Boolean network sketches: a unifying framework for logical model inference

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.

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

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: 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

Abstract

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