Social networks help to infer causality in the tumor microenvironment.

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_E092613F445E
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Social networks help to infer causality in the tumor microenvironment.
Périodique
Bmc Research Notes
Auteur(s)
Crespo I., Doucey M.A., Xenarios I.
ISSN
1756-0500 (Electronic)
ISSN-L
1756-0500
Statut éditorial
Publié
Date de publication
2016
Peer-reviewed
Oui
Volume
9
Numéro
1
Pages
168
Langue
anglais
Résumé
BACKGROUND: Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems-such as the tumor microenvironment-where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies.
RESULTS: In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature.
CONCLUSIONS: This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.
Pubmed
Open Access
Oui
Création de la notice
17/03/2016 17:32
Dernière modification de la notice
20/08/2019 16:04
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