Boolean network sketches: a unifying framework for logical
model inference

J 2023

Boolean network sketches: a unifying framework for logical model inference

BENEŠ, Nikola, Luboš BRIM, Ondřej HUVAR, Samuel PASTVA, David ŠAFRÁNEK et. al.

Basic information

Original name

Boolean network sketches: a unifying framework for logical model inference

Authors

BENEŠ, Nikola (203 Czech Republic, belonging to the institution), Luboš BRIM (203 Czech Republic, belonging to the institution), Ondřej HUVAR (203 Czech Republic, belonging to the institution), Samuel PASTVA (203 Czech Republic) and David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution)

Edition

Bioinformatics, 2023, 1367-4803

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.800 in 2022

RIV identification code

RIV/00216224:14330/23:00130796

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1093/bioinformatics/btad158

UT WoS

000976610800001

Keywords in English

Boolean Networks; model inference; logical modelling

Změněno: 8/4/2024 15:38, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

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. RESULTS: 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.

Links

GA22-10845S, research and development project

Name: Studium role polyhydroxyalkanoátů u bakterie Schlegelella thermodepolymerans – slibného bakteriálního kandidáta pro biotechnologie nové generace (Acronym: PHAST)

Investor: Czech Science Foundation, Unraveling the role of polyhydroxyalkanoates in Schlegelella thermodepolymerans – promising environmental bacterium for next generation biotechnology

MUNI/A/1081/2022, interní kód MU

Name: Modelování, analýza a verifikace (2023)

Investor: Masaryk University

Citovat

BENEŠ, Nikola, Luboš BRIM, Ondřej HUVAR, Samuel PASTVA and David ŠAFRÁNEK. Boolean network sketches: a unifying framework for logical model inference. Bioinformatics. 2023, vol. 39, No 4, p. "btad158", 8 pp. ISSN 1367-4803. Available from: https://dx.doi.org/10.1093/bioinformatics/btad158.

@article{2283417,
   author = {Beneš, Nikola and Brim, Luboš and Huvar, Ondřej and Pastva, Samuel and Šafránek, David},
   article_number = {4},
   doi = {http://dx.doi.org/10.1093/bioinformatics/btad158},
   keywords = {Boolean Networks; model inference; logical modelling},
   language = {eng},
   issn = {1367-4803},
   journal = {Bioinformatics},
   title = {Boolean network sketches: a unifying framework for logical model inference},
   url = {https://doi.org/10.1093/bioinformatics/btad158},
   volume = {39},
   year = {2023}
}

TY  - JOUR
ID  - 2283417
AU  - Beneš, Nikola - Brim, Luboš - Huvar, Ondřej - Pastva, Samuel - Šafránek, David
PY  - 2023
TI  - Boolean network sketches: a unifying framework for logical model inference
JF  - Bioinformatics
VL  - 39
IS  - 4
SP  - "btad158"
EP  - "btad158"
SN  - 13674803
KW  - Boolean Networks
KW  - model inference
KW  - logical modelling
UR  - https://doi.org/10.1093/bioinformatics/btad158
N2  - 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. RESULTS: 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.
ER  -

BENEŠ, Nikola, Luboš BRIM, Ondřej HUVAR, Samuel PASTVA and David ŠAFRÁNEK. Boolean network sketches: a unifying framework for logical model inference. \textit{Bioinformatics}. 2023, vol.~39, No~4, p.~''btad158'', 8 pp. ISSN~1367-4803. Available from: https://dx.doi.org/10.1093/bioinformatics/btad158.

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