生物網路模型的建構對於系統生物學來說是一個很重要的研究主題。一般而言，在分子生物層次，生物網路可以分為訊息傳遞網路、基因調控網路及代謝網路。過去大部分的研究都著重在單一類別的分子網路建構，卻忽略了探討整合適網路中，不同階層網路間的的交互作用。因此，發展一個系統化的方法，整合理論假設及實驗資料，建立一個整合性的生物網路模型並探討其生理意義是很必要的。
此研究是以單一細胞的整合性網路為研究對象，並以連結訊號傳遞網路以及基因調控網路為目標。我們提出一個結合正向工程及逆向工程的系統化方法來連結訊號傳遞反應及基因的表現。為了證明此一方法的可行性，我們以連結 NF-κB 的訊息傳遞網路及基因調控為例子。NF-κB 已經被證實在發炎反應中扮演非常關鍵的角色，因此我們想看看不同訊號對於基因表現的影響。首先，我們利用正向工程（混合式功能斐式圖）建立 NF-κB 的訊息傳遞模型，以及利用逆向工程（網路元件分析）建立其基因調控網路。接著我們連接兩個網路並且成功辨別出在不同刺激強度下的 IKK 活性曲線，而此結果也與生物實驗驗證吻合。另外，我們還發現一些發炎反應中重要之細胞生長素和趨化素基因（TNF-α、IL-1、IL-6、CXCL1、CXCL2 和 CCL3）的表現曲線和 NF-κB 的活性曲線相當一致。而 NF-κB 對於這些基因在我們建立的調控網路裡有較強的影響力。這些不同強度發炎刺激下的調控影響，反應 NF-κB 在急性發炎所扮演的關鍵角色，並可進一步幫助我們去了解系統性發炎的併發症。
我們成功地整合訊息傳遞網路以及基因調控網路。未來若有更完整之訊息傳遞動力學方程式係數，將可更進一步討論複雜訊號傳遞之串音問題。這個研究已經對於生物系統整合模型的建立提供了提供了全新的視野。

Network modeling is an important topic on systems biology. Several different kinds of biological network can be distinguished at the molecular level: signal transduction networks or protein-protein interaction networks, gene regulatory networks, and metabolic networks. Although a great deal of effort has been made on modeling these distinct types of molecular networks respectively, the networks of interactions and altogether different level of detail on integrated networks seems to be lacking. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to integrate the different biological networks and identify the physiological consequences.
In this study, we focus exclusively on molecular processes that take place within a cell, and specifically on integration two distinct types of cellular mechanisms: signal transduction and transcriptional regulation. We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome.
We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-κB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.

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