再生是一項重要的生物特徵，而受傷部位的再生能力會隨著不同器官、個體、物種而有所差異。目前尚未有一套系統性的理論去解釋各層次間的差異。本研究將在一個基於動態調控數學模型的框架下，對再生與非再生系統進行分析。再生與非再生系統的動態結構可被再生與非再生系統的全基因組基因表現量及多資料庫的候選網路所辨識。換言之，我們利用動態調控模型中的調控參數去刻劃再生與非再生動態基因調控網路的特徵。網路中節點的功能分析發現了在非再生系統中有異常活化的細胞間溝通，這很有可能阻礙再生的進行。而網路的中心節點及其周圍節點標定了兩個系統中不同的基因調控熱區。接著，一個由三個節點所構成的簡單拓樸結構在再生與非再生系統的豐富程度與其結構之特性進一步說明兩個系統間的差異。隨著分析對象逐漸複雜化，我們進一步考慮再生與非再生系統中共同的二十個功能模組。功能模組間的連結強弱揭露了兩個系統在功能層次上的調控差異。此外，整體網路的連結度、群聚度的分布則涉及到兩個系統在演化上的差異。除了系統結構特性的分析外，動態調控模型的引入使我們能根據系統理論提出一個數值，用來評估再生與非再生系統中的子系統對訊號處理的能力。相較於三角結構的豐富程度，三角結構的訊號傳遞能力提供了另一個可區分再生與非再生系統的維度。而基於功能模組間的連結與功能模組的訊號傳遞能力，我們提出了一個具有三層功能組織的再生機制，用來解釋兩個系統在功能層次上調控機制的差異。最後，我們進一步提出一個基於訊號傳遞能力的藥物設計方法。該方法可提供更廣泛的目標選擇、降低個體差異對藥物效果的影響並進一步指出未來再生醫學的發展方向。

Regeneration is an essential characteristic of biological organisms. The ability to regenerate injured sites is varied across organs, individuals, and species. No systematic theory explains the differences of regenerative capacity at different organizational levels. In this study, we analyzed the regenerative and nonregenerative systems in a unified framework based on a dynamic regulatory model. With the gene expression levels of the regenerative and nonregenerative systems and the multi-database-derived candidate regulations of the genes, system identification techniques are used to identify the dynamic structure of the regenerative and nonregenerative systems. That is, we exploited the regulatory abilities in the dynamic regulatory model to delineate the dynamic regulatory networks of the regenerative and nonregenerative systems. The functional analysis of the nodes in the constructed networks reveals that an excess cell communication in the nonregenerative systems may hinder the regeneration of the injured brain. The hubs and their network neighbors reflect the regulatory hot zone in the regenerative and nonregenerative networks. Next, a subgraph consisting of three connected nodes, called a triad, was considered for further systematic analysis of regenerative and nonregenerative networks. The topological structure of the triads explains the difference between the triad significance profiles of the regenerative and nonregenerative networks. In line with the previous results of the functional analysis, we focused on the functional networks consisting of the 20 common enriched functional modules in the regenerative and nonregenerative systems. The strength in the connectivity of the 20 modules reveals the different regulations of the regenerative and nonregenerative systems at the functional level. Further, the distributions of in-degree, out-degree, and clustering coefficient could implicate the evolutionary difference between the regenerative and nonregenerative systems. The advantage of the dynamic regulatory model to interpret the regenerative and nonregenerative networks motivated us to propose a quantity, called transduction ability, to evaluate the dynamical systematic property of the subsystems in the regenerative and nonregenerative systems. Against to the triad enrichment, the transduction ability of triads provides another dimension for discriminating the regenerative biological systems from nonregenerative ones. A three-level functional organization is proposed to explain the different regulatory mechanisms at the functional level based on the transduction ability and connectivity of the modules. Moreover, based on the regeneration mechanisms by comparing transduction abilities between regenerative and nonregenerative networks, we devised a drug design method to improve their transduction abilities of modules in the nonregenerative systems. The design paradigm based on the transduction ability will provide a wider choice of drug targets, reduce the effect of individual heterogeneity on drug efficacy, and shed light on the future of regenerative medicine from the systems biology perspective.

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