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Analysing Cell Line Specific EGFR Signalling via Optimized Automata Based Model Checking

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Book cover Computational Methods in Systems Biology (CMSB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9308))

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Abstract

Building models with a high degree of specificity, e.g. for particular cell lines, is becoming an important tool in the advancement towards personalised medicine. Constraint-based modelling approaches allow for utilizing general system knowledge to generate a set of possible models that can be further filtered with more specific data. Here, we exploit such an approach in a Boolean modelling framework to investigate EGFR signalling for different cancer cell lines, motivated by a study from Klinger et al. [8]. To optimize performance of the underlying model checking procedure, we present a number of constraint encodings tailored to describing common data types and experimental set-ups. This results in a significant increase in the performance of the approach.

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Notes

  1. 1.

    The tool used here, called TREMPPI, is available in a development version at github.com/xstreck1/TREMPPI and is expected to be fully released in 2015.

  2. 2.

    For spatial reasons, only samples from the results are provided in the article. All the data are listed in the supplementary archive or at dibimath.github.io/CMSB_2015.

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Author information

Authors and Affiliations

  1. Freie Universität Berlin, Berlin, Germany

    Adam Streck, Kirsten Thobe & Heike Siebert

Authors
  1. Adam Streck
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  2. Kirsten Thobe
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  3. Heike Siebert
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Corresponding author

Correspondence to Adam Streck .

Editor information

Editors and Affiliations

  1. École Centrale de Nantes, Nantes, France

    Olivier Roux

  2. Université de Nantes, Nantes, France

    Jérémie Bourdon

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© 2015 Springer International Publishing Switzerland

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Streck, A., Thobe, K., Siebert, H. (2015). Analysing Cell Line Specific EGFR Signalling via Optimized Automata Based Model Checking. In: Roux, O., Bourdon, J. (eds) Computational Methods in Systems Biology. CMSB 2015. Lecture Notes in Computer Science(), vol 9308. Springer, Cham. https://doi.org/10.1007/978-3-319-23401-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-23401-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23400-7

  • Online ISBN: 978-3-319-23401-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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