Modeling and Simulation of Living Systems as Systems of Systems

  • Bernard P. Zeigler
  • Hessam S. Sarjoughian
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


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.


Living System Main Stem Agent Base Simulation Virtual Experiment Epidemiological Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Bernard P. Zeigler
    • 1
  • Hessam S. Sarjoughian
    • 2
  1. 1.Chief ScientistRTSync Corp.RockvilleUSA
  2. 2.Computer Science & Engineering FacultyArizona State UniversityTempeUSA

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