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arxiv: 2606.30270 · v1 · pith:F4IMBU2Nnew · submitted 2026-06-29 · 💻 cs.CC · cs.DM· math.DS

Cyclic Attractor Detection in Boolean Network Dynamics under Local Logical Constraints

Pith reviewed 2026-06-30 03:16 UTC · model grok-4.3

classification 💻 cs.CC cs.DMmath.DS
keywords Boolean networksattractor detectionPost's latticecomputational complexitycyclic attractorsdichotomy theoremNP-completeness
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The pith

The problem of detecting a cyclic attractor of exact period k in a Boolean network is NP-complete or polynomial-time solvable depending on which closed class of local rules from Post's lattice is used.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper maps the computational complexity of finding whether a Boolean network has a cycle of exact length k under parallel updates. The local rules are circuits from a fixed closed class of Boolean functions, classified by Post's lattice. For each fixed k at least 2, the problem is NP-complete if the class includes majority-like self-dual rules or mixed conjunctive-disjunctive monotone families. It is solvable in polynomial time for the other classes, including affine rules and pure conjunctive or disjunctive rules with constants. The boundary arises because certain rule types preserve structure that allows efficient search while others can express hard global constraints.

Core claim

For every fixed k ≥ 2, the exact-k-cyclic-attractor problem over Boolean networks is NP-complete whenever the local rule class contains majority-like self-dual rules or one of the two mixed conjunctive-disjunctive monotone families, and it is polynomial-time solvable in all remaining Post classes, with affine rules and pure conjunctive or pure disjunctive rules with constants providing the boundary tractable cases.

What carries the argument

The classification of closed Boolean function classes given by Post's lattice, which determines whether the local rules preserve enough algebraic or ordering structure to make attractor search tractable.

If this is right

  • Networks whose update rules are affine admit polynomial-time detection of period-k cycles because the preserved linear structure allows efficient solving.
  • Rule classes with self-dual majority functions allow reduction of hard consistency problems to the attractor detection task.
  • Pure conjunctive rules with constants permit attractor detection by checking consistency in an order structure in polynomial time.
  • The dichotomy holds for every fixed period k at least 2.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The findings indicate that models of gene networks using majority voting rules may require different analysis techniques than those using linear or one-sided rules.
  • Similar complexity boundaries might appear in related problems such as finding fixed points rather than cycles.
  • Relaxing the assumption of synchronous updates could produce a different set of tractable rule classes.
  • Practical implementations could first check the rule class to decide which algorithm to apply.

Load-bearing premise

The coordinate functions are realized by circuits over a fixed finite basis of a closed Boolean-function class and the network evolves under parallel synchronous update.

What would settle it

An explicit Post class containing mixed conjunctive-disjunctive monotone rules together with a polynomial-time algorithm for the period-k problem in that class, or conversely an affine class in which the problem is NP-complete.

Figures

Figures reproduced from arXiv: 2606.30270 by Alexander Drobyshev, Grigoriy Bokov.

Figure 1
Figure 1. Figure 1: From local Boolean rules to the attractor landscape for [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Complexity regions in Theorem 7 for exact-period cyclic-attractor existence with fixed [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Layered construction of the D2-network Nψ used in Lemma 10. The literal vertices are fixed by self-loops. The checking vertices encode clause satisfaction and consistency by maj￾gates. The cycle layer is a circular shift register; δ1 receives δk, n copies of each Gj , and m copies of each Bi . These multiplicities make transmission through δ1 depend on the balance between clause-test and consistency-test o… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper claims a complete complexity dichotomy, for every fixed k≥2, for the problem of deciding whether a Boolean network (with parallel synchronous update) whose coordinate functions are circuits over a fixed finite basis of a closed class in Post's lattice has a cyclic attractor of exact period k. The problem is NP-complete precisely when the class contains majority-like self-dual rules or one of the two mixed conjunctive-disjunctive monotone families; it is polynomial-time solvable in all remaining classes, with affine rules and pure conjunctive or pure disjunctive rules with constants as the boundary tractable cases.

