TY  - JOUR
A1  - Ostrowski, Max
A1  - Pauleve, L.
A1  - Schaub, Torsten H.
A1  - Siegel, A.
A1  - Guziolowski, Carito
T1  - Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming
JF  - Biosystems : journal of biological and information processing sciences
N2  - 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.
KW  - Model identification
KW  - Time series data
KW  - Multiplex phosphoproteomics data
KW  - Boolean networks
KW  - Answer Set Programming
Y1  - 2016
U6  - https://doi.org/10.1016/j.biosystems.2016.07.009
SN  - 0303-2647
SN  - 1872-8324
VL  - 149
SP  - 139
EP  - 153
PB  - Elsevier
CY  - Oxford
ER  -