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The study of the influence of micro-environmental signals on macrophage differentiation using a quantitative Petri net based model

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Języki publikacji
EN
Abstrakty
EN
The complexity of many biological processes, which, thanks to the development of many fields of science, becomes for us more and more obvious, makes these processes extremely interesting for further analysis. In this paper a quantitative model of the process of macrophage differentiation, which is essential for many phenomena occurring in the human body, is proposed and analyzed. The model is expressed in the language of Petri net theory on the basis of one of the three hypotheses concerning macrophage differentiation existing in the literature. The performed analysis allowed to find an importance of individual factors in the studied phenomenon.
Słowa kluczowe
Rocznik
Strony
331--349
Opis fizyczny
Bibliogr. 29 poz., rys., schem., tab.
Twórcy
  • Institute of Computing Science, Poznań University of Technology, Piotrowo str. 2, 60-965 Poznan, Piotrowo
  • department of Clinical Biochemistry and laboratory Medicine, Poznan University of Medical Sciences, Grunwaldzka str. 6, 60-780 Poznan, Poland
  • Institute of Computing Science, Poznan University of Technology, Piotrowo str. 2, 60-965 Poznan
  • Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego str. 12/14, 61-704 Poznan, Poland
Bibliografia
  • [1] L. Arnold, A. Henry, F. Poron, Y. Baba-Amer, N. van Rooijen, A. Plonquet, R.K. Gherardi and B. Chazaud: Inflammatory monocytes recruited after skeletal muscle injury switch into antiinflammatory macrophages to support myogenesis. J. of Experimental Medicine, 204 (2007), 1057-1069.
  • [2] T. Calinski and J. Harabasz: A dendrite method for cluster analysis. Communications in Statistics, 3 (1974), 1-27.
  • [3] G. Chinetti-Gbaguidi, S. Colin and B. Stael: Macrophage subsets in atherosclerosis. Nature Reviews Cardiology, 12 (2015), 10-17.
  • [4] J. M. Colom and M. Silva: Convex geometry and semiflows in P/T nets: a comparative study of algorithms for computation of minimal P-semiflows. Lecture Notes in Computer Science, 483 (1991), 79-112.
  • [5] J. Einloft, J. Ackermann, J. Nöthen and I. Koch: MonaLisa-visualization and analysis of functional modules in biochemical networks. Bioinformatics, 29 (2013), 1469-1470.
  • [6] P. J. Delves, S. J. Martin, D. R. Burton and I. M. Roitt: Roitt’s Essential Immunology. 13th Edition. Wiley-Blackwell, 2017.
  • [7] D. Formanowicz, A. Kozak, T. Głowacki, M. Radom and P. Formanowicz: Hemojuvelin-hepcidin axis modeled and analyzed using Petri nets. J. of Biomedical Informatics, 46 (2013), 1030-1043.
  • [8] D. Formanowicz, A. Sackmann, A. Kozak, J. Błaż ewicz and P. Formanowicz: Some aspects of the anemia of chronic disorders modeled and analyzed by petri net based approach. Bioprocess and Biosystems Engineering, 34 (2011), 581-595.
  • [9] M. Heiner, M. Schwarick and J. Wegener: Charlie – an extensible Petri net analysis tool. In Proc. PETRI NETS, Brussels, Springer, LNCS, 9115 (2015), 200-211.
  • [10] R. Hofestädt: A Petri net application of metabolic processes. J. of System Analysis, Modelling and Simulation, 16 (1994), 113-122.
  • [11] INA Home page. Available at http://www2.informatik.hu-berlin.de/starke/ina.html.
  • [12] P. Italiani and D. Boraschi: From monocytes to M1/M2 macrophages:phenotypical vs. functional differentatnion. Frontiers in Immunology, 5 (2014), 514.
  • [13] P. Italiani, E.. Mazza, D. Lucchesi, I. Cifola, C. Gemelli, A. Grande, C. Battaglia, S. Bicciato and D. Boraschi: Transcriptomic profiling of the development of the inflammatory response in human monocytes in vitro. PLoS One, 9 (2014), e87680.
  • [14] L. Kaufman and P. J. Rousseeuw: Finding groups in data: an introduction to cluster analysis. John Wiley and Sons, New York, 1990.
  • [15] I. Koch, W. Reisig and F. Schreiber (Ed.): Modeling in Systems Biology. The Petri Net Approach. Springer, London, 2011.
  • [16] K. Lautenbach: Exakte Bedingungen der Lebendigkeit für eine Klasse von Petri-Netzen. Number 82 in Berichte der GMD. Gesellschaft für Mathematik und Datenverarbeitung, Sankt Augustin, 1973.
  • [17] N. Leitinger and I.G. Schulman: Phenotypic polarization of macrophages in atherosclerosis. Atherosclerosis, Thrombosis and Vascular Biology, 13 (2013), 1120-1126.
  • [18] A. Mantovani, A. Sica, S. Sozzani, P. Allavena, A. Vecchi and M. Locati: The chemokine system in diverse forms of macrophage activation and polarization. Trends in Immunology, 25 (2004), 677-686.
  • [19] F. O. Martinez and S. Gordon: The M1 and M2 paradigm of macrophage activation: time for reassessment. F1000 Prime Reports, 6 (2014), 13.
  • [20] C. D. Mills and K. Ley: M1 and M2 Macrophages:The Chicken and the Egg of Immunity. J. of Innate Immunology, 6 (2014), 716-726.
  • [21] T. Murata: Petri nets: Properties, analysis and applications. Proc. of the IEEE, 77 (1989), 541-580.
  • [22] K. J. Mylonas, M. G. Nair, L. Prieto-Lafuente, D. Paape and J. E. Allen: Alternatively activated macrophages elicited by helminth infection can be reprogrammed to enable microbial killing. J. of Immunology, 182 (2009), 3084-3094.
  • [23] C. A. Petri: Communication with automata. Schriften des Instituts fur Instrumentelle Mathematik, Bonn, (1962), (in German).
  • [24] V. A. Reddy, M. L. Mavrovouniotis and M. N. Liebman: Petri net representation in metabolic pathways. Proc. of the 1st Int. Conf. on Intelligent Systems for Molecular Biology, AAAI Press, Menlo Park, (1993), 328-336.
  • [25] K. Rżosińska, D. Formanowicz and P. Formanowicz: Ilościowy model procesu różnicowania makrofagów oparty na sieciach Petriego. In: A. Świerniak, J. Krystek (Eds.): Automatyzacja procesów dyskretnych. Teoria i zastosowania, Vol. II, 205-216, Wydawnictwo Pracowni Komputerowej Jacka Skalmierskiego, Gliwice, 2016, (in Polish).
  • [26] A. Sackmann, M. Heiner and I. Koch: Application of Petri net based analysis techniques to signal transduction pathways. BMC Bioinformatics, 7 (2006), 482.
  • [27] R. D. Stout, C. Jiang, B. Matta, I. Tietzel, S.K. Watkins and J. Suttles: Macrophages sequentially change their functional phenotypes in response to changes in microenvironmental influences. J. of Immunology, 175 (2005), 342-349.
  • [28] C. Varol, A. Mildner and S. Jung: Macrophages: Development and Tissue Specialization. Annual Review of Immunology, 33 (2015), 643-675.
  • [29] J. Yang, L. Zhang, C. Yu, X-F Yang and H. Wang: Monocyte and macrophage differentiation: circulation inflammatory monocyte as biomarker for inflammatory disease. Biomarkers Research 2 (2014), 1.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d0667d5c-c049-4fa7-a5d4-af3491064b16
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