Abstraction-Based Parameter Synthesis for Multiaffine Systems

  • Sergiy BogomolovEmail author
  • Christian Schilling
  • Ezio Bartocci
  • Gregory Batt
  • Hui Kong
  • Radu Grosu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9434)


Multiaffine hybrid automata (MHA) represent a powerful formalism to model complex dynamical systems. This formalism is particularly suited for the representation of biological systems which often exhibit highly non-linear behavior. In this paper, we consider the problem of parameter identification for MHA. We present an abstraction of MHA based on linear hybrid automata, which can be analyzed by the SpaceEx model checker. This abstraction enables a precise handling of time-dependent properties. We demonstrate the potential of our approach on a model of a genetic regulatory network and a myocyte model.


Parameter Domain Valid Parameter Kripke Structure Hybrid Automaton Genetic Regulatory Network 
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.



This work was partly supported by the European Research Council (ERC) under grant 267989 (QUAREM), by the Austrian Science Fund (FWF) under grants S11402-N23, S11405-N23 and S11412-N23 (RiSE/SHiNE) and Z211-N23 (Wittgenstein Award), and by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS,


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sergiy Bogomolov
    • 1
    Email author
  • Christian Schilling
    • 2
  • Ezio Bartocci
    • 3
  • Gregory Batt
    • 4
  • Hui Kong
    • 1
  • Radu Grosu
    • 3
  1. 1.IST AustriaKlosterneuburgAustria
  2. 2.University of FreiburgFreiburg im BreisgauGermany
  3. 3.Vienna University of TechnologyViennaAustria
  4. 4.INRIA Paris-RocquencourtParisFrance

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