Cargando…
Network inference from perturbation time course data
Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into es...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622863/ https://www.ncbi.nlm.nih.gov/pubmed/36316338 http://dx.doi.org/10.1038/s41540-022-00253-6 |
_version_ | 1784821868264423424 |
---|---|
author | Sarmah, Deepraj Smith, Gregory R. Bouhaddou, Mehdi Stern, Alan D. Erskine, James Birtwistle, Marc R. |
author_facet | Sarmah, Deepraj Smith, Gregory R. Bouhaddou, Mehdi Stern, Alan D. Erskine, James Birtwistle, Marc R. |
author_sort | Sarmah, Deepraj |
collection | PubMed |
description | Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible. |
format | Online Article Text |
id | pubmed-9622863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96228632022-11-02 Network inference from perturbation time course data Sarmah, Deepraj Smith, Gregory R. Bouhaddou, Mehdi Stern, Alan D. Erskine, James Birtwistle, Marc R. NPJ Syst Biol Appl Article Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible. Nature Publishing Group UK 2022-11-01 /pmc/articles/PMC9622863/ /pubmed/36316338 http://dx.doi.org/10.1038/s41540-022-00253-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sarmah, Deepraj Smith, Gregory R. Bouhaddou, Mehdi Stern, Alan D. Erskine, James Birtwistle, Marc R. Network inference from perturbation time course data |
title | Network inference from perturbation time course data |
title_full | Network inference from perturbation time course data |
title_fullStr | Network inference from perturbation time course data |
title_full_unstemmed | Network inference from perturbation time course data |
title_short | Network inference from perturbation time course data |
title_sort | network inference from perturbation time course data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622863/ https://www.ncbi.nlm.nih.gov/pubmed/36316338 http://dx.doi.org/10.1038/s41540-022-00253-6 |
work_keys_str_mv | AT sarmahdeepraj networkinferencefromperturbationtimecoursedata AT smithgregoryr networkinferencefromperturbationtimecoursedata AT bouhaddoumehdi networkinferencefromperturbationtimecoursedata AT sternaland networkinferencefromperturbationtimecoursedata AT erskinejames networkinferencefromperturbationtimecoursedata AT birtwistlemarcr networkinferencefromperturbationtimecoursedata |