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.
Keywords
- Boolean Networks
- Model checking
- EGFR
- Cancer
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- 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.
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.
References
Baier, C., Katoen, J.-P.: Principles of Model Checking. The MIT Press, Cambridge (2008)
Feng, Z., Levine, A.J.: The regulation of energy metabolism and the igf-1/mtor pathways by the p53 protein. Trends Cell Biol. 20(7), 427–434 (2010)
Gallet, E., Manceny, M., Le Gall, P., Ballarini, P.: An LTL model checking approach for biological parameter inference. In: Merz, S., Pang, J. (eds.) ICFEM 2014. LNCS, vol. 8829, pp. 155–170. Springer, Heidelberg (2014)
Guziolowski, C., Videla, S., Eduati, F., Thiele, S., Cokelaer, T., Siegel, A., Saez-Rodriguez, J.: Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics 29, 2320–2326 (2013)
Huth, M., Ryan, M.: Logic in Computer Science: Modelling and reasoning about systems. Cambridge University Press, Cambridge (2004)
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)
Klarner, H.: Contributions to the Analysis of Qualitative Models of Regulatory Networks. Ph.D. thesis, Freie Universität Berlin, Germany (2015)
Klinger, B., Sieber, A., Fritsche-Guenther, R., Witzel, F., Berry, L., Schumacher, D., Yan, Y., Durek, P., Merchant, M., Schäfer, R., et al.: Network quantification of EGFR signaling unveils potential for targeted combination therapy. Mol. Syst. Biol. 9(1), 673 (2013)
Rozengurt, E., Soares, H.P., Sinnet-Smith, J.: Suppression of feedback loops mediated by pi3k/mtor induces multiple overactivation of compensatory pathways: An unintended consequence leading to drug resistance. Mol. Cancer Ther. 13(11), 2477–2488 (2014)
Samaga, R., Saez-Rodriguez, J., Alexopoulos, L.G., Sorger, P.K., Klamt, S.: The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput. Biol. 5(8), e1000438 (2009)
Tanti, J.-F., Jager, J.: Cellular mechanisms of insulin resistance: role of stress-regulated serine kinases and insulin receptor substrates (irs) serine phosphorylation. Curr. Opin. Pharmacol. 9(6), 753–762 (2009)
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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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