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A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation.

Kang CC, Chuang YJ, Tung KC, Chao CC, Tang CY, Peng SC, Wong DS - BMC Bioinformatics (2011)

Bottom Line: Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology.We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses.This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw

ABSTRACT

Background: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.

Results: In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified.

Conclusions: We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

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Related in: MedlinePlus

The pattern of model simulation compare with experimental data. Simulations profile of our model (Upper boxes) and coresponding original data [2,17,18]. Blue bars represent on and white space means off, respectively. X-axis is the time course (minutes) and y-axis means the activities of target in our model in distinct conditions: (A) IKK (left) and NFkB’s (right) activities induced by TNF 45 minutes treatment with wild type and A20 mutant. (B) IKK (left) and NFkB’s (right) activities with wild type and A20 knockout condition of LPS 45 minutes treatment. (C) NFkB’s activities induced by transient 15 minutes IL-1 in wild type and IκBα mutant. (D) IkBa’s activities with TNF (left) and LPS (right) 45 minutes stimuli.
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Figure 4: The pattern of model simulation compare with experimental data. Simulations profile of our model (Upper boxes) and coresponding original data [2,17,18]. Blue bars represent on and white space means off, respectively. X-axis is the time course (minutes) and y-axis means the activities of target in our model in distinct conditions: (A) IKK (left) and NFkB’s (right) activities induced by TNF 45 minutes treatment with wild type and A20 mutant. (B) IKK (left) and NFkB’s (right) activities with wild type and A20 knockout condition of LPS 45 minutes treatment. (C) NFkB’s activities induced by transient 15 minutes IL-1 in wild type and IκBα mutant. (D) IkBa’s activities with TNF (left) and LPS (right) 45 minutes stimuli.

Mentions: Figure 4 shows the comparison between our model outcome and the experimental data in the learning sets. The upper boxes are each active pattern with specific treatment (the description was written on the graph) and compared with real data (the western blot data under corresponded box). The MSE value between our model and data is 0.0919.


A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation.

Kang CC, Chuang YJ, Tung KC, Chao CC, Tang CY, Peng SC, Wong DS - BMC Bioinformatics (2011)

The pattern of model simulation compare with experimental data. Simulations profile of our model (Upper boxes) and coresponding original data [2,17,18]. Blue bars represent on and white space means off, respectively. X-axis is the time course (minutes) and y-axis means the activities of target in our model in distinct conditions: (A) IKK (left) and NFkB’s (right) activities induced by TNF 45 minutes treatment with wild type and A20 mutant. (B) IKK (left) and NFkB’s (right) activities with wild type and A20 knockout condition of LPS 45 minutes treatment. (C) NFkB’s activities induced by transient 15 minutes IL-1 in wild type and IκBα mutant. (D) IkBa’s activities with TNF (left) and LPS (right) 45 minutes stimuli.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044271&req=5

Figure 4: The pattern of model simulation compare with experimental data. Simulations profile of our model (Upper boxes) and coresponding original data [2,17,18]. Blue bars represent on and white space means off, respectively. X-axis is the time course (minutes) and y-axis means the activities of target in our model in distinct conditions: (A) IKK (left) and NFkB’s (right) activities induced by TNF 45 minutes treatment with wild type and A20 mutant. (B) IKK (left) and NFkB’s (right) activities with wild type and A20 knockout condition of LPS 45 minutes treatment. (C) NFkB’s activities induced by transient 15 minutes IL-1 in wild type and IκBα mutant. (D) IkBa’s activities with TNF (left) and LPS (right) 45 minutes stimuli.
Mentions: Figure 4 shows the comparison between our model outcome and the experimental data in the learning sets. The upper boxes are each active pattern with specific treatment (the description was written on the graph) and compared with real data (the western blot data under corresponded box). The MSE value between our model and data is 0.0919.

Bottom Line: Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology.We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses.This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw

ABSTRACT

Background: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.

Results: In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified.

Conclusions: We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

Show MeSH
Related in: MedlinePlus