Data-driven reconstruction of directed networks

Regular Article

Abstract

We investigate the properties of a recently introduced asymmetric association measure, called inner composition alignment (IOTA), aimed at inferring regulatory links (couplings). We show that the measure can be used to determine the direction of coupling, detect superfluous links, and to account for autoregulation. In addition, the measure can be extended to infer the type of regulation (positive or negative). The capabilities of IOTA to correctly infer couplings together with their directionality are compared against Kendall’s rank correlation for time series of different lengths, particularly focussing on biological examples. We demonstrate that an extended version of the measure, bidirectional inner composition alignment (biIOTA), increases the accuracy of the network reconstruction for short time series. Finally, we discuss the applicability of the measure to infer couplings in chaotic systems.

Keywords

Statistical and Nonlinear Physics 

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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sabrina Hempel
    • 1
    • 2
    • 3
  • Aneta Koseska
    • 2
    • 3
    • 4
  • Zoran Nikoloski
    • 5
    • 6
  1. 1.Potsdam Institute for Climate Impact Research (PIK)PotsdamGermany
  2. 2.Department of PhysicsHumboldt University of BerlinBerlinGermany
  3. 3.Interdisciplinary Center for Dynamics of Complex Systems, University of PotsdamPotsdamGermany
  4. 4.Max Planck Institute for Molecular PhysiologyDortmundGermany
  5. 5.Systems Biology and Mathematical Modeling Group, Max Planck Institute for Molecular Plant PhysiologyPotsdamGermany
  6. 6.Institute of Biochemistry and Biology, University of PotsdamPotsdamGermany

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