Inference of an Integrative, Executable ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map
Auteur(s) :
Miagoux, Quentin [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Computational systems biology and optimization [Lifeware]
Singh, Vidisha [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
de Mézquita, Dereck [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Chaudru, Valérie [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Elati, Mohamed [Auteur]
Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Petit-Teixeira, Elisabeth [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Niarakis, Anna [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Computational systems biology and optimization [Lifeware]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Computational systems biology and optimization [Lifeware]
Singh, Vidisha [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
de Mézquita, Dereck [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Chaudru, Valérie [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Elati, Mohamed [Auteur]
Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Petit-Teixeira, Elisabeth [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Niarakis, Anna [Auteur]
Laboratoire de recherche européen pour la polyarthrite rhumatoïde [GenHotel]
Computational systems biology and optimization [Lifeware]
Titre de la revue :
Journal of personalized medicine
Pagination :
785
Éditeur :
MDPI
Date de publication :
2021
ISSN :
2075-4426
Mot(s)-clé(s) en anglais :
network inference
integrative biology
rheumatoid arthritis
signaling cascades
gene regulation
transcription factors
Boolean simulations
systems biology
integrative biology
rheumatoid arthritis
signaling cascades
gene regulation
transcription factors
Boolean simulations
systems biology
Discipline(s) HAL :
Sciences du Vivant [q-bio]/Médecine humaine et pathologie/Rhumatologie et système ostéo-articulaire
Informatique [cs]/Bio-informatique [q-bio.QM]
Informatique [cs]/Bio-informatique [q-bio.QM]
Résumé en anglais : [en]
Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by ...
Lire la suite >Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.Lire moins >
Lire la suite >Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400381
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- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400381
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