Boolean Network Identification from Multiplex Time Series Data

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 scal-able 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.
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Contributor : Loïc Paulevé <>
Submitted on : Wednesday, June 17, 2015 - 5:02:38 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:04 PM
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Max Ostrowski, Loïc Paulevé, Torsten Schaub, Anne Siegel, Carito Guziolowski. Boolean Network Identification from Multiplex Time Series Data. CMSB 2015 - 13th conference on Computational Methods for Systems Biology, Sep 2015, Nantes, France. pp.170-181, ⟨10.1007/978-3-319-23401-4_15⟩. ⟨hal-01164751⟩



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