Cell and tissue communication networks

点击翻译按钮获取中文摘要

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.