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Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects
Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072688/ https://www.ncbi.nlm.nih.gov/pubmed/27764199 http://dx.doi.org/10.1371/journal.pone.0165049 |
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author | Zhang, Fan Wu, Min Kwoh, Chee Keong Zheng, Jie |
author_facet | Zhang, Fan Wu, Min Kwoh, Chee Keong Zheng, Jie |
author_sort | Zhang, Fan |
collection | PubMed |
description | Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model. |
format | Online Article Text |
id | pubmed-5072688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50726882016-10-27 Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects Zhang, Fan Wu, Min Kwoh, Chee Keong Zheng, Jie PLoS One Research Article Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model. Public Library of Science 2016-10-20 /pmc/articles/PMC5072688/ /pubmed/27764199 http://dx.doi.org/10.1371/journal.pone.0165049 Text en © 2016 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhang, Fan Wu, Min Kwoh, Chee Keong Zheng, Jie Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title | Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title_full | Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title_fullStr | Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title_full_unstemmed | Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title_short | Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects |
title_sort | power-law modeling of cancer cell fates driven by signaling data to reveal drug effects |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072688/ https://www.ncbi.nlm.nih.gov/pubmed/27764199 http://dx.doi.org/10.1371/journal.pone.0165049 |
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