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  -