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Modeling and Simulation of Living Systems as Systems of Systems

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Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

Abstract

In this chapter we show that the system theoretic basis of the DEVS formalism matches the systemic point of view adopted in the living sciences field. Two examples, one in animal epidemiology and the other in plant growth modeling, illustrate different characteristics of DEVS and its extensions. We show how these multi-formalistic abilities of DEVS Modeling Environments are very promising to help answer critical issues regarding natural risk management and poverty reduction. We show how DEVS can serve as a universal formalism for living dynamical systems modeling and simulation. However, DEVS is an abstract formalism and can be hard to manage when the modeling effort has to focus on the application domain. So the Virtual Laboratory Environment (VLE) provides an environment where DEVS is used at the simulation level but where the modeling level is composed by a set of specialized modeling components, where components are represented by appropriate formalisms. In this way, the modeler can design the model using the most suitable formalism, or coupling several ones, without any knowledge of DEVS. VLE supplies the mappings into DEVS of the main formalisms used in living systems modeling and simulation. We show how the tackling of urgent issues such as the economical and ecological crises, diversity erosion or poverty, will be based on creative compositions of shared representations among multiple discipline-based experts. These shared models will embed heterogeneous knowledge elements at different scales, i.e., in heterogeneous formalisms. For these reasons, simulation models for living systems, viewed as systems of systems, are best formalized with DEVS and its extensions.

Contributed by Raphaël Duboz and Jean-Christophe Soulié.

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Notes

  1. 1.

    See http://www.vle-project.org for last versions.

References

  • Aumann, G. A. (2007). A methodology for developing simulation models of complex systems. Ecological Modelling, 202, 385–396.

    Article  Google Scholar 

  • Barros, F. J. (1996). Dynamic structure discrete event system specification: formalism, abstract simulators and applications. Transaction of the Society for Computer Simulation International, 13(1), 35–46.

    Google Scholar 

  • Barros, F. J. (1997). Modeling formalisms for dynamic structure systems. ACM Transactions on Modeling and Computer Simulation, 7, 501–515.

    Article  MATH  Google Scholar 

  • Bergez, J.-E., Chabrier, P., Gary, C., Jeuffroy, M. H., Makowski, D., Quesnel, G., Ramat, E., Raynal, H., Rousse, N., Wallach, D., Debaeke, P., Durand, P., Duru, M., Dury, J., Faverdin, P., Gascuel-Odoux, C., & Garcia, F. (2012, in press). An open platform to build, evaluate and simulate integrated models of farming and agroecosystems. Environmental Modelling & Software.

    Google Scholar 

  • Bonte, B., Duboz, R., Quesnel, G., & Müller, J. P. (2009). Recursive simulation and experimental frame for multiscale simulation. In Proceedings of the summer computer simulation conference, Istambul, Turkey (pp. 164–172).

    Google Scholar 

  • Bonté, B., Duboz, R., & Muller, J. P. (2012). Modeling the Minsky triad: a framework to perform reflexive M&S studies. Accepted for publication in the proceedings of the Winter Simulation Conference (ACM/IEEE), Berlin, Germany, December 9–12.

    Google Scholar 

  • Diekmann, O., & Heesterbeek, J. A. P. (2000). In S. Levin (Ed.), Wiley series in mathematical and computational biology. Mathematical epidemiology of infectious diseases: model building, analysis and interpretation (p. 303). Princeton: Princeton University Press.

    Google Scholar 

  • Dingkuhn, M., Luquet, D., Quilot, B., & Reffye, P. D. (2005). Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity. Australian Journal of Agricultural Research, 56, 1–14.

    Article  Google Scholar 

  • Dingkuhn, M., Luquet, D., Kim Tambour, L., & Clément-Vidal, A. (2006). EcoMeristem, a model of morphogenesis and competition among sinks in rice: 2. Simulating genotype responses to phosphorus deficiency. Functional plant biology, 33(4), 325–337.

    Article  Google Scholar 

  • Duboz, R., Ramat, É., & Preux, P. (2003). Scale transfer modelling: using emergent computation for coupling an ordinary differential equation system with a reactive agent model. Systems Analysis, Modelling, Simulation, 43(6), 793–814.

    Article  MATH  Google Scholar 

  • Duboz, R., Versmisse, D., Quesnel, G., Muzzy, A., & Ramat, É. (2006). Specification of dynamic structure discrete event multiagent systems. In The conference proceedings of agent directed Simulation (spring simulation multiconference), Huntsville, Alabama, USA, April 2–6.

    Google Scholar 

  • Duboz, R., Versmisse, D., Travers, M., Ramat, E., & Shin, Y. J. (2010). Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model. Ecological Modelling, 221(5), 840–849.

    Article  Google Scholar 

  • Duboz, R., Bonté, B., & Quesnel, G. (2012). Vers une spécification des modèles de simulation de systèmes complexes. Studia Informatica Universalis 10, 7–37.

