Bio-Curation for Cellular Signalling: The KAMI Project

  • Russ Harmer
  • Yves-Stan Le Cornec
  • Sébastien Légaré
  • Ievgeniia Oshurko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10545)

Abstract

The general question of what constitutes bio-curation for rule-based modelling of cellular signalling is posed. A general approach to the problem is presented, based on rewriting in hierarchies of graphs, together with a specific instantiation of the methodology that addresses our particular bio-curation problem. The current state of the ongoing development of the KAMI (Knowledge Aggregator & Model Instantiator) bio-curation tool, based on this approach, is detailed along with our plans for future development.

Notes

Acknowledgements

This work was sponsored by the Defense Advanced Research Projects Agency (DARPA) and the U.S. Army Research Office under grant numbers W911NF-14-1-0367 and W911NF-15-1-0544. The views, opinions, and/or findings contained in this report are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.

The first author thanks specially Walter Fontana for many discussions over the years related to this work. Thanks also to Pierre Boutillier, John Bachman and Ben Gyori; and to Adrien Basso-Blandin and Ismaïl Lahkim Bennani who worked on prototypes of KAMI and ReGraph respectively.

References

  1. 1.
    Baldan, P.: Modelling concurrent computations: from contextual Petri nets to graph grammars. Ph.D. thesis, Department of Computer Science, University of Pisa (2000)Google Scholar
  2. 2.
    Basso-Blandin, A., Fontana, W., Harmer, R.: A knowledge representation meta-model for rule-based modelling of signalling networks. EPTCS 204, 47–59 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Corradini, A., Heindel, T., Hermann, F., König, B.: Sesqui-pushout rewriting. In: Corradini, A., Ehrig, H., Montanari, U., Ribeiro, L., Rozenberg, G. (eds.) ICGT 2006. LNCS, vol. 4178, pp. 30–45. Springer, Heidelberg (2006). doi: 10.1007/11841883_4 CrossRefGoogle Scholar
  4. 4.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Hayman, J., Krivine, J., Thompson-Walsh, C., Winskel, G.: Graphs, rewriting and pathway reconstruction for rule-based models. In: Foundations of Software Technology and Theoretical Computer Science (2012)Google Scholar
  5. 5.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling of cellular signalling. In: Caires, L., Vasconcelos, V.T. (eds.) CONCUR 2007. LNCS, vol. 4703, pp. 17–41. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74407-8_3 CrossRefGoogle Scholar
  6. 6.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling and model perturbation. In: Priami, C., Back, R.-J., Petre, I. (eds.) Transactions on Computational Systems Biology XI. LNCS, vol. 5750, pp. 116–137. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04186-0_6 CrossRefGoogle Scholar
  7. 7.
    Danos, V., Harmer, R., Winskel, G.: Constraining rule-based dynamics with types. MSCS 23(2), 272–289 (2013)MathSciNetMATHGoogle Scholar
  8. 8.
    Demir, E., et al.: The BioPAX community standard for pathway data sharing. Nat. Biotechnol. 28(9), 935–942 (2010)CrossRefGoogle Scholar
  9. 9.
    Gerhart, J.: 1998 Warkany lecture: signaling pathways in development. Teratology 60(4), 226–239 (1999)CrossRefGoogle Scholar
  10. 10.
    Gyori, B.M., Bachman, J.A., et al.: From word models to executable models of signaling networks using automated assembly. BioRxiv (2017)Google Scholar
  11. 11.
    Harmer, R.: Rule-based modelling and tunable resolution. EPTCS 9, 65–72 (2009)CrossRefGoogle Scholar
  12. 12.
    Harmer, R.: Rule-Based Meta-modelling for Bio-curation. Habilitation à Diriger des Recherches, ENS Lyon (2017)Google Scholar
  13. 13.
    Harris, L.A., et al.: BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32(21), 3366–3368 (2016)CrossRefGoogle Scholar
  14. 14.
    Janes, K.A., et al.: A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310(5754), 1646–1653 (2005)CrossRefGoogle Scholar
  15. 15.
    Laurent, J.: Causal analysis of rule-based models of signaling pathways. Master’s thesis, École Normale Supérieure, Paris, France (2015)Google Scholar
  16. 16.
    Machado, R., Ribeiro, L., Heckel, R.: Rule-based transformation of graph rewriting rules: towards higher-order graph grammars. Theoret. Comput. Sci. 594, 1–23 (2015)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Mazurkiewicz, A.: Introduction to trace theory. In: The Book of Traces, pp. 3–41 (1995)Google Scholar
  18. 18.
    Molinelli, E.J., et al.: Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput. Biol. 9(12), e1003290 (2013)CrossRefGoogle Scholar
  19. 19.
    Nelander, S., et al.: Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4(1), 216 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Russ Harmer
    • 1
  • Yves-Stan Le Cornec
    • 1
  • Sébastien Légaré
    • 1
  • Ievgeniia Oshurko
    • 1
  1. 1.Université de Lyon, CNRS – ENS Lyon – Université Claude Bernard Lyon 1, LIPLyonFrance

Personalised recommendations