Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming. (November 2016)

• Record Type: Journal Article
• Authors: Ostrowski, M.; Paulevé, L.; Schaub, T.; Siegel, A.; Guziolowski, C.
• Is Part Of: Bio systems. Volume 149(2016:Nov.)
• Page Start: 139
• Page End: 153
• Publication Date: 2016-11
• Subjects: Model identification -- Time series data -- Multiplex phosphoproteomics data -- Boolean networks -- Answer Set Programming; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2016.07.009
• Languages: English
• ISSNs: 0303-2647

Abstract

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.