Significance. If the claimed dichotomy holds, the result is significant: it supplies a full classification of exact-period cyclic attractor detection over the standard algebraic structure of Post's lattice, showing that tractability is governed by whether the local rules preserve affine structure or one-sided order properties versus the ability to encode global Boolean consistency constraints via majority-like or mixed monotone rules. This refines the computational boundary for attractor analysis in applications such as gene regulation.

minor comments (1)
  1. Ensure that the main text explicitly enumerates the Post classes falling on each side of the dichotomy (rather than relying solely on the abstract's high-level description) so that readers can immediately verify the boundary cases without reconstructing the lattice.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive recommendation to accept and for the accurate summary of the complexity dichotomy over Post's lattice.

Circularity Check

0 steps flagged

No significant circularity; classification is external to the paper

full rationale

The paper establishes a complexity dichotomy for cyclic attractor detection by partitioning over the standard, externally defined Post's lattice of Boolean function classes. The tractable and NP-complete cases are identified by algebraic properties of those classes (affine, pure conjunctive/disjunctive with constants vs. majority-like self-dual or mixed monotone families), with no equations, reductions, or predictions that collapse back to fitted parameters or self-defined quantities. No self-citations are invoked as load-bearing uniqueness theorems, and the problem formulation (circuits over a fixed basis, parallel update, exact period k) introduces no internal self-reference that would make the claimed boundaries tautological. The result is therefore a standard complexity classification against an independent mathematical structure.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the standard classification of Boolean functions by Post's lattice and on the modeling choice of parallel update; no free parameters or invented entities are introduced.

axioms (2)
  • standard math Post's lattice enumerates all closed classes of Boolean functions under composition
    Invoked throughout the abstract as the organizing structure for the dichotomy
  • domain assumption Network evolution uses parallel (synchronous) update
    Explicitly stated as the update scheme under which the attractor question is posed

pith-pipeline@v0.9.1-grok · 5756 in / 1414 out tokens · 59376 ms · 2026-06-30T03:16:26.562748+00:00 · methodology

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Reference graph

Works this paper leans on

66 extracted references · 54 canonical work pages

  1. [1]

    Exploring Complex Networks

    Steven H. Strogatz. “Exploring Complex Networks”. In:Nature410.6825 (2001), pp. 268– 276.doi:10.1038/35065725

  2. [2]

    Complex Networks: Structure and Dynamics

    S. Boccaletti et al. “Complex Networks: Structure and Dynamics”. In:Physics Reports424 (2006), pp. 175–308.doi:10.1016/j.physrep.2005.10.009

  3. [3]

    Statistical Mechanics of Complex Networks

    Réka Albert and Albert-László Barabási. “Statistical Mechanics of Complex Networks”. In: Reviews of Modern Physics74.1 (2002), pp. 47–97.doi:10.1103/RevModPhys.74.47

  4. [4]

    SIAM Review 45(2), 167–256 (2003) https://doi.org/10.1137/S003614450342480 arXiv:cond- mat/0303516

    M. E. J. Newman. “The Structure and Function of Complex Networks”. In:SIAM Review 45.2 (2003), pp. 167–256.doi:10.1137/S003614450342480

  5. [5]

    A Logical Calculus of the Ideas Immanent in Nervous Activity

    Warren S. McCulloch and Walter Pitts. “A Logical Calculus of the Ideas Immanent in Nervous Activity”. In:The Bulletin of Mathematical Biophysics5.4 (1943), pp. 115–133. doi:10.1007/BF02478259

  6. [6]

    MetabolicStabilityandEpigenesisinRandomlyConstructedGenetic Nets

    StuartA.Kauffman.“MetabolicStabilityandEpigenesisinRandomlyConstructedGenetic Nets”. In:Journal of Theoretical Biology22.3 (1969), pp. 437–467.doi:10.1016/0022- 5193(69)90015-0

  7. [7]

    Boolean Formalization of Genetic Control Circuits

    René Thomas. “Boolean Formalization of Genetic Control Circuits”. In:Journal of Theo- retical Biology42.3 (1973), pp. 563–585.doi:10.1016/0022-5193(73)90247-6

