Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions
<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2009-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/322 |
| _version_ | 1818061016634753024 |
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| author | Bender Christian Arlt Dorit Sahin Özgür Fröhlich Holger Beißbarth Tim |
| author_facet | Bender Christian Arlt Dorit Sahin Özgür Fröhlich Holger Beißbarth Tim |
| author_sort | Bender Christian |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p> |
| first_indexed | 2024-12-10T13:41:37Z |
| format | Article |
| id | doaj.art-c53a68351973437aaf57c7c3a10d521e |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T13:41:37Z |
| publishDate | 2009-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c53a68351973437aaf57c7c3a10d521e2022-12-22T01:46:39ZengBMCBMC Bioinformatics1471-21052009-10-0110132210.1186/1471-2105-10-322Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventionsBender ChristianArlt DoritSahin ÖzgürFröhlich HolgerBeißbarth Tim<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p>http://www.biomedcentral.com/1471-2105/10/322 |
| spellingShingle | Bender Christian Arlt Dorit Sahin Özgür Fröhlich Holger Beißbarth Tim Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions BMC Bioinformatics |
| title | Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions |
| title_full | Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions |
| title_fullStr | Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions |
| title_full_unstemmed | Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions |
| title_short | Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions |
| title_sort | deterministic effects propagation networks for reconstructing protein signaling networks from multiple interventions |
| url | http://www.biomedcentral.com/1471-2105/10/322 |
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