Control-Guided Refinement of Partially Specified Boolean Networks: Applications to RTK Signalling

Clicks: 1
ID: 314861
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #792 of 829 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: System control can be used to provide new insights into the dynamics of biological systems. A key application is the identification of therapeutic targets in silico, which requires an executable model of the system's dynamics. However, such models are typically underspecified due to incomplete mechanistic knowledge. RESULTS: We introduce a novel computational framework that employs control-guided model refinement, predicting informative perturbation experiments to reduce knowledge gaps. The approach is based on partially specified Boolean networks (PSBNs), which enable direct integration of uncertain or incomplete information into executable models. We further extend the framework to handle oscillatory phenotypes as explicit control targets. The applicability of the method is demonstrated on receptor-tyrosine kinase (RTK) signalling, with a focus on fibroblast growth factor signalling in the context of skeletal dysplasias and cancer. We obtain several new insights into modelling of the FGFR3-MAPK pathway. AVAILABILITY AND IMPLEMENTATION: Code and datasets are available at https://doi.org/10.5281/zenodo.16886813.
Reference Key
openalex_W7162340890 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Eva Šmijáková, Luboš Brim, Samuel Pastva, David Šafránek
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag275
URL
https://doi.org/10.1093/bioinformatics/btag275
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.