背景
發炎反應是很多人類疾病中的一個特徵，當主要的治病因素未知時，建構發炎基因調控網路有時能提供我們更多的理解來幫助找出病徵。因此，去了解發炎反應的機制是對於疾病的了解和治療的方式有很大的幫助。
結果
這本篇論文中，我們使用了很多理論跟資料庫，來描述動態機基因轉錄反應。我們是利用人類的白血球動態基因表現量接收到發炎的刺激(細菌毒素)來計算基因調控網路的動態參數，而根據這些參數，我們可找出在發炎反應中的特徵基因來幫助臨床上的研究。
結論
在這篇論文中，我們利用運算分析各種不同類型的資料來有效率的選擇出可能的調控子(regulators)來模擬出發炎轉錄機制。
第一章 簡介
由於DNA microarray 能夠看出病源體進入人體後，寄主細胞中巨大的基因表現量的改變，這可以幫助系統生物學家在做人類的發炎反應研究中有更多的理解。但是要克服如何辨識和找出有關生物的意義在這麼大量的資料當中，是一個挑戰。在本篇論文中，藉由我們發展出來的方法，能夠比較發炎基因調控網路在發炎和正常時不同。再使用動態表現量模擬分析，能有效實現完整的動態發炎基因網路機制。
（有關本章詳細內容，請參考英文版論文第一章）
第二章 方法
藉由許多大規模的生物實驗相關的文獻結合資料庫，我們可以建立粗略的網路，再利用Cross-correlation和統計方法Akaike Infromation Criterion (AIC) 逐步篩選，進而得到發炎動態基因調控網路。

（有關本章詳細內容，參考英文版論文第二章）
第三章 結果
本論文針對本論文的目標基因來分析結果，依據現有的生物知識及文獻探討，以及將本文所找出的調控之調控能力加以量化探討，最後建構整個調控網路的架構。
（有關本章詳細內容，參考英文版論文第三章）
第四章 討論與結論
我們不但成功的建立出發炎動態基因調控網路，並比較在發炎情況和正常期情況時的不同，總結本論文主要貢獻及優點，相信在現今生物晶片的大量發展下，可以藉由本文所提供的系統化分析方式，深入探討影響基因表現的調控網路。未來將可利用本分析技巧預測基因表現及提供實驗方向。
（有關本章詳細內容，參考英文版論文第四章）

Background:
Inflammation is a hallmark of many human diseases. When primary pathogenetic events are unknown, construction of gene regulatory network of inflammation is sometimes the best way to gain more insight into it. To better elucidate the mechanisms underlying systemic inflammation is an important topic to monitor disease progress for individual treatment regimens. It is more appealing to construct a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases.
Results:
In this study, we present a gene regulatory network via database (Ensembl, JASPAR), Cross-correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) to describe genome-wide transcriptional responses in the context of dynamic genes, which are regulated by transcription factors (TFs) including family. This approach is based on the dynamic equation of blood leukocyte gene expression profiles of human subject to receive an inflammatory stimulus (bacterial endotoxin). Based on the magnitudes of kinetic parameters of dynamic gene regulatory network, we could identify significant properties (such as susceptibility to infection) of inflammation systems, which are useful for clinic research.
Conclusion:
It is important to find that the transcriptional programs are modified as cell progresses a reaction to change environmental conditions. In this study, a computational analysis of multiple types of data was developed to efficiently select candidates of regulators of the network in the inflammation system. Compared with previous results in literature, the proposed gene network construction method is found with significant improvement.

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