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  Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment

Wierling, C., Kühn, A., Hache, H., Daskalaki, A., Maschke-Dutz, E., Peycheva, S., et al. (2012). Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment. Mutation Research-Genetic Toxixology and Environmental Mutagenesis, 746(2), 163-170. doi:10.1016/j.mrgentox.2012.01.005.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-F0B7-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-F0B8-D
Genre: Journal Article

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 Creators:
Wierling, Christoph1, Author              
Kühn, Alexander1, Author              
Hache, Hendrik1, Author              
Daskalaki, Andriani1, Author              
Maschke-Dutz, Elisabeth2, Author              
Peycheva, Svetlana, Author
Li, Jian, Author
Herwig, Ralf3, Author              
Lehrach, Hans2, Author              
Affiliations:
1Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, Berlin, Germany, ou_1479656              
2Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              
3Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479648              

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Free keywords: Computer Simulation Epidermal Growth Factor/metabolism Humans *Monte Carlo Method Neoplasms/*therapy Protein Kinase Inhibitors/therapeutic use Signal Transduction Systems Biology/*methods
 Abstract: Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.

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 Dates: 2012-01-232012-08-15
 Publication Status: Published in print
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.mrgentox.2012.01.005
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Title: Mutation Research-Genetic Toxixology and Environmental Mutagenesis
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Source Genre: Journal
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Publ. Info: New York, NY : Elsevier
Pages: - Volume / Issue: 746 (2) Sequence Number: - Start / End Page: 163 - 170 Identifier: ISSN: 1383-5718
CoNE: https://pure.mpg.de/cone/journals/resource/111019684377020