Reducing Boolean Networks with Backward Boolean Equivalence

Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium large due the state-space explosion problem. We propose a novel reduction technique called Backward Equivalence (BBE), which preserves some properties interest BNs. In particular, reduced provide compact representation by grouping variables that, if initialized equally, always updated equally. resulting state space is subset original one, restricted identical initialization grouped variables. corresponding trajectories BN can exactly restored. show effectiveness BBE performing large-scale validation on whole GINsim repository. selected cases, we how our method enables analyses that would otherwise intractable. Our complements, and combined with, other methods found in literature.