Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles
Abstract 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 alte...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-019-3304-5 |
| _version_ | 1818610022831095808 |
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| author | Ricardo Ramirez Allen Michael Herrera Joshua Ramirez Chunjiang Qian David W. Melton Paula K. Shireman Yu-Fang Jin |
| author_facet | Ricardo Ramirez Allen Michael Herrera Joshua Ramirez Chunjiang Qian David W. Melton Paula K. Shireman Yu-Fang Jin |
| author_sort | Ricardo Ramirez |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-16T15:07:50Z |
| format | Article |
| id | doaj.art-a22261ab78534568921f3561c4c7dac6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-16T15:07:50Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a22261ab78534568921f3561c4c7dac62022-12-21T22:27:04ZengBMCBMC Bioinformatics1471-21052019-12-0120112310.1186/s12859-019-3304-5Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profilesRicardo Ramirez0Allen Michael Herrera1Joshua Ramirez2Chunjiang Qian3David W. Melton4Paula K. Shireman5Yu-Fang Jin6Department of Electrical and Computer Engineering, The University of Texas at San AntonioDepartment of Electrical and Computer Engineering, The University of Texas at San AntonioDepartment of Electrical and Computer Engineering, The University of Texas at San AntonioDepartment of Electrical and Computer Engineering, The University of Texas at San AntonioDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical CenterDepartment of Surgery, Long School of Medicine, University of Texas Health Science Center San AntonioDepartment of Electrical and Computer Engineering, The University of Texas at San AntonioAbstract 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.https://doi.org/10.1186/s12859-019-3304-5Macrophage polarizationBoolean networksCytokinesInflammation |
| spellingShingle | Ricardo Ramirez Allen Michael Herrera Joshua Ramirez Chunjiang Qian David W. Melton Paula K. Shireman Yu-Fang Jin Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles BMC Bioinformatics Macrophage polarization Boolean networks Cytokines Inflammation |
| title | Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| title_full | Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| title_fullStr | Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| title_full_unstemmed | Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| title_short | Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| title_sort | deriving a boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles |
| topic | Macrophage polarization Boolean networks Cytokines Inflammation |
| url | https://doi.org/10.1186/s12859-019-3304-5 |
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