  8. [8]

    The Logical Analysis of Continuous, Non-linear Biochemical Control Networks

    Leon Glass and Stuart A. Kauffman. “The Logical Analysis of Continuous, Non-linear Biochemical Control Networks”. In:Journal of Theoretical Biology39.1 (1973), pp. 103– 129.doi:10.1016/0022-5193(73)90208-7. 16

  9. [9]

    Neural Networks and Physical Systems with Emergent Collective Com- putational Abilities

    John J. Hopfield. “Neural Networks and Physical Systems with Emergent Collective Com- putational Abilities”. In:Proceedings of the National Academy of Sciences79.8 (1982), pp. 2554–2558.doi:10.1073/pnas.79.8.2554

  10. [10]

    Information Flows, Graphs and Their Guessing Numbers

    Søren Riis. “Information Flows, Graphs and Their Guessing Numbers”. In:The Electronic Journal of Combinatorics14.1 (2007), R44.doi:10.37236/962

  11. [11]

    Graph-Theoretical Constructions for Graph En- tropy and Network Coding Based Communications

    Maximilien Gadouleau and Søren Riis. “Graph-Theoretical Constructions for Graph En- tropy and Network Coding Based Communications”. In:IEEE Transactions on Information Theory57.10 (2011), pp. 6703–6717.doi:10.1109/TIT.2011.2155618

  12. [12]

    Reduction and Fixed Points of Boolean Networks and Linear Network Coding Solvability

    Maximilien Gadouleau, Adrien Richard, and Eric Fanchon. “Reduction and Fixed Points of Boolean Networks and Linear Network Coding Solvability”. In:IEEE Transactions on Information Theory62.5 (2016), pp. 2504–2519.doi:10.1109/TIT.2016.2544344

  13. [13]

    A General Model of Binary Opinions Updating

    Alexis Poindron. “A General Model of Binary Opinions Updating”. In:Mathematical Social Sciences109 (2021), pp. 52–76.doi:10.1016/j.mathsocsci.2020.10.004

  14. [14]

    Dynamics of Neural Networks over Undirected Graphs

    Eric Goles and Gonzalo A. Ruz. “Dynamics of Neural Networks over Undirected Graphs”. In:Neural Networks63 (2015), pp. 156–169.doi:10.1016/j.neunet.2014.10.013

  15. [15]

    Heteroclinic Cycles in Hopfield Networks

    Pascal Chossat and Maciej Krupa. “Heteroclinic Cycles in Hopfield Networks”. In:Journal of Nonlinear Science26.2 (2015), pp. 315–344.doi:10.1007/s00332-015-9276-3

  16. [16]

    Ceccherini-Silberstein and M

    Vandana M. Ladwani and V. Ramasubramanian. “Connectionist Temporal Sequence De- coding: M-ary Hopfield Neural-Network with Multi-limit Cycle Formulation”. In:Artificial Neural Networks and Machine Learning – ICANN 2023. Vol. 14258. Lecture Notes in Com- puter Science. Springer Nature Switzerland, 2023, pp. 255–268.doi:10.1007/978-3-031- 44192-9_21

  17. [17]

    Limit Cycles in Models of Circular Gene Networks Regulated by Negative Feedback Loops

    Vitaly A. Likhoshvai, Vladimir P. Golubyatnikov, and Tamara M. Khlebodarova. “Limit Cycles in Models of Circular Gene Networks Regulated by Negative Feedback Loops”. In: BMC Bioinformatics21.S11 (2020), p. 255.doi:10.1186/s12859-020-03598-z

  18. [18]

    Positive Feedback Loops and Multistationarity

    R. Thomas and J. Richelle. “Positive Feedback Loops and Multistationarity”. In:Discrete Applied Mathematics19.1–3 (1988), pp. 381–396.doi:10.1016/0166-218X(88)90026-1

  19. [19]

