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Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection
BACKGROUND: The decision pro- or contra apoptosis is complex, involves a number of different inputs, and is central for the homeostasis of an individual cell as well as for the maintenance and regeneration of the complete organism. RESULTS: This study centers on Fas ligand (FasL)-mediated apoptosis,...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760522/ https://www.ncbi.nlm.nih.gov/pubmed/19772631 http://dx.doi.org/10.1186/1752-0509-3-97 |
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author | Philippi, Nicole Walter, Dorothee Schlatter, Rebekka Ferreira, Karine Ederer, Michael Sawodny, Oliver Timmer, Jens Borner, Christoph Dandekar, Thomas |
author_facet | Philippi, Nicole Walter, Dorothee Schlatter, Rebekka Ferreira, Karine Ederer, Michael Sawodny, Oliver Timmer, Jens Borner, Christoph Dandekar, Thomas |
author_sort | Philippi, Nicole |
collection | PubMed |
description | BACKGROUND: The decision pro- or contra apoptosis is complex, involves a number of different inputs, and is central for the homeostasis of an individual cell as well as for the maintenance and regeneration of the complete organism. RESULTS: This study centers on Fas ligand (FasL)-mediated apoptosis, and a complex and internally strongly linked network is assembled around the central FasL-mediated apoptosis cascade. Different bioinformatical techniques are employed and different crosstalk possibilities including the integrin pathway are considered. This network is translated into a Boolean network (74 nodes, 108 edges). System stability is dynamically sampled and investigated using the software SQUAD. Testing a number of alternative crosstalk possibilities and networks we find that there are four stable system states, two states comprising cell survival and two states describing apoptosis by the intrinsic and the extrinsic pathways, respectively. The model is validated by comparing it to experimental data from kinetics of cytochrome c release and caspase activation in wildtype and Bid knockout cells grown on different substrates. Pathophysiological modifications such as input from cytomegalovirus proteins M36 and M45 again produces output behavior that well agrees with experimental data. CONCLUSION: A network model for apoptosis and crosstalk in hepatocytes shows four different system states and reproduces a number of different conditions around apoptosis including effects of different growth substrates and viral infections. It produces semi-quantitative predictions on the activity of individual nodes, agreeing with experimental data. The model (SBML format) and all data are available for further predictions and development. |
format | Text |
id | pubmed-2760522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27605222009-10-13 Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection Philippi, Nicole Walter, Dorothee Schlatter, Rebekka Ferreira, Karine Ederer, Michael Sawodny, Oliver Timmer, Jens Borner, Christoph Dandekar, Thomas BMC Syst Biol Research Article BACKGROUND: The decision pro- or contra apoptosis is complex, involves a number of different inputs, and is central for the homeostasis of an individual cell as well as for the maintenance and regeneration of the complete organism. RESULTS: This study centers on Fas ligand (FasL)-mediated apoptosis, and a complex and internally strongly linked network is assembled around the central FasL-mediated apoptosis cascade. Different bioinformatical techniques are employed and different crosstalk possibilities including the integrin pathway are considered. This network is translated into a Boolean network (74 nodes, 108 edges). System stability is dynamically sampled and investigated using the software SQUAD. Testing a number of alternative crosstalk possibilities and networks we find that there are four stable system states, two states comprising cell survival and two states describing apoptosis by the intrinsic and the extrinsic pathways, respectively. The model is validated by comparing it to experimental data from kinetics of cytochrome c release and caspase activation in wildtype and Bid knockout cells grown on different substrates. Pathophysiological modifications such as input from cytomegalovirus proteins M36 and M45 again produces output behavior that well agrees with experimental data. CONCLUSION: A network model for apoptosis and crosstalk in hepatocytes shows four different system states and reproduces a number of different conditions around apoptosis including effects of different growth substrates and viral infections. It produces semi-quantitative predictions on the activity of individual nodes, agreeing with experimental data. The model (SBML format) and all data are available for further predictions and development. BioMed Central 2009-09-22 /pmc/articles/PMC2760522/ /pubmed/19772631 http://dx.doi.org/10.1186/1752-0509-3-97 Text en Copyright © 2009 Philippi 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 | Research Article Philippi, Nicole Walter, Dorothee Schlatter, Rebekka Ferreira, Karine Ederer, Michael Sawodny, Oliver Timmer, Jens Borner, Christoph Dandekar, Thomas Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title | Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title_full | Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title_fullStr | Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title_full_unstemmed | Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title_short | Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection |
title_sort | modeling system states in liver cells: survival, apoptosis and their modifications in response to viral infection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760522/ https://www.ncbi.nlm.nih.gov/pubmed/19772631 http://dx.doi.org/10.1186/1752-0509-3-97 |
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