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A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining.

Chen BS, Yang SK, Lan CY, Chuang YJ - BMC Med Genomics (2008)

Bottom Line: When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease.Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network.Compared with previous methodologies reported in the literatures, the proposed gene network perturbation method has shown a great improvement in analyzing the systemic inflammation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300, Taiwan. bschen@ee.nthu.edu.tw.

ABSTRACT

Background: Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions.

Results: In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs) such as NF-kappaB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin). Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection) of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network.

Conclusion: In this study, Data mining and dynamic network analyses were integrated to examine the gene regulatory network in the inflammatory response system. Compared with previous methodologies reported in the literatures, the proposed gene network perturbation method has shown a great improvement in analyzing the systemic inflammation.

No MeSH data available.


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The inflammatory transcriptional gene network in immune system without LPS. The inflammatory gene network in normal condition.
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Figure 4: The inflammatory transcriptional gene network in immune system without LPS. The inflammatory gene network in normal condition.

Mentions: Based on the 49 target genes (see Table 1) and their candidate regulators [see Additional file 2, Column (C)], we construct a rough gene regulatory network of the human inflammatory system. Then, according to the rough gene regulatory network, we set up the dynamic model for the rough gene regulatory network to prune it once more to set up a refined gene regulatory network by a system identification scheme and parsimonious AIC method via microarray data. At this point, we can construct two more refined gene regulatory networks for both the inflammatory/activated and the normal/resting conditions by the same construction flow chart shown in Figure 1, and draw two gene regulatory networks by the Osprey tool [27] (See Figure 3 and 4). In Figure 3, there are 94 nodes with 336 edges for the inflammatory/activated gene network and in Figure 4, there are 66 nodes with 264 edges for the normal/resting gene network.


A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining.

Chen BS, Yang SK, Lan CY, Chuang YJ - BMC Med Genomics (2008)

The inflammatory transcriptional gene network in immune system without LPS. The inflammatory gene network in normal condition.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2567339&req=5

Figure 4: The inflammatory transcriptional gene network in immune system without LPS. The inflammatory gene network in normal condition.
Mentions: Based on the 49 target genes (see Table 1) and their candidate regulators [see Additional file 2, Column (C)], we construct a rough gene regulatory network of the human inflammatory system. Then, according to the rough gene regulatory network, we set up the dynamic model for the rough gene regulatory network to prune it once more to set up a refined gene regulatory network by a system identification scheme and parsimonious AIC method via microarray data. At this point, we can construct two more refined gene regulatory networks for both the inflammatory/activated and the normal/resting conditions by the same construction flow chart shown in Figure 1, and draw two gene regulatory networks by the Osprey tool [27] (See Figure 3 and 4). In Figure 3, there are 94 nodes with 336 edges for the inflammatory/activated gene network and in Figure 4, there are 66 nodes with 264 edges for the normal/resting gene network.

Bottom Line: When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease.Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network.Compared with previous methodologies reported in the literatures, the proposed gene network perturbation method has shown a great improvement in analyzing the systemic inflammation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300, Taiwan. bschen@ee.nthu.edu.tw.

ABSTRACT

Background: Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions.

Results: In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs) such as NF-kappaB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin). Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection) of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network.

Conclusion: In this study, Data mining and dynamic network analyses were integrated to examine the gene regulatory network in the inflammatory response system. Compared with previous methodologies reported in the literatures, the proposed gene network perturbation method has shown a great improvement in analyzing the systemic inflammation.

No MeSH data available.


Related in: MedlinePlus