    Multistationarity, the Basis of Cell Differentiation and Memory. II. Logical Analysis of Regulatory Networks in Terms of Feedback Circuits

    R. Thomas and M. Kaufman. “Multistationarity, the Basis of Cell Differentiation and Memory. II. Logical Analysis of Regulatory Networks in Terms of Feedback Circuits”. In: Chaos: An Interdisciplinary Journal of Nonlinear Science11.1 (2001), pp. 180–195.doi: 10.1063/1.1349893

  20. [20]

    Negative Circuits and Sustained Oscillations in Asynchronous Automata Networks

    Adrien Richard. “Negative Circuits and Sustained Oscillations in Asynchronous Automata Networks”. In:Advances in Applied Mathematics44.4 (2010), pp. 378–392.doi:10.1016/ j.aam.2009.11.011

  21. [21]

    Positive and Negative Cycles in Boolean Networks

    Adrien Richard. “Positive and Negative Cycles in Boolean Networks”. In:Journal of The- oretical Biology463 (2019), pp. 67–76.doi:10.1016/j.jtbi.2018.11.028

  22. [22]

    Number of Fixed Points and Disjoint Cycles in Monotone Boolean Networks

    Julio Aracena, Adrien Richard, and Lilian Salinas. “Number of Fixed Points and Disjoint Cycles in Monotone Boolean Networks”. In:SIAM Journal on Discrete Mathematics31.3 (2017), pp. 1702–1725.doi:10.1137/16M1060868

  23. [23]

    Local Negative Circuits and Cyclic Attractors in Boolean Networks with at Most Five Components

    Elisa Tonello, Etienne Farcot, and Claudine Chaouiya. “Local Negative Circuits and Cyclic Attractors in Boolean Networks with at Most Five Components”. In:SIAM Journal on Applied Dynamical Systems18.1 (2019), pp. 68–79.doi:10.1137/18M1173988

  24. [24]

    Attractor Separation and Signed Cycles in Asyn- chronous Boolean Networks

    Adrien Richard and Elisa Tonello. “Attractor Separation and Signed Cycles in Asyn- chronous Boolean Networks”. In:Theoretical Computer Science947 (2023), p. 113706. doi:10.1016/j.tcs.2023.113706. 17

  25. [25]

    Random Networks of Automata: A Simple Annealed Ap- proximation

    B. Derrida and Y. Pomeau. “Random Networks of Automata: A Simple Annealed Ap- proximation”. In:Europhysics Letters (EPL)1.2 (1986), pp. 45–49.doi:10.1209/0295- 5075/1/2/001

  26. [26]

    Boolean Dynamics with Random Couplings

    Maximino Aldana, Susan Coppersmith, and Leo P. Kadanoff. “Boolean Dynamics with Random Couplings”. In:Perspectives and Problems in Nonlinear Science. Springer New York, 2003, pp. 23–89.doi:10.1007/978-0-387-21789-5_2

  27. [27]

    Random Boolean Networks

    Barbara Drossel. “Random Boolean Networks”. In:Reviews of Nonlinear Dynamics and Complexity. Wiley, 2008, pp. 69–110.doi:10.1002/9783527626359.ch3

  28. [28]

    Superpolynomial Growth in the Number of Attractors in Kauffman Networks

    Björn Samuelsson and Carl Troein. “Superpolynomial Growth in the Number of Attractors in Kauffman Networks”. In:Physical Review Letters90.9 (2003), p. 098701.doi:10.1103/ PhysRevLett.90.098701

  29. [29]

    NumberofAttractorsinRandomBooleanNetworks

    BarbaraDrossel.“NumberofAttractorsinRandomBooleanNetworks”.In:Physical Review E72.1 (2005), p. 016110.doi:10.1103/PhysRevE.72.016110

  30. [30]

    Stable and Unstable Attractors in Boolean Networks

    Konstantin Klemm and Stefan Bornholdt. “Stable and Unstable Attractors in Boolean Networks”. In:Physical Review E72.5 (2005), p. 055101.doi:10.1103/PhysRevE.72. 055101

  31. [31]

