and Dynamic Networks

基本信息

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
项目类别：
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

项目摘要

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：生成差分动态网络——项目摘要生物系统具有令人难以置信的多样性和动态性，在一个系统中存在数百种已知的细胞类型和状态相比之下，复杂的多细胞生物（例如人类）通常使用分子网络和路径图。显示有机体所有相互作用的单一静态视图，主要是由于成本和技术限制最近，基因和蛋白质相互作用图谱技术取得了一系列突破性的实验进展。使网络能够在空间和时间上以更高的覆盖范围和更精细的分辨率进行映射新的质谱技术可以捕获蛋白质的全面变化。以更低的成本和更高的速度表达和磷酸化，从而实现差分网络的测量临床样本和其他环境中的表达信息，包括单细胞。 RNA-seq (scRNA-seq) 现在实现了每个细胞转录状态的高分辨率测量最后，新的高分辨率质谱工作流程可实现全面的分析。样本中多个时间点的相互作用组图谱，具有跨组织或内部的空间分辨率不同的细胞区室。在此 TR&D 中，我们开发了利用这些新质量数据的新计算技术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] 捕获完全数据驱动的分子信息从目标扰动流向下游细胞反应预测动态网络模型；[目标 2] 定义单个细胞类型的功能表征机制并对发育谱系的动态进行建模；以及[目标 3] 可视化、分析和预测模型跨生物环境（例如疾病与正常情况）蛋白质相互作用的差异变化。技术研究和开发目标是由一系列驱动生物医学项目（DBP）推动的，包括蛋白质相互作用的全局图谱 (DBPs 1-2,5) 和侧重于理解的单细胞生物学组织开发与再生医学工程应用（DBP 6,7）。由技术合作伙伴支持，这将帮助我们使用来自生物的基因功能信息本体论和数据库 (TPs 1,3) 和 scRNA-seq 数据门户 (TP 5)，用于表征差异和细胞、组织和疾病状态的动态网络。