Cargando…
Social networks help to infer causality in the tumor microenvironment
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, s...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793762/ https://www.ncbi.nlm.nih.gov/pubmed/26979239 http://dx.doi.org/10.1186/s13104-016-1976-8 |
_version_ | 1782421418903863296 |
---|---|
author | Crespo, Isaac Doucey, Marie-Agnès Xenarios, Ioannis |
author_facet | Crespo, Isaac Doucey, Marie-Agnès Xenarios, Ioannis |
author_sort | Crespo, Isaac |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-016-1976-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4793762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47937622016-03-17 Social networks help to infer causality in the tumor microenvironment Crespo, Isaac Doucey, Marie-Agnès Xenarios, Ioannis BMC Res Notes Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-016-1976-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-03-15 /pmc/articles/PMC4793762/ /pubmed/26979239 http://dx.doi.org/10.1186/s13104-016-1976-8 Text en © Crespo et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Crespo, Isaac Doucey, Marie-Agnès Xenarios, Ioannis Social networks help to infer causality in the tumor microenvironment |
title | Social networks help to infer causality in the tumor microenvironment |
title_full | Social networks help to infer causality in the tumor microenvironment |
title_fullStr | Social networks help to infer causality in the tumor microenvironment |
title_full_unstemmed | Social networks help to infer causality in the tumor microenvironment |
title_short | Social networks help to infer causality in the tumor microenvironment |
title_sort | social networks help to infer causality in the tumor microenvironment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793762/ https://www.ncbi.nlm.nih.gov/pubmed/26979239 http://dx.doi.org/10.1186/s13104-016-1976-8 |
work_keys_str_mv | AT crespoisaac socialnetworkshelptoinfercausalityinthetumormicroenvironment AT douceymarieagnes socialnetworkshelptoinfercausalityinthetumormicroenvironment AT xenariosioannis socialnetworkshelptoinfercausalityinthetumormicroenvironment |