    Attractors in Continuous and Boolean Networks

    Johannes Norrell, Björn Samuelsson, and Joshua E. S. Socolar. “Attractors in Continuous and Boolean Networks”. In:Physical Review E76.4 (2007), p. 046122.doi:10 . 1103 / PhysRevE.76.046122

  32. [32]

    Damage Spreading and the Lyapunov Spectrum of Cellular Automata and Boolean Networks

    Milan Vispoel, Aisling J. Daly, and Jan M. Baetens. “Damage Spreading and the Lyapunov Spectrum of Cellular Automata and Boolean Networks”. In:Chaos, Solitons & Fractals184 (2024), p. 114989.doi:10.1016/j.chaos.2024.114989

  33. [33]

    Choices of Regulatory Logic Class Modulate the Dynamical Regime in Random Boolean Networks

    Priyotosh Sil et al. “Choices of Regulatory Logic Class Modulate the Dynamical Regime in Random Boolean Networks”. In:Chaos, Solitons & Fractals195 (2025), p. 116231.doi: 10.1016/j.chaos.2025.116231

  34. [34]

    Cluster Synchronization in Boolean Neuronal Networks: Roles of Temperature, Time-Delay and Network Topology

    Chenggui Yao, Wei Zou, and Jürgen Kurths. “Cluster Synchronization in Boolean Neuronal Networks: Roles of Temperature, Time-Delay and Network Topology”. In:Chaos, Solitons & Fractals200 (2025), p. 117136.doi:10.1016/j.chaos.2025.117136

  35. [35]

    Strategic Node Identification in Complex Network Dynamics

    Elaheh Nikougoftar. “Strategic Node Identification in Complex Network Dynamics”. In: Chaos, Solitons & Fractals187 (2024), p. 115348.doi:10.1016/j.chaos.2024.115348

  36. [36]

    Dynamics Resilience of Complex Networks under Edge-Additions

    Xingyue Wen et al. “Dynamics Resilience of Complex Networks under Edge-Additions”. In: Chaos, Solitons & Fractals201 (2025), p. 117389.doi:10.1016/j.chaos.2025.117389

  37. [37]

    Cyclic Symmetric Dynamics in Chaotic Maps

    Jin Liu, Kehui Sun, and Huihai Wang. “Cyclic Symmetric Dynamics in Chaotic Maps”. In: Chaos, Solitons & Fractals189 (2024), p. 115684.doi:10.1016/j.chaos.2024.115684

  38. [38]

    The Effect of Network Topology on the Stability of Discrete State Models of Genetic Control

    Andrew Pomerance et al. “The Effect of Network Topology on the Stability of Discrete State Models of Genetic Control”. In:Proceedings of the National Academy of Sciences 106.20 (2009), pp. 8209–8214.doi:10.1073/pnas.0900142106

  39. [39]

    Algorithms for Finding Small Attractors in Boolean Networks

    Shu-Qin Zhang et al. “Algorithms for Finding Small Attractors in Boolean Networks”. In: EURASIP Journal on Bioinformatics and Systems Biology2007 (2007), pp. 1–13.doi: 10.1155/2007/20180

  40. [40]

    A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks

    Elena Dubrova and Maxim Teslenko. “A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks”. In:IEEE/ACM Transactions on Computational Biology and Bioinformatics8.5 (2011), pp. 1393–1399.doi:10.1109/TCBB.2010.20. 18

  41. [41]

    An Efficient Algorithm for Computing Attractors of Synchronous and Asynchronous Boolean Networks

    Desheng Zheng et al. “An Efficient Algorithm for Computing Attractors of Synchronous and Asynchronous Boolean Networks”. In:PLoS ONE8.4 (2013), e60593.doi:10.1371/ journal.pone.0060593

  42. [42]

    Using Answer Set Programming to Deal with Boolean Networks and Attractor Computation: Application to Gene Regula- tory Networks of Cells

