Stability of linear Boolean networks

Karthik Chandrasekhar, Claus Kadelka, Reinhard Laubenbacher, David Murrugarra

Research output: Contribution to journalArticlepeer-review


Stability is an important characteristic of network models that has implications for other desirable aspects such as controllability. The stability of a Boolean network (BN) depends on various factors, such as the topology of its wiring diagram and the type of the functions describing its dynamics. Linear Boolean networks can be completely described by their wiring diagram, and therefore the structure of linear networks plays a prominent role in determining their stability. In this paper, we study the stability of linear Boolean networks by computing Derrida curves and quantifying the number of attractors and cycle lengths imposed by their network topologies. Derrida curves are commonly used to measure the stability of Boolean networks and several parameters such as the average in-degree K and the output bias p can indicate if a network is stable, critical, or unstable. For random unbiased Boolean networks there is a critical connectivity value Kc=2 such that if K<Kc networks operate in the ordered regime, and if K>Kc networks operate in the chaotic regime. Here, we show that for linear networks, which are the least canalizing and most unstable, the phase transition from order to chaos already happens at an average in-degree of Kc=1. Consistently, we also show that unstable networks exhibit a large number of attractors with very long limit cycles while stable and critical networks exhibit fewer attractors with shorter limit cycles. Additionally, we present theoretical results to quantify important dynamical properties of linear networks. First, we present a formula for the proportion of attractor states in linear systems. Second, we show that the expected number of fixed points in linear systems is 2, while general Boolean networks possess on average one fixed point. Third, we present a formula to quantify the number of bijective linear Boolean networks and provide a lower bound for the percentage of this type of network.

Original languageEnglish
Article number133775
JournalPhysica D: Nonlinear Phenomena
StatePublished - Sep 2023

Bibliographical note

Funding Information:
K.C. thanks Gergő Nemes, research fellow at Alfréd Rényi Institute of Mathematics, for his pertinent input in approximating convergent products. D.M. was partially supported by a Collaboration grant ( 850896 ) from the Simons Foundation, United States . C.K was partially supported by a Collaboration grant ( 712537 ) from the Simons Foundation, United States . The contribution of R.L. was partially supported by NIH, United States , Grants 1U01EB024501- 01 , 1 R01AI135128-01 and 1R01GM127909-01 .

Publisher Copyright:
© 2023 Elsevier B.V.


  • Attractors
  • Boolean networks
  • Derrida curves
  • Linear systems
  • Stability

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Applied Mathematics


Dive into the research topics of 'Stability of linear Boolean networks'. Together they form a unique fingerprint.

Cite this