Mapping the attractor landscape of Boolean networks with biobalm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00142554" target="_blank" >RIV/00216224:14330/25:00142554 - isvavai.cz</a>

Výsledek na webu

<a href="https://academic.oup.com/bioinformatics/article/41/5/btaf280/8125815" target="_blank" >https://academic.oup.com/bioinformatics/article/41/5/btaf280/8125815</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioinformatics/btaf280" target="_blank" >10.1093/bioinformatics/btaf280</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mapping the attractor landscape of Boolean networks with biobalm

Popis výsledku v původním jazyce

Motivation Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington's epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.Results We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist's Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.Availability and implementation The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.

Název v anglickém jazyce

Mapping the attractor landscape of Boolean networks with biobalm

Popis výsledku anglicky

Motivation Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington's epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.Results We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist's Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.Availability and implementation The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

BIOINFORMATICS

ISSN

1367-4803

e-ISSN

1367-4811

Svazek periodika

41

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1-8

Kód UT WoS článku

001493400600001

EID výsledku v databázi Scopus

2-s2.0-105006628370