Computational Design of Informative Experiments in Systems Biology

  • Alberto Giovanni Busetto
  • Mikael Sunnåker
  • Joachim M. Buhmann


Accurate predictions of the behavior of biological systems can be achieved through multiple iterations of modeling and experimentation. In this chapter, we present the central ideas for the design of informative experiments in systems biology. We start by formalizing the task, and proceed by introducing the required tools to process data subject to uncertainty. We analyze design approaches which are Bayesian and information-theoretic in nature. A particular emphasis is placed on implicit and explicit assumptions of the available techniques. Two main design goals are here compared: reducing uncertainty and challenging existing belief. Finally, we discuss the limitations of the presented approaches to provide general guidelines for predictive modeling.


Hypothesis DNA-damage Causality Deterministic Activator-inhibitor Goldbeter-Koshland function Macromolecule Phosphorylation Aleatory variability 



We thank Sotiris Dimopoulos, Jörg Stelling, Cheng Soon Ong, Simonetta Scola, Gabriel Krummenacher, Alain Hauser, Elias Zamora-Sillero, Kay H. Brodersen, Jean Daunizeau, Andreas Krause and Marcus Hutter for insightful discussions and helpful comments. This work was financed with grants from YeastX and LiverX through the Swiss initiative, evaluated by the Swiss National Science Foundation.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Alberto Giovanni Busetto
    • 1
    • 3
  • Mikael Sunnåker
    • 2
    • 3
    • 4
  • Joachim M. Buhmann
    • 1
    • 3
  1. 1.Department of Computer ScienceETH ZurichZurichSwitzerland
  2. 2.Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
  3. 3.Competence Center for Systems Physiology and Metabolic DiseasesZurichSwitzerland
  4. 4.Swiss Institute of BioinformaticsZurichSwitzerland

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