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(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，未来可以控制它们来生长人工器官、修复组织和开发新的治疗方法。然而，到目前为止，开发分析这些数据的方法所做的工作很少。已经建立了细胞在发育过程中形成组织模式的动态数学模型，但这些模型在细胞群体/组织水平上发挥作用，并将细胞视为一个隔间或空间梯度，没有考虑单个细胞-细胞之间的相互作用。也许研究得最好的细胞-细胞相互作用网络是线虫的细胞-细胞相互作用网络，这一网络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，神经系统中的中间神经元连接更紧密，导致了这些细胞充当中央处理单元的解释。最近的工作预测了参与癌症治疗耐药的细胞-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丰富，并且特定的组件比其他组件更重要，然而这项工作到目前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-细胞网络信息量将迅速增长。这一领域的数据和分析方法的增长将使网络科学能够有助于更广泛地了解生理系统。为了实现这一目标，我们将在发展新的计算方法的支持下，推断、分析和可视化各种系统中的细胞间通信网络及其相应的细胞内信号网络和通路的多尺度模型。特别是，我们将根据细胞类型特定的分子图谱推断细胞-细胞相互作用网络，发现细胞-细胞相互作用网络的关键角色和规则，并识别控制细胞-细胞相互作用的细胞内途径。

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.