---
_id: '12876'
abstract:
- lang: eng
  text: "Motivation: The problem of model inference is of fundamental importance to
    systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally
    attractive approach capable of handling large biological networks. The models
    are typically inferred from experimental data. However, even with a substantial
    amount of experimental data supported by some prior knowledge, existing inference
    methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults:
    We propose Boolean network sketches as a new formal instrument for the inference
    of Boolean networks. A sketch integrates (typically partial) knowledge about the
    network’s topology and the update logic (obtained through, e.g. a biological knowledge
    base or a literature search), as well as further assumptions about the properties
    of the network’s transitions (e.g. the form of its attractor landscape), and additional
    restrictions on the model dynamics given by the measured experimental data. Our
    new BNs inference algorithm starts with an ‘initial’ sketch, which is extended
    by adding restrictions representing experimental data to a ‘data-informed’ sketch
    and subsequently computes all BNs consistent with the data-informed sketch. Our
    algorithm is based on a symbolic representation and coloured model-checking. Our
    approach is unique in its ability to cover a broad spectrum of knowledge and efficiently
    produce a compact representation of all inferred BNs. We evaluate the method on
    a non-trivial collection of real-world and simulated data."
acknowledgement: This work was partially supported by GACR [grant No. GA22-10845S];
  and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was
  partially supported by European Union’s Horizon 2020 research and innovation programme
  under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.].
article_number: btad158
article_processing_charge: No
article_type: original
author:
- first_name: Nikola
  full_name: Beneš, Nikola
  last_name: Beneš
- first_name: Luboš
  full_name: Brim, Luboš
  last_name: Brim
- first_name: Ondřej
  full_name: Huvar, Ondřej
  last_name: Huvar
- first_name: Samuel
  full_name: Pastva, Samuel
  id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
  last_name: Pastva
  orcid: 0000-0003-1993-0331
- first_name: David
  full_name: Šafránek, David
  last_name: Šafránek
citation:
  ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches:
    A unifying framework for logical model inference. <i>Bioinformatics</i>. 2023;39(4).
    doi:<a href="https://doi.org/10.1093/bioinformatics/btad158">10.1093/bioinformatics/btad158</a>'
  apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., &#38; Šafránek, D. (2023). Boolean
    network sketches: A unifying framework for logical model inference. <i>Bioinformatics</i>.
    Oxford University Press. <a href="https://doi.org/10.1093/bioinformatics/btad158">https://doi.org/10.1093/bioinformatics/btad158</a>'
  chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek.
    “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.”
    <i>Bioinformatics</i>. Oxford University Press, 2023. <a href="https://doi.org/10.1093/bioinformatics/btad158">https://doi.org/10.1093/bioinformatics/btad158</a>.'
  ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network
    sketches: A unifying framework for logical model inference,” <i>Bioinformatics</i>,
    vol. 39, no. 4. Oxford University Press, 2023.'
  ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches:
    A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.'
  mla: 'Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for
    Logical Model Inference.” <i>Bioinformatics</i>, vol. 39, no. 4, btad158, Oxford
    University Press, 2023, doi:<a href="https://doi.org/10.1093/bioinformatics/btad158">10.1093/bioinformatics/btad158</a>.'
  short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023).
date_created: 2023-04-30T22:01:05Z
date_published: 2023-04-03T00:00:00Z
date_updated: 2025-05-14T11:06:50Z
day: '03'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btad158
ec_funded: 1
external_id:
  isi:
  - '000976610800001'
  pmid:
  - '37004199'
file:
- access_level: open_access
  checksum: 2cb90ddf781baefddf47eac4b54e2a03
  content_type: application/pdf
  creator: dernst
  date_created: 2023-05-02T07:39:04Z
  date_updated: 2023-05-02T07:39:04Z
  file_id: '12886'
  file_name: 2023_Bioinformatics_Benes.pdf
  file_size: 478740
  relation: main_file
  success: 1
file_date_updated: 2023-05-02T07:39:04Z
fulldoi: https://doi.org/10.1093/bioinformatics/btad158
has_accepted_license: '1'
intvolume: '        39'
isi: 1
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
  call_identifier: H2020
  grant_number: '101034413'
  name: 'IST-BRIDGE: International postdoctoral program'
publication: Bioinformatics
publication_identifier:
  eissn:
  - 1367-4811
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://doi.org/10.5281/zenodo.7688740
scopus_import: '1'
status: public
title: 'Boolean network sketches: A unifying framework for logical model inference'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2023'
...