Multiscale Modeling: Concepts, Technologies, and Use Cases in Immunology
作者： Vida Abedi , Raquel Hontecillas , Adria Carbo , Casandra Philipson , Stefan Hoops
DOI: 10.1016/B978-0-12-803697-6.00008-4
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摘要: Abstract In today’s Big Data era, computing tools and techniques help revolutionize immunological research in a meaningful way that is also shifting paradigm from top-down or bottom-up approaches to middle-out approach. The latter based on conceptualizing models at the level provides richest data connecting higher lower levels for comprehensive integrated systems. essence, building large multiscale are an emerging trend mainly because these can be useful providing great opportunities model-driven knowledge discoveries hypothesis generation clinically pharmacologically relevant. this chapter, we will highlight technological advances drive cutting-edge immune-informatics practices systems immunology cycle. We present our next-generation modeling (MSM) technology, ENteric Immune Simulator (ENISI) MSM, tool integrates ordinary differential equation, partial agent-based technologies four spatiotemporal scales. ENISI MSM designed mucosal immune responses simulate 10 8 –10 9 cells high-performance simulations.
sci-hub.se 参考文章(68)
Heather N. Hayenga, Bryan C. Thorne, Phillip Yen, Jason A. Papin, Shayn M. Peirce, Jay D. Humphrey, Multiscale Computational Modeling in Vascular Biology: From Molecular Mechanisms to Tissue-Level Structure and Function Springer, Berlin, Heidelberg. pp. 209- 240 ,(2013) , 10.1007/8415_2012_147
Pedro Mendes, Stefan Hoops, Sven Sahle, Ralph Gauges, Joseph Dada, Ursula Kummer, Computational modeling of biochemical networks using COPASI. Methods of Molecular Biology. ,vol. 500, pp. 17- 59 ,(2009) , 10.1007/978-1-59745-525-1_2
T. Sumner, Sensitivity analysis in systems biology modelling and its application to a multi-scale model of blood glucose homeostasis Doctoral thesis, UCL (University College London).. ,(2010)
James Mata, Melvin Cohn, Cellular automata-based modeling program: synthetic immune system Immunological Reviews. ,vol. 216, pp. 198- 212 ,(2007) , 10.1111/J.1600-065X.2007.00511.X
J. C. Helton, Uncertainty and Sensitivity Analysis for Models of Complex Systems Springer, Berlin, Heidelberg. pp. 207- 228 ,(2008) , 10.1007/978-3-540-77362-7_9
Yongguo Mei, Vida Abedi, Adria Carbo, Xiaoying Zhang, Pinyi Lu, Casandra Philipson, Raquel Hontecillas, Stefan Hoops, Nathan Liles, Josep Bassaganya-Riera, Multiscale modeling of mucosal immune responses BMC Bioinformatics. ,vol. 16, pp. 1- 14 ,(2015) , 10.1186/1471-2105-16-S12-S2
Eran Gery, David Harel, Eldad Palachi, Rhapsody: A Complete Life-Cycle Model-Based Development System integrated formal methods. pp. 1- 10 ,(2002) , 10.1007/3-540-47884-1_1
Virginia A Folcik, Gary C An, Charles G Orosz, The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity Theoretical Biology and Medical Modelling. ,vol. 4, pp. 39- 39 ,(2007) , 10.1186/1742-4682-4-39
A. Buganza Tepole, E. Kuhl, Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing. Computer Methods in Biomechanics and Biomedical Engineering. ,vol. 19, pp. 13- 30 ,(2016) , 10.1080/10255842.2014.980821
R. Puzone, B. Kohler, P. Seiden, F. Celada, IMMSIM, a flexible model for in machina experiments on immune system responses Future Generation Computer Systems. ,vol. 18, pp. 961- 972 ,(2002) , 10.1016/S0167-739X(02)00075-4