Reducing Boolean Networks with Backward Boolean Equivalence

Georgios Argyris, Alberto Lluch Lafuente, Mirco Tribastone, Max Tschaikowski, Andrea Vandin

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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 \emph{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.
Original languageEnglish
Title of host publicationComputational Methods in Systems Biology
Number of pages19
Publication date2021
ISBN (Print)9783030856328
Publication statusPublished - 2021
Event19th International Conference on Computational Methods in Systems Biology
- Bordeaux, France
Duration: 22 Sept 202124 Sept 2021


Conference19th International Conference on Computational Methods in Systems Biology
SeriesLecture Notes in Computer Science


  • Boolean Network
  • State Transition Graph
  • Attractor analysis
  • Exact Reduction
  • GinSim Repository


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