Cargando…

Statistical inference of the time-varying structure of gene-regulation networks

BACKGROUND: Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems...

Descripción completa

Detalles Bibliográficos
Autores principales: Lèbre, Sophie, Becq, Jennifer, Devaux, Frédéric, Stumpf, Michael PH, Lelandais, Gaëlle
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2955603/
https://www.ncbi.nlm.nih.gov/pubmed/20860793
http://dx.doi.org/10.1186/1752-0509-4-130
_version_ 1782188049275289600
author Lèbre, Sophie
Becq, Jennifer
Devaux, Frédéric
Stumpf, Michael PH
Lelandais, Gaëlle
author_facet Lèbre, Sophie
Becq, Jennifer
Devaux, Frédéric
Stumpf, Michael PH
Lelandais, Gaëlle
author_sort Lèbre, Sophie
collection PubMed
description BACKGROUND: Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions. METHODS: To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.). RESULTS: We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning. CONCLUSIONS: ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.
format Text
id pubmed-2955603
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-29556032010-10-18 Statistical inference of the time-varying structure of gene-regulation networks Lèbre, Sophie Becq, Jennifer Devaux, Frédéric Stumpf, Michael PH Lelandais, Gaëlle BMC Syst Biol Methodology Article BACKGROUND: Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions. METHODS: To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.). RESULTS: We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning. CONCLUSIONS: ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail. BioMed Central 2010-09-22 /pmc/articles/PMC2955603/ /pubmed/20860793 http://dx.doi.org/10.1186/1752-0509-4-130 Text en Copyright ©2010 Lèbre et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Lèbre, Sophie
Becq, Jennifer
Devaux, Frédéric
Stumpf, Michael PH
Lelandais, Gaëlle
Statistical inference of the time-varying structure of gene-regulation networks
title Statistical inference of the time-varying structure of gene-regulation networks
title_full Statistical inference of the time-varying structure of gene-regulation networks
title_fullStr Statistical inference of the time-varying structure of gene-regulation networks
title_full_unstemmed Statistical inference of the time-varying structure of gene-regulation networks
title_short Statistical inference of the time-varying structure of gene-regulation networks
title_sort statistical inference of the time-varying structure of gene-regulation networks
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2955603/
https://www.ncbi.nlm.nih.gov/pubmed/20860793
http://dx.doi.org/10.1186/1752-0509-4-130
work_keys_str_mv AT lebresophie statisticalinferenceofthetimevaryingstructureofgeneregulationnetworks
AT becqjennifer statisticalinferenceofthetimevaryingstructureofgeneregulationnetworks
AT devauxfrederic statisticalinferenceofthetimevaryingstructureofgeneregulationnetworks
AT stumpfmichaelph statisticalinferenceofthetimevaryingstructureofgeneregulationnetworks
AT lelandaisgaelle statisticalinferenceofthetimevaryingstructureofgeneregulationnetworks