TY  - GEN
A1  - Razzaq, Misbah
A1  - Kaminski, Roland
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Bourdon, Jeremie
A1  - Guziolowski, Carito
T1  - Computing diverse boolean networks from phosphoproteomic time series data
T2  - Computational Methods in Systems Biology
N2  - Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.
KW  - Diverse solution enumeration
KW  - Answer set programming
KW  - Boolean Networks
KW  - Model checking
KW  - Time series data
Y1  - 2018
SN  - 978-3-319-99429-1
SN  - 978-3-319-99428-4
U6  - https://doi.org/10.1007/978-3-319-99429-1_4
SN  - 0302-9743
SN  - 1611-3349
VL  - 11095
SP  - 59
EP  - 74
PB  - Springer
CY  - Berlin
ER  - 
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  -