%0 Journal Article %1 Philippi2009Modeling %A Philippi, Nicole %A Walter, Dorothee %A Schlatter, Rebekka %A Ferreira, Karine %A Ederer, Michael %A Sawodny, Oliver %A Timmer, Jens %A Borner, Christoph %A Dandekar, Thomas %D 2009 %I BioMed Central %J BMC Systems Biology %K boolean-networks liver modelling %N 1 %P 1--11 %R 10.1186/1752-0509-3-97 %T Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection %U http://dx.doi.org/10.1186/1752-0509-3-97 %V 3 %X 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.

@article{Philippi2009Modeling, abstract = {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.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Philippi, Nicole and Walter, Dorothee and Schlatter, Rebekka and Ferreira, Karine and Ederer, Michael and Sawodny, Oliver and Timmer, Jens and Borner, Christoph and Dandekar, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2188f15288f590c559f4ef1d0cc6b2fa6/karthikraman}, citeulike-article-id = {5826786}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1752-0509-3-97}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19772631}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19772631}, citeulike-linkout-3 = {http://www.springerlink.com/content/g457677807085150}, day = 1, doi = {10.1186/1752-0509-3-97}, interhash = {6c083d01a882447857dcf3252349ca62}, intrahash = {188f15288f590c559f4ef1d0cc6b2fa6}, issn = {1752-0509}, journal = {BMC Systems Biology}, keywords = {boolean-networks liver modelling}, month = dec, number = 1, pages = {1--11}, pmid = {19772631}, posted-at = {2011-02-07 12:44:20}, priority = {2}, publisher = {BioMed Central}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Modeling system states in liver cells: Survival, apoptosis and their modifications in response to viral infection}, url = {http://dx.doi.org/10.1186/1752-0509-3-97}, volume = 3, year = 2009 }