@article{OstrowskiPauleveSchaubetal.2016,
  author    = {Ostrowski, Max and Pauleve, L. and Schaub, Torsten H. and Siegel, A. and Guziolowski, Carito},
  title     = {Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming},
  series = {Biosystems : journal of biological and information processing sciences},
  volume    = {149},
  journal   = {Biosystems : journal of biological and information processing sciences},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0303-2647},
  doi       = {10.1016/j.biosystems.2016.07.009},
  pages     = {139 -- 153},
  year      = {2016},
  abstract  = {Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic 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 end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78\% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7 min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. (C) 2016 Elsevier Ireland Ltd. All rights reserved.},
  language  = {en}
}