Cell and tissue communication networks

细胞-细胞相互作用网络是网络科学的一个新兴领域，其中节点代表细胞，边缘代表物理或化学（例如激素）相互作用。这些细胞间信号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，并且可以在未来控制人工器官的生长，组织的愈合和开发新的治疗方法。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。已经建立了细胞在发育过程中形成组织的动态数学模型，但它们在细胞群体/组织水平上起作用，并将细胞视为一个隔室或空间梯度，而不考虑单个细胞-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整地描绘了生物体的发育。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加紧密，这导致了这些细胞充当中央处理单元的解释。最近的研究预测了参与癌症治疗抵抗的细胞-细胞网络，并发现特定的网络基元在细胞间细胞因子介导的通信网络中丰富，并且特定的成分比其他成分更重要，然而，这项工作迄今为止研究的是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进步，我们预计可用的细胞-细胞网络信息的数量将迅速增长。该领域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，我们将通过开发新的计算方法来推断、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定的分子图谱中推断细胞-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，并确定细胞内控制和受细胞-细胞相互作用控制的途径。

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.