    Tarek Khaled, Belaid Benhamou, and Van-Giang Trinh. “Using Answer Set Programming to Deal with Boolean Networks and Attractor Computation: Application to Gene Regula- tory Networks of Cells”. In:Annals of Mathematics and Artificial Intelligence91.5 (2023), pp. 713–750.doi:10.1007/s10472-023-09886-7

  43. [43]

    Attractor Detection and Enumeration Algorithms for Boolean Networks

    Tomoya Mori and Tatsuya Akutsu. “Attractor Detection and Enumeration Algorithms for Boolean Networks”. In:Computational and Structural Biotechnology Journal20 (2022), pp. 2512–2520.doi:10.1016/j.csbj.2022.05.027

  44. [44]

    Identification of Periodic Attractors in Boolean Networks Using a Priori Information

    Ulrike Münzner et al. “Identification of Periodic Attractors in Boolean Networks Using a Priori Information”. In:PLoS Computational Biology18.1 (2022), e1009702.doi:10.1371/ journal.pcbi.1009702

  45. [45]

    Exploring Attractor Bifurcations in Boolean Networks

    Nikola Beneš et al. “Exploring Attractor Bifurcations in Boolean Networks”. In:BMC Bioinformatics23.1 (2022), p. 173.doi:10.1186/s12859-022-04708-9

  46. [46]

    BooN: Boolean network analysis software

    Franck Delaplace. “BooN: Boolean network analysis software”. In:Bioinformatics Advances 5.1 (2025), vbaf082.doi:10.1093/bioadv/vbaf082

  47. [47]

    Dynamics of Generalized Asynchronous Boolean Networks Based on Proba- bility Transition: Searching for Attractors and Basins

    G. Li et al. “Dynamics of Generalized Asynchronous Boolean Networks Based on Proba- bility Transition: Searching for Attractors and Basins”. In:Chaos, Solitons & Fractals197 (2025), p. 116467.doi:10.1016/j.chaos.2025.116467

  48. [48]

    Finding Attractors in Asynchronous Boolean Dynamics

    Thomas Skodawessely and Konstantin Klemm. “Finding Attractors in Asynchronous Boolean Dynamics”. In:Advances in Complex Systems14.3 (2011), pp. 439–449.doi: 10.1142/S0219525911003098

  49. [49]

    Computing Maximal and Min- imal Trap Spaces of Boolean Networks

    Hannes Klarner, Alexander Bockmayr, and Heike Siebert. “Computing Maximal and Min- imal Trap Spaces of Boolean Networks”. In:Natural Computing14.4 (2015), pp. 535–544. doi:10.1007/s11047-015-9520-7

  50. [50]

    Computational Complexity of Minimal Trap Spaces in Boolean Networks

    Kyungduk Moon, Kangbok Lee, and Loïc Paulevé. “Computational Complexity of Minimal Trap Spaces in Boolean Networks”. In:SIAM Journal on Discrete Mathematics38.4 (2024), pp. 2691–2708.doi:10.1137/23M1553248

  51. [51]

    Complexity of Limit-Cycle Problems in Boolean Networks

    Florian Bridoux et al. “Complexity of Limit-Cycle Problems in Boolean Networks”. In: SOFSEM 2021: Theory and Practice of Computer Science. Vol. 12607. Lecture Notes in Computer Science. Springer, 2021, pp. 135–146.doi:10.1007/978-3-030-67731-2_10

  52. [52]

    Limit Cycles and Update Digraphs in Boolean Networks

    Julio Aracena, Luis Gómez, and Lilian Salinas. “Limit Cycles and Update Digraphs in Boolean Networks”. In:Discrete Applied Mathematics161.1–2 (2013), pp. 1–12.doi:10. 1016/j.dam.2012.07.003

  53. [53]

    Complexity of Limit Cycles with Block-Sequential Update Schedules in Conjunctive Networks

    Julio Aracena et al. “Complexity of Limit Cycles with Block-Sequential Update Schedules in Conjunctive Networks”. In:Natural Computing22 (2023), pp. 411–429.doi:10.1007/ s11047-023-09947-0

  54. [54]

    Dichotomy Results for Fixed-Point Existence Problems for Boolean Dy- namical Systems

