Cell and tissue communication networks

细胞和组织通讯网络

• 批准号: RGPIN-2016-05529
• 负责人: Bader, Gary
• 金额: $ 3.93万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=628802
• 关键词: Cell; tissue; communication; networks

Cell-cell interaction networks are an emerging area of network science where nodes represent cells, and edges represent physical or chemical (e.g. hormonal) interactions. These inter-cellular signaling and regulation networks are important to model and understand as they underlie tissue and physiological systems and could in the future be controlled to grow artificial organs, heal tissues and develop novel therapies. However, minimal work has been performed to date to develop methods to analyze these data. Dynamic mathematical models of cells patterning into tissues during development have been built, but they function at the cell population/tissue level and treat cells as a compartment or spatial gradient and do not consider individual cell-cell interactions. Perhaps the best-studied cell-cell interaction network is that of the worm, Caenorhabditis elegans, which has been completely mapped over organism development by microscopy. Network analysis by clustering found that interneurons are more densely connected in the nervous system compared to sensory or motor neurons, leading to the interpretation that these cells act as central processing units. More recent work predicted cell-cell networks involved in cancer therapy resistance, and found that specific network motifs are enriched in inter-cellular cytokine mediated communication networks and that specific components are more important than others, however this work has thus far studied small cell-cell network models that were never experimentally validated. As technology for single cell, neural network and stem cell measurement improves, we expect rapid growth in the amount of available cell-cell network information. Growth of data and analysis methods in this area will enable network science to contribute to the wider understanding of physiological systems. Towards this goal, we will infer, analyze and visualize multi-scale models of inter-cellular communication networks and their corresponding intracellular signaling networks and pathways in a variety of systems, supported by development of novel computational methods. In particular, we will infer cell-cell interaction networks from cell type specific molecular profiles, discover key players and rules of cell-cell interaction networks, and identify intracellular pathways that control and are controlled by cell-cell interactions.

细胞-细胞相互作用网络是网络科学的一个新兴领域，其中节点表示细胞，边表示物理或化学（例如激素）相互作用。这些细胞间信号传导和调节网络对于建模和理解非常重要，因为它们是组织和生理系统的基础，并且在未来可以被控制来生长人工器官，愈合组织和开发新的疗法。然而，迄今为止，在开发分析这些数据的方法方面所做的工作很少。已经建立了在发育期间细胞图案化成组织的动态数学模型，但是它们在细胞群体/组织水平上起作用，并且将细胞视为隔室或空间梯度，并且不考虑单个细胞-细胞相互作用。也许研究得最好的细胞-细胞相互作用网络是线虫，秀丽隐杆线虫，它已经通过显微镜完全绘制了生物体发育的地图。通过聚类进行的网络分析发现，与感觉或运动神经元相比，中间神经元在神经系统中的连接更密集，这导致了这些细胞作为中央处理单元的解释。最近的工作预测了参与癌症治疗抗性的细胞-细胞网络，并发现特定的网络基序在细胞间细胞因子介导的通信网络中富集，并且特定的组分比其他组分更重要，然而这项工作迄今为止研究了从未经过实验验证的小细胞-细胞网络模型。随着单细胞、神经网络和干细胞测量技术的改进，我们预计可用的细胞-细胞网络信息量将快速增长。这一领域的数据和分析方法的增长将使网络科学有助于更广泛地理解生理系统。为了实现这一目标，我们将在新型计算方法开发的支持下，推断、分析和可视化各种系统中细胞间通信网络及其相应的细胞内信号网络和途径的多尺度模型。特别是，我们将从细胞类型特异性分子谱推断细胞-细胞相互作用网络，发现细胞-细胞相互作用网络的关键参与者和规则，并确定控制细胞-细胞相互作用的细胞内途径。