系統化生物網路整合模型之建構與應用

• Main Authors: Peng, Shih-Chi, 彭士齊
• Other Authors: Tang, Chuan-Yi
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/37051551418460251427

博士 === 國立清華大學 === 資訊工程學系 === 98 === 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.