Boolean network sketches: a unifying framework for logical model inference

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130796" target="_blank" >RIV/00216224:14330/23:00130796 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1093/bioinformatics/btad158" target="_blank" >https://doi.org/10.1093/bioinformatics/btad158</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioinformatics/btad158" target="_blank" >10.1093/bioinformatics/btad158</a>

Result language

angličtina

Original language name

Boolean network sketches: a unifying framework for logical model inference

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-10845S" target="_blank" >GA22-10845S: Unraveling the role of polyhydroxyalkanoates in Schlegelella thermodepolymerans – promising environmental bacterium for next generation biotechnology</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Bioinformatics

ISSN

1367-4803

e-ISSN

—

Volume of the periodical

39

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

„btad158“

UT code for WoS article

000976610800001

EID of the result in the Scopus database

2-s2.0-85153541643