An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00144508" target="_blank" >RIV/00216224:14330/25:00144508 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00285-025-02235-8" target="_blank" >https://link.springer.com/article/10.1007/s00285-025-02235-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00285-025-02235-8" target="_blank" >10.1007/s00285-025-02235-8</a>

Result language

angličtina

Original language name

An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?

Original language description

Asynchronous Boolean networks are a type of discrete dynamical system in which each variable can take one of two states, and a single variable state is updated in each time step according to pre-selected rules. Boolean networks are popular in systems biology due to their ability to model long-term biological phenotypes within a qualitative, predictive framework. Boolean networks model phenotypes as attractors, which are closely linked to minimal trap spaces (inescapable hypercubes in the system's state space). In biological applications, attractors and minimal trap spaces are typically in one-to-one correspondence. However, this correspondence is not guaranteed: motif-avoidant attractors (MAAs) that lie outside minimal trap spaces are possible. MAAs are rare and poorly understood, despite recent efforts. In this contribution to the BMB &amp; JMB Special Collection "Problems, Progress and Perspectives in Mathematical and Computational Biology", we summarize the current state of knowledge regarding MAAs and present several novel observations regarding their response to node deletion reductions and linear extensions of edges. We conduct large-scale computational studies on an ensemble of 14 000 models derived from published Boolean models of biological systems, and more than 100 million Random Boolean Networks. Our findings quantify the rarity of MAAs; in particular, we only observed MAAs in biological models after applying standard simplification methods, highlighting the role of network reduction in introducing MAAs into the dynamics. We also show that MAAs are fragile to linear extensions: in sparse networks, even a single linear node can disrupt virtually all MAAs. Motivated by this observation, we improve the upper bound on the number of delays needed to disrupt a motif-avoidant attractor.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-10845S" target="_blank" >GA22-10845S: Unraveling the role of polyhydroxyalkanoates in Schlegelella thermodepolymerans – promising environmental bacterium for next generation biotechnology</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF MATHEMATICAL BIOLOGY

ISSN

0303-6812

e-ISSN

1432-1416

Volume of the periodical

91

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

36

Pages from-to

1-36

UT code for WoS article

001507009300001

EID of the result in the Scopus database

2-s2.0-105007921634