Cyclic Attractor Detection in Boolean Network Dynamics under Local Logical Constraints
2026-06-29 • Computational Complexity
Computational ComplexityDiscrete Mathematics
AI summaryⓘ
The authors study how hard it is to find repeating patterns (called cyclic attractors) in systems known as Boolean networks, which are used in biology and computing. They focus on networks where the update rules are built from specific types of logical functions and want to know if a cycle of a particular length exists. They find that for some classes of rules, this problem is easy (can be solved quickly), while for others it is hard (NP-complete). Their results help clarify which logical rules make the problem tractable and which do not.
Boolean networkscyclic attractorsNP-completenessPost's latticeBoolean functionsparallel updateself-dual rulesmonotone functionsaffine functions
Authors
Alexander Drobyshev, Grigoriy Bokov
Abstract
Boolean networks are finite discrete nonlinear systems whose long-term behaviour is organised by fixed-point and cyclic attractors. Detecting such recurrent states is important in applications ranging from gene regulation and neural computation to complex-network models, but the computational boundary between tractable and intractable attractor analysis is still not fully understood. We study that boundary from the perspective of local logical rules. We consider Boolean networks under parallel update whose coordinate functions are given by circuits over a fixed finite basis of a closed Boolean-function class, and ask whether the network has a cyclic attractor of prescribed exact period $k$. For every fixed $k\ge 2$, we obtain a complete complexity dichotomy over Post's lattice. The problem is $\mathrm{NP}$-complete whenever the local rule class contains majority-like self-dual rules or one of the two mixed conjunctive-disjunctive monotone families. In all remaining Post classes it is polynomial-time solvable, with affine rules and pure conjunctive or pure disjunctive rules with constants providing the boundary tractable cases. The results show that exact attractor detection is governed not only by the network architecture but also by the logical mechanism of local update: affine and one-sided rules preserve algebraic or order structure, whereas majority-like and mixed monotone rules can encode global Boolean consistency constraints.