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.

Základní údaje

Originální název

Boolean network sketches: a unifying framework for logical model inference

Autoři

BENEŠ, Nikola; Luboš BRIM; Ondřej HUVAR; Samuel PASTVA a David ŠAFRÁNEK

Vydání

Bioinformatics, 2023, 1367-4803

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.400

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/23:00130796

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Boolean Networks; model inference; logical modelling

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

Anotace

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.

Návaznosti

GA22-10845S, projekt VaV

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

Investor: Grantová agentura ČR, Studium role polyhydroxyalkanoátů u bakterie Schlegelella thermodepolymerans – slibného bakteriálního kandidáta pro biotechnologie nové generace

MUNI/A/1081/2022, interní kód MU

Název: Modelování, analýza a verifikace (2023)

Investor: Masarykova univerzita, Modelování, analýza a verifikace (2023)

Citovat

BENEŠ, Nikola; Luboš BRIM; Ondřej HUVAR; Samuel PASTVA a David ŠAFRÁNEK. Boolean network sketches: a unifying framework for logical model inference. Bioinformatics. 2023, roč. 39, č. 4, s. "btad158", 8 s. ISSN 1367-4803. Dostupné z: https://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 = {https://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 a David ŠAFRÁNEK. Boolean network sketches: a unifying framework for logical model inference. \textit{Bioinformatics}. 2023, roč.~39, č.~4, s.~''btad158'', 8 s. ISSN~1367-4803. Dostupné z: https://doi.org/10.1093/bioinformatics/btad158.

Přehled o publikaci