    Google Scholar 

  • Dufour, B., & Hendrikx, P. (2009). Epidemiological surveillance in animal health (2nd ed.). FAO, 386 p.

    Google Scholar 

  • Giese, M., Brueck, H., Dingkuhn, M., Kiepe, P., & Asch, F. (2009). Developing rice and sorghum crop adaptation strategies for climate change in vulnerable environments in Africa RISOCAS. In Proceedings of tropentag 2009, Hamburg. University of Hamburg.

    Google Scholar 

  • Gutjahr, S., Clément-Vidal, A., Trouche, G., Vaksmann, M., Thera, K., Sonderegger, N., Dingkuhn, M., & Luquet, D. (2010). Functional analysis of sugar accumulation in sorghum stems and its competition with grain filling among contrasted genotype. In Proceedings of Agro2010, Montpellier, France.

    Google Scholar 

  • Hwang, M. XSY#: C# implementation of system theory. http://sourceforge.net/mailarchive/forum.php?forum_name=xsy-csharp-devssharp.

  • Hammer, G. L., Chapman, S., Van Oosterom, E., & Poldich, D. W. (2005). Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Australian Journal of Agricultural Research, 56, 947–960.

    Article  Google Scholar 

  • Summary for Policymakers. IPCC (2007). Cambridge, United Kingdom and New York, NY, USA.

    Google Scholar 

  • Luquet, D., Dingkuhn, M., Kim Tambour, L., & Clément-Vidal, A. (2006). EcoMeristem, a model of morphogenesis and competition among sinks in rice: 1. Concept, validation and sensitivity analysis. Functional Plant Biology, 33(4), 309–323.

    Article  Google Scholar 

  • Luquet, D., Rebolledo, M.-C., & Soulié, J.-C. (2012). Functional-structural plant modeling to support complex trait phenotyping: case of rice early vigour and drought tolerance using Ecomeristem model. In M. Kang, Y. Dumont, & Y. Guo (Eds.), Proceedings of the 4th international symposium on plant growth modeling, simulation, visualization and applications, 31st Oct.–3rd 2012, Shangai, China (pp. 270–277). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Nicotra, A. B., Atkin, O. K., Bonser, S. P., Davidson, A. M., Finnegan, E. J., Mathesius, U., Poot, P., Purugganan, M. D., Richards, C. L., Valladares, F., & an Kleunen, M. (2010). Plant phenotypic plasticity in a changing. Trends in Plant Science, 15, 684–692.

    Article  Google Scholar 

  • Pacheco, X (2001). Delphi 6 Developer’s Guide. SAMS. 1200 p.

    Google Scholar 

  • Pattee, H. H. (Ed.) (1973). Hierarchy theory, the challenge of complex systems. New York: Braziller.

    Google Scholar 

  • Piou, C., Berger, U., & Grimm, V. (2009). Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework. Ecological Modelling, 220(17), 1957–1967.

    Article  Google Scholar 

  • Quesnel, G., Duboz, R., & Ramat, E. (2009). The virtual laboratory environment—an operational framework for multi-modelling, simulation and analysis of complex dynamical systems. Simulation Modelling Practice and Theory, 17, 641–653.

    Article  Google Scholar 

  • R Core Team (2012). R: a language and environment for statistical computing. R foundation for statistical computing. Vienna, Austria. ISBN 3-900051-07-0. URL http://www.R-project.org/.

  • Saltelli, A., Chan, K., & Scott, M. (2000). Wiley series in probability and statistics. Sensitivity analysis. New York: Wiley.

    MATH  Google Scholar 

  • Soulie, J. C., Pradal, C., Fournier, C., & Luquet, D. (2010). Feedbacks between plant microclimate and morphogenesis in fluctuating environment: analysis for rice using Ecomeristem model coupled with 3D plant and energy balance computation tools in OpenAlea platform. In T. M. Dejong & D. Da Silva (Eds.), Proceedings of the 6th international workshop on functional-structural plant models, September 12–17 (pp. 138–140). Davis: University of California.

    Google Scholar 

  • Hu, X., & Zeigler, B. P. (2005). Model continuity in the design of dynamic distributed real-time systems. IEEE Transactions on Systems, Man and Cybernetics. Part A. Systems and Humans, 35(6), 867–878.

    Article  Google Scholar 

  • Zeigler, B. P. (1990). Object oriented simulation with hierarchical, modular models: intelligent agents and endomorphic agents. Orlando: Academic Press.

    MATH  Google Scholar 

  • Zeigler, B. P., Kim, D., & Praehofer, H. (2000). Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. San Diego: Academic Press.

    Google Scholar 

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Zeigler, B.P., Sarjoughian, H.S. (2013). Modeling and Simulation of Living Systems as Systems of Systems. In: Guide to Modeling and Simulation of Systems of Systems. Simulation Foundations, Methods and Applications. Springer, London. https://doi.org/10.1007/978-0-85729-865-2_17

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  • DOI: https://doi.org/10.1007/978-0-85729-865-2_17

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-864-5

  • Online ISBN: 978-0-85729-865-2

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