Boolean Network Identification from Multiplex Time Series Data

Max Ostrowski; Loïc Paulevé; Torsten Schaub; Anne Siegel; Carito Guziolowski

doi:10.1007/978-3-319-23401-4_15

Boolean Network Identification from Multiplex Time Series Data

Max Ostrowski ¹ Loïc Paulevé ² Torsten Schaub ¹ Anne Siegel ³ Carito Guziolowski ⁴

1 Institut für informatik [Potsdam]

2 LRI - Laboratoire de Recherche en Informatique

3 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences

Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE

4 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

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.

Document type :

Conference papers

Domain :

Computer Science [cs] / Bioinformatics [q-bio.QM]

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https://hal.archives-ouvertes.fr/hal-01164751
Contributor : Loïc Paulevé <>
Submitted on : Wednesday, June 17, 2015 - 5:02:38 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:04 PM
Document(s) archivé(s) le : Tuesday, April 25, 2017 - 11:11:23 AM

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