Modeling delayed dynamics in biological regulatory networks from time series data
type: Item
instance of: scholarly article
title: Modeling delayed dynamics in biological regulatory networks from time series data
zbMATH Open document ID: 1461.62184
author: Emna Ben Abdallah
author: Tony Ribeiro
author: Morgan Magnin
author: Olivier Roux
author: Katsumi Inoue
published in: Algorithms
publication date: 2018-08-20T00:00:00Z
review text: Summary: Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4 (in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 92C42
zbMATH DE Number: 6920553
zbMATH Keywords: learning models
zbMATH Keywords: dynamics modeling
zbMATH Keywords: delayed biological regulatory networks
zbMATH Keywords: timed automata networks
zbMATH Keywords: time series data
describes a project that uses: Clingo
describes a project that uses: GeneNetWeaver
describes a project that uses: DREAMTools
describes a project that uses: PREMER
describes a project that uses: hiPathDB
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a10010008
OpenAlex ID: W2568256205
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DOI: 10.3390/A10010008
description: scientific article; zbMATH DE number 6920553