TR&D 1 - Generating Differential and Dynamic Networks

R&D 1：生成差异和动态网络--项目总结 生物系统是令人难以置信的多样化和动态的，在一个 复杂的多细胞有机体，如人类。相比之下，分子网络和路径图通常 显示有机体所有相互作用的单一静态视图，这主要是由于成本和技术限制 基因和蛋白质互作图谱技术。最近，一系列突破性的实验进展 使网络在空间和时间上的覆盖率和分辨率远远高于 以前是有可能的。新的质谱学技术可以捕捉蛋白质的全面变化 以更低的成本和更快的速度进行表达和磷酸化，使差异网络的测量成为可能 临床样本和其他上下文中的表达信息。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对转录状态的高分辨率测量 在多个时间点上。最后，新的高分辨率质谱学工作流程支持全面 样本中多个时间点的交互作用组图以及跨组织或在组织内的空间分辨率 不同的细胞隔间。 在这项研发中，我们开发了新的计算技术，利用这些质量上的新数据 类型以更好地理解网络如何在不同的生物条件下发挥作用并对其进行量化建模 并推导出全细胞动态网络模型。这些技术的目标是[​Aim 1]捕获 在完全数据驱动的情况下，分子信息流从靶向扰动到下游细胞响应 预测性动态网络模型；[​Aim 2]从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；以及[​Aim 3]可视化、分析和预测性建模 不同生物背景下蛋白质相互作用的不同变化，如疾病与正常。我们的 技术研究和开发目标受到一系列推动生物医学项目(DBP)的推动， 包括蛋白质相互作用的全球图谱(DBP 1-2，5)和侧重于理解的单细胞生物学 再生医学中的组织发展与工程应用(DBPS 6，7)。这些目标将是 由技术伙伴关系支持，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(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。 ​ ​ ​

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. ​ ​ ​