TY - GEN
T1 - Reducing Boolean Networks with Backward Boolean Equivalence
AU - Argyris, Georgios
AU - Lluch Lafuente, Alberto
AU - Tribastone, Mirco
AU - Tschaikowski, Max
AU - Vandin, Andrea
PY - 2021
Y1 - 2021
N2 - Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called Backward Boolean Equivalence (BBE), which preserves some properties of interest of BNs. In particular, reduced BNs provide a compact representation by grouping variables that, if initialized equally, are always updated equally. The resulting reduced state space is a subset of the original one, restricted to identical initialization of grouped variables. The corresponding trajectories of the original BN can be exactly restored. We show the effectiveness of BBE by performing a large-scale validation on the whole GINsim BN repository. In selected cases, we show how our method enables analyses that would be otherwise intractable. Our method complements, and can be combined with, other reduction methods found in the literature.
AB - Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called Backward Boolean Equivalence (BBE), which preserves some properties of interest of BNs. In particular, reduced BNs provide a compact representation by grouping variables that, if initialized equally, are always updated equally. The resulting reduced state space is a subset of the original one, restricted to identical initialization of grouped variables. The corresponding trajectories of the original BN can be exactly restored. We show the effectiveness of BBE by performing a large-scale validation on the whole GINsim BN repository. In selected cases, we show how our method enables analyses that would be otherwise intractable. Our method complements, and can be combined with, other reduction methods found in the literature.
U2 - 10.1007/978-3-030-85633-5_1
DO - 10.1007/978-3-030-85633-5_1
M3 - Article in proceeding
SN - 978-3-030-85632-8
T3 - Lecture Notes in Computer Science
SP - 1
EP - 18
BT - Computational Methods in Systems Biology
PB - Springer
T2 - International Conference on Computational Methods in Systems Biology - CMSB 2021
Y2 - 22 September 2021 through 24 September 2021
ER -