Global Optimization in Systems Biology: Stochastic Methods and Their Applications

  • Eva Balsa-CantoEmail author
  • J. R. Banga
  • J. A. Egea
  • A. Fernandez-Villaverde
  • G. M. de Hijas-Liste
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for model development, (2) in model identification, or (3) in the computation of optimal stimulation procedures to synthetically achieve a desired biological behavior. These problems are usually formulated as nonlinear programing problems (NLPs) with dynamic and algebraic constraints. However the nonlinear and highly constrained nature of systems biology models, together with the usually large number of decision variables, can make their solution a daunting task, therefore calling for efficient and robust optimization techniques. Here, we present novel global optimization methods and software tools such as cooperative enhanced scatter search (eSS), AMIGO, or DOTcvpSB, and illustrate their possibilities in the context of modeling including model identification and stimulation design in systems biology.



This work was supported by the Spanish MICINN project “MultiSysBio” (ref. DPI2008-06880-C03-02), and by CSIC intramural project “BioREDES” (ref. PIE-201170E018).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Eva Balsa-Canto
    • 1
    Email author
  • J. R. Banga
    • 1
  • J. A. Egea
    • 2
  • A. Fernandez-Villaverde
    • 1
  • G. M. de Hijas-Liste
    • 1
  1. 1.(Bio)Process Engineering Group, IIM-CSICVigoSpain
  2. 2.Department of Applied Mathematics and StatisticsTechnical University of Cartagena (UPCT)CartagenaSpain

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