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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.

研究与开发1：生成微分和动态网络-项目总结