BACKGROUND: Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. RESULTS: We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. CONCLUSION: Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.
Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles.
利用布尔动力学揭示体外时间细胞因子表达谱中的巨噬细胞活化
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作者:Ramirez Ricardo, Herrera Allen Michael, Ramirez Joshua, Qian Chunjiang, Melton David W, Shireman Paula K, Jin Yu-Fang
| 期刊: | BMC Bioinformatics | 影响因子: | 3.300 |
| 时间: | 2019 | 起止号: | 2019 Dec 18; 20(1):725 |
| doi: | 10.1186/s12859-019-3304-5 | 研究方向: | 细胞生物学 |
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