    Sven Kosub. “Dichotomy Results for Fixed-Point Existence Problems for Boolean Dy- namical Systems”. In:Mathematics in Computer Science1.3 (2008), pp. 487–505.doi: 10.1007/s11786-007-0038-y

  55. [55]

    Dichotomy Results for Fixed Point Counting in Boolean Dynamical Systems

    Christopher M. Homan and Sven Kosub. “Dichotomy Results for Fixed Point Counting in Boolean Dynamical Systems”. In:Theoretical Computer Science573 (2015), pp. 16–25. doi:10.1016/j.tcs.2015.01.040. 19

  56. [56]

    Complexity of Fixed Point Counting Problems in Boolean Net- works

    Florian Bridoux et al. “Complexity of Fixed Point Counting Problems in Boolean Net- works”. In:Journal of Computer and System Sciences126 (2022), pp. 138–164.doi:10. 1016/j.jcss.2022.01.004

  57. [57]

    A SAT-Based Method for Counting All Singleton Attractors in Boolean Networks

    Rei Higuchi et al. “A SAT-Based Method for Counting All Singleton Attractors in Boolean Networks”.In:Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2025, pp. 2601–2609.doi:10.24963/ijcai.2025/290

  58. [58]

    Springer Monographs in Mathematics

    Dietlinde Lau.Function Algebras on Finite Sets: Basic Course on Many-Valued Logic and Clone Theory. Springer Monographs in Mathematics. Berlin: Springer, 2006.doi:10.1007/ 3-540-36023-9

  59. [59]

    S. V. Yablonsky, G. P. Gavrilov, and V. B. Kudryavtsev.Functions of the Algebra of Logic and Post Classes. In Russian. Moscow: Nauka, 1966. 120 pp

  60. [60]

    Garey and David S

    Michael R. Garey and David S. Johnson.Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco: W. H. Freeman, 1979.isbn: 0-7167-1045-5

  61. [61]

    Attractor Identification in Asynchronous Boolean Dynam- ics with Network Reduction

    Elisa Tonello and Loïc Paulevé. “Attractor Identification in Asynchronous Boolean Dynam- ics with Network Reduction”. In:Computational Methods in Systems Biology. Vol. 14137. Lecture Notes in Computer Science. Springer Nature Switzerland, 2023, pp. 202–219.doi: 10.1007/978-3-031-42697-1_14

  62. [62]

    Mapping the Attractor Landscape of Boolean Networks with biobalm

    Van-Giang Trinh et al. “Mapping the Attractor Landscape of Boolean Networks with biobalm”. In:Bioinformatics41.5 (2025), btaf280.doi:10 . 1093 / bioinformatics / btaf280

  63. [63]

    The Basis of Easy Controllability in Boolean Networks

    Enrico Borriello and Bryan C. Daniels. “The Basis of Easy Controllability in Boolean Networks”. In:Nature Communications12.1 (2021), p. 5227.doi:10.1038/s41467-021- 25533-3

  64. [64]

    Difficult Control Is Related to Instability in Bi- ologically Inspired Boolean Networks

    Bryan C. Daniels and Enrico Borriello. “Difficult Control Is Related to Instability in Bi- ologically Inspired Boolean Networks”. In:PLOS Complex Systems2.1 (2025), e0000025. doi:10.1371/journal.pcsy.0000025

  65. [65]

    Lattice Structures That Parameterize Regulatory Network Dynamics

    Tomáš Gedeon. “Lattice Structures That Parameterize Regulatory Network Dynamics”. In: Mathematical Biosciences374 (2024), p. 109225.doi:10.1016/j.mbs.2024.109225

  66. [66]

    An Open Problem: Why Are Motif-Avoidant Attractors So Rare in Asynchronous Boolean Networks?

    Samuel Pastva et al. “An Open Problem: Why Are Motif-Avoidant Attractors So Rare in Asynchronous Boolean Networks?” In:Journal of Mathematical Biology91.1 (2025), p. 11. doi:10.1007/s00285-025-02235-8. 20