Motivation
Development in systems biology can provide a global view to investigate numerous system properties of molecular networks. However there is still lack of systematic approaches to reconstruct the stochastic dynamic protein-protein association networks at different time stages via high-throughput data for further analysis of the possible cross-talks among different pathways.
Results
In this study we attempt to integrate protein-protein interactions from databases to construct the rough protein-protein association networks (PPAN) for immune systems. Further, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model are used to rebuild the significant PPANs at different time stage to illustrate the development of innate immune system. A new cross-talk ranking method is also suggested to evaluate the potential core elements in the related signaling pathways of Toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R). The highly ranked cross-talks which are functionally relevant to the TNFα stress are also identified. A bow-tie structure is then extracted from these cross-talk pathways for the robustness of network structure, the coordination of signal transduction and feedback control for efficient immune responses to different stimuli, and several characteristics of signal transduction and feedback control in the infected organism are observed.
Conclusion
A systematic approach based on stochastic dynamic model is proposed for biologists to get more insight into the underlying defense mechanisms of immune systems via the construction of corresponding signaling networks upon specific stimulus. Apart from immune system, this systematic approach can also be applied to other signaling networks under different conditions for different species. As the experimental techniques for detecting protein expression levels advance enough and the microarray data with multiple sampling points are available in the future, the performance of the proposed method will be efficiently improved.

動機
系統生物學在近年來日益蓬勃發展，提供了研究學者們用全面性的觀點來探討許多生物系統的特性。然而在這門領域中，仍然缺乏一個系統化的方法去利用高產量的實驗數據重建不同時期之動態隨機蛋白質交互連結網路，以進行不同信號傳遞途徑之間的交互作用分析。
結果
在這篇研究當中，我們嘗試整合了由資料庫所得的蛋白質交互作用聯結來建構初步的蛋白質關聯性網路。接著我們利用一個隨機的動態模型結合受到促進腫瘤壞死因子刺激下的人體靜脈血管內皮細胞之基因表現量，來重建一個更具生物意義的蛋白質關聯性網路，並用它來描繪先天免疫系統在人體內作用的進程。在此，我們提出一個創新的信號傳遞交互作用分析方法，用來評估TNF-R, TLR-4以及IL-1R傳遞至IKK信號途徑中可能的核心蛋白質因子。在這些互相作用的信號傳遞途徑之中，我們發現了一個特殊的領結狀信號傳遞結構，這種結構有助於維持生物網路的強健性、信號傳遞的調節並根據不同的外來訊號刺激產生相對應的免疫反應。此外，許多訊號傳遞和回授控制的系統特性也在我們所建構的蛋白質關聯性網路中可以觀察到。
結論
本篇研究提供了一個根據隨機的動態模型來重建對特定刺激訊號之下的訊號傳遞網路，並幫助生物學家能夠對免疫系統潛在的防禦機制有更深一層的了解。除了免疫系統之外，這個方法亦能適用於不同物種、不同刺激之下的信號傳遞網路分析。當未來實驗技術的進步，可以讓我們偵測大範圍的蛋白質表現量以及獲得更多連續時間點的數據時，本篇所提出的方法將會具有更高的可靠度與真實性。

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