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Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks
BACKGROUND: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more acc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433335/ https://www.ncbi.nlm.nih.gov/pubmed/22651900 http://dx.doi.org/10.1186/1752-0509-6-54 |
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author | Martin, Florian Thomson, Ty M Sewer, Alain Drubin, David A Mathis, Carole Weisensee, Dirk Pratt, Dexter Hoeng, Julia Peitsch, Manuel C |
author_facet | Martin, Florian Thomson, Ty M Sewer, Alain Drubin, David A Mathis, Carole Weisensee, Dirk Pratt, Dexter Hoeng, Julia Peitsch, Manuel C |
author_sort | Martin, Florian |
collection | PubMed |
description | BACKGROUND: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. RESULTS: Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. CONCLUSIONS: The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments. |
format | Online Article Text |
id | pubmed-3433335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34333352012-09-06 Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks Martin, Florian Thomson, Ty M Sewer, Alain Drubin, David A Mathis, Carole Weisensee, Dirk Pratt, Dexter Hoeng, Julia Peitsch, Manuel C BMC Syst Biol Methodology Article BACKGROUND: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. RESULTS: Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. CONCLUSIONS: The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments. BioMed Central 2012-05-31 /pmc/articles/PMC3433335/ /pubmed/22651900 http://dx.doi.org/10.1186/1752-0509-6-54 Text en Copyright ©2012 Martin et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Martin, Florian Thomson, Ty M Sewer, Alain Drubin, David A Mathis, Carole Weisensee, Dirk Pratt, Dexter Hoeng, Julia Peitsch, Manuel C Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title | Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title_full | Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title_fullStr | Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title_full_unstemmed | Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title_short | Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
title_sort | assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433335/ https://www.ncbi.nlm.nih.gov/pubmed/22651900 http://dx.doi.org/10.1186/1752-0509-6-54 |
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