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Boolean Network Identification from Multiplex Time Series Data

  • Max Ostrowski
  • Loïc Paulevé
  • Torsten Schaub
  • Anne Siegel
  • Carito Guziolowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9308)

Abstract

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logical models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that goal, we exhibit a necessary condition that must be satisfied by a Boolean network dynamics to be consistent with a discretized time series trace. Based on this condition, we use a declarative programming approach (Answer Set Programming) to compute an over-approximation of the set of Boolean networks which fit best with experimental data. Combined with model-checking approaches, we end up with a global learning algorithm and compare it to learning approaches based on static data.

Keywords

Time Series Time Series Data Boolean Network Interaction Graph Time Series Dataset 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Max Ostrowski
    • 1
  • Loïc Paulevé
    • 2
  • Torsten Schaub
    • 1
  • Anne Siegel
    • 3
  • Carito Guziolowski
    • 4
  1. 1.Computer Science DepartmentPotsdam UniversityPostdamGermany
  2. 2.CNRSUniversité Paris-Sud LRI-UMR 8623OrsayFrance
  3. 3.CNRSUniversité de Rennes 1, IRISA-UMR 6074RennesFrance
  4. 4.École Centrale de NantesIRCCyN-UMR CNRS 6597NantesFrance

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