Abstract
Pathway Logic (PL) is a general system for modeling signal transduction and other cellular processes with the objective of understanding how cells work. Each specific model system builds on a knowledge base of rules formalizing local process steps such as post translational modification. The Pathway Logic Assistant (PLA) is a collection of visualization and reasoning tools that allow users to derive specific executable models by specifying of an initial state. The resulting network of rule instances describes possible behaviors of the modelled system. Subnets and pathways can then be computed (they are not hard wired) by specifying states to reach and/or to avoid. The STM knowledge base is a curated collection of signal transduction rules supported by experimental evidence. In this paper we describe methods for using the PL STM knowledge base and the PLA tools to explain observed perturbations of signaling pathways when cells are treated with drugs targeting specific activities or protein states. We also explore ideas for conjecturing targets of unknown drugs. We illustrate the methods on phosphoproteomics data (RPPA) from SKMEL133 melanoma cancer cells treated with different drugs targeting components of cancer signaling pathways. Existing curated knowledge allowed to us explain many of the responses. Conflicts between the STM model predictions and the data suggest missing requirements for rules to apply.
The work was partially supported by funding from the DARPA Big Mechansim program. The authors would like to thank the PL team for their many contributions, and the anonymous reviewers for helpful criticisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
One can knockout an occurrence, either from the initial state or a potentially reachable occurrence, or a rule. Each choice corresponds to a different experimental perturbation.
- 2.
Although in printed form the node labels are not readable, zooming in with a pdf reader reveals all the details.
References
Aksoy, B.A.: BioPax prior information provider (PERA) (2017). http://bit.ly/bp_prior. Accessed 26 Apr 2017
M.D. Anderson Cancer Center: Antibody information and protocols (2017). https://www.mdanderson.org/research/research-resources/core-facilities/functional-proteomics-rppa-core/antibody-information-and-protocols.html. Accessed 25 Apr 2017
M.D. Anderson Cancer Center: RPPA core facility (2017). https://www.mdanderson.org/research/research-resources/core-facilities/functional-proteomics-rppa-core.html. Accessed 25 Apr 2017
Clavel, M., Durán, F., Eker, S., Lincoln, P., MartÃ-Oliet, N., Meseguer, J., Talcott, C.: All About Maude - A High-Performance Logical Framework. LNCS, vol. 4350. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71999-1
Dienstmann, R., Rodon, J., Serra, V., Tabernero, J.: Picking the point of inhibition: a comparative review of PI3K/AKT/mTOR pathway inhibitors. Mol. Cancer Ther. 13(5), 1021–1031 (2014)
Donaldson, R., Talcott, C., Knapp, M., Calder, M.: Understanding signalling networks as collections of signal transduction pathways. In: Computational Methods in Systems Biology (2010)
Fisher, J., Henzinger, T.A.: Executable cell biology. Nat. Biotechnol. 25(11), 1239–1249 (2007)
Gujral, T.S., Karp, R.L., Finski, A., Chan, M., Schwartz, P.E., MacBeath, G., Sorger, P.: Profiling phospho-signaling networks in breast cancer using reverse-phase protein arrays. Oncogene 32(29), 3470–3476 (2013)
Hill, S.M., et al.: Inferring causal molecular networks: empirical assessment through a community-based effort. Nat. Methods 13(4), 310–318 (2016)
Korkut, A., Wang, W., Demir, E., Aksoy, B.A., Jing, X., Molinelli, E.J., Babur, O., Bemis, D.L., Onur, S.S., Solit, D.B., Pratilas, C.A., Sander, C.: Perturbation biol- ogy nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells. Elife 18(4) (2015). Article id 26284497
Logie, L., Ruiz-Alcaraz, A.J., Keane, M., Woods, Y.L., Bain, J., Marquez, R., Alessi, D.R., Sutherland, C.: Characterization of a protein kinase B inhibitor in vitro and in insulin-treated liver cells. Diabetes 56(9), 2218–2227 (2007)
Nigam, V., Donaldson, R., Knapp, M., McCarthy, T., Talcott, C.: Inferring executable models from formalized experimental evidence. In: Roux, O., Bourdon, J. (eds.) CMSB 2015. LNCS, vol. 9308, pp. 90–103. Springer, Cham (2015). doi:10.1007/978-3-319-23401-4_9
Pathway Logic (2016). http://pl.csl.sri.com. Accessed 10 Jan 2016
Samatar, A.A., Poulikakos, P.I.: Targeting RAS-ERK signalling in cancer: promises and challenges. Nat. Rev. Drug Discov. 13(12), 928–942 (2014)
Schmidt, K.: LoLA a low level analyser. In: Nielsen, M., Simpson, D. (eds.) ICATPN 2000. LNCS, vol. 1825, pp. 465–474. Springer, Heidelberg (2000). doi:10.1007/3-540-44988-4_27
Talcott, C.: The pathway logic formal modeling system: diverse views of a formal representation of signal transduction. In: Wang, Q. (ed.) Workshop on Formal Methods in Bioinformatics and Biomedicine (2016)
Tsai, J., et al.: Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity. Proc. Natl. Acad. Sci. USA 105(8), 3041–3046 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Talcott, C., Knapp, M. (2017). Explaining Response to Drugs Using Pathway Logic. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-67471-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67470-4
Online ISBN: 978-3-319-67471-1
eBook Packages: Computer ScienceComputer Science (R0)