A network-based approach to modelling cell-niche interactions and its application to studying salamander limb regeneration

细胞命运的决定是一个涉及多层调控的复杂过程。不仅细胞的内在遗传程序，而且外部影响，如其生态位（微环境）所施加的影响，都可以诱导细胞从一种状态过渡到另一种状态。这种生态位效应是通过细胞内信号转导途径来解释的，进而导致细胞基因调控网络（GRN）的变化。关键的生态位成分包括与邻近细胞的直接相互作用、其他细胞分泌的因子、免疫控制和缺氧等环境条件。生态位相互作用的影响包括各种干细胞的分化倾向、上皮向间质转化、细胞迁移和再生。蝾螈是唯一的四足动物，其成年肢体可以再生所有组成组织。了解蝾螈肢体再生的机制与包括人类在内的哺乳动物有直接关系，因为研究表明，小鼠组织的再生利用了与蝾螈肢体再生相似的细胞外基质（ECM）环境。因此，从细胞生态位相互作用和哺乳动物再生两方面研究诱导蝾螈肢体再生的分子因素至关重要。虽然有几种工具可以推断活跃/不活跃的信号通路，但它们并没有将信号通路与GRN结合起来，因此无法预测负责细胞状态转换的关键信号通路。在此，我们提出了一种整合信号通路和grn的计算方法，并设计了一种系统的计算策略，通过差分网络分析来预测关键信号通路及其靶基因。我们将把这种方法应用于蝾螈肢体再生系统，并解决哪些信号通路组合负责再生。该项目的完成将丰富社区知识，使再生在临床上更有用。

Cell fate decision is a complex process involving several layers of regulation. Not only the intrinsic genetic programme of the cell, but also external influences such as those exerted by its niche (microenvironment), can induce a transition from one cellular state to another. The niche effect is interpreted through intracellular signal transduction pathways, which in turn brings about changes in the gene regulatory network (GRN) of the cell. Key niche components include direct interactions with neighbouring cells, secreted factors by other cells, immunological control, and environmental conditions such as hypoxia. The effects of niche interactions include differentiation tendency of various stem cells, epithelial-to-mesenchymal transitions, cell migration and regeneration. Salamander is the only tetrapod, where the adult limb functionally regenerates all constituent tissues. Understanding the mechanism of salamander limb regeneration has direct relevance to mammals including humans, as it has been shown that regeneration of mouse tissue utilized the extracellular matrix (ECM) environments that were similar to those characterized in salamander limb regeneration. Thus, investigating the molecular factors that induce salamander limb regeneration is crucial from both aspects of cell niche interactions and addressing regeneration in mammals. Although there exist several tools that can infer active/inactive signalling pathways, they do not integrate signalling pathways with a GRN, and are therefore unable to predict key signalling pathways responsible for cellular state transitions. Here we propose a novel computational method that integrates signalling pathways and GRNs, and devise a systematic computational strategy for predicting key signalling pathways and their target genes by means of differential network analysis. We will apply this method to the salamander limb regeneration system and address which combinations of signalling pathways are responsible for regeneration. The completion of this project will enrich community knowledge and enable regeneration to become clinically more useful.