Advertisement

Bringing LTL Model Checking to Biologists

  • Zara Ahmed
  • David Benque
  • Sergey Berezin
  • Anna Caroline E. Dahl
  • Jasmin FisherEmail author
  • Benjamin A. Hall
  • Samin Ishtiaq
  • Jay Nanavati
  • Nir Piterman
  • Maik Riechert
  • Nikita Skoblov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10145)

Abstract

The BioModelAnalyzer (BMA) is a web based tool for the development of discrete models of biological systems. Through a graphical user interface, it allows rapid development of complex models of gene and protein interaction networks and stability analysis without requiring users to be proficient computer programmers. Whilst stability is a useful specification for testing many systems, testing temporal specifications in BMA presently requires the user to perform simulations. Here we describe the LTL module, which includes a graphical and natural language interfaces to testing LTL queries. The graphical interface allows for graphical construction of the queries and presents results visually in keeping with the current style of BMA. The Natural language interface complements the graphical interface by allowing a gentler introduction to formal logic and exposing educational resources.

Keywords

Model Check Graphical User Interface Operator Precedence Bound Model Check Conversational Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bonzanni, N., Garg, A., Feenstra, K.A., Schütte, J., Kinston, S., Miranda-Saavedra, D., Heringa, J., Xenarios, I., Göttgens, B.: Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model. Bioinformatics 29, i80–i88 (2013)CrossRefGoogle Scholar
  2. 2.
    Chuang, R., Hall, B., Benque, D., Cook, B., Ishtiaq, S., Piterman, N., Taylor, A., Vardi, M., Koschmieder, S., Gottgens, B., Fisher, J.: Drug target optimization in chronic myeloid leukemia using innovative computational platform. Sci. Rep. 5, 8190 (2015)Google Scholar
  3. 3.
    Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, J., Tanaka, Y., Wilkinson, A., Buettner, F., Nishikawa, S., Piterman, N., Kouskoff, V., Theis, F., Fisher, J., Gottgens, B.: Decoding the regulatory network of early blood development from single cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015)CrossRefGoogle Scholar
  4. 4.
    Remy, E., Rebouissou, S., Chaouiya, C., Zinovyev, A., Radvanyi, F., Calzone, L.: A modeling approach to explain mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis. Cancer Res. 75, 4042–4052 (2015)CrossRefGoogle Scholar
  5. 5.
    Terfve, C.D.A., Wilkes, E.H., Casado, P., Cutillas, P.R., Saez-Rodriguez, J.: Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nat. Commun. 6, 8033 (2015)CrossRefGoogle Scholar
  6. 6.
    Benque, D., et al.: BMA: visual tool for modeling and analyzing biological networks. In: Madhusudan, P., Seshia, S.A. (eds.) CAV 2012. LNCS, vol. 7358, pp. 686–692. Springer, Berlin (2012). doi: 10.1007/978-3-642-31424-7_50 CrossRefGoogle Scholar
  7. 7.
    Naldi, A., Thieffry, D., Chaouiya, C.: Decision diagrams for the representation and analysis of logical models of genetic networks. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS, vol. 4695, pp. 233–247. Springer, Berlin (2007). doi: 10.1007/978-3-540-75140-3_16 CrossRefGoogle Scholar
  8. 8.
    Cook, B., Fisher, J., Krepska, E., Piterman, N.: Proving stabilization of biological systems. In: Jhala, R., Schmidt, D. (eds.) VMCAI 2011. LNCS, vol. 6538, pp. 134–149. Springer, Berlin (2011). doi: 10.1007/978-3-642-18275-4_11 CrossRefGoogle Scholar
  9. 9.
    Cook, B., Fisher, J., Hall, B.A., Ishtiaq, S., Juniwal, G., Piterman, N.: Finding instability in biological models. In: Biere, A., Bloem, R. (eds.) CAV 2014. LNCS, vol. 8559, pp. 358–372. Springer, Cham (2014). doi: 10.1007/978-3-319-08867-9_24 Google Scholar
  10. 10.
    Claessen, K., Fisher, J., Ishtiaq, S., Piterman, N., Wang, Q.: Model-checking signal transduction networks through decreasing reachability sets. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 85–100. Springer, Berlin (2013). doi: 10.1007/978-3-642-39799-8_5 CrossRefGoogle Scholar
  11. 11.
    Schaub, M., Henzinger, T., Fisher, J.: Qualitative networks: a symbolic approach to analyze biological signaling networks. BMC Syst. Biol. 1, 4 (2007)CrossRefGoogle Scholar
  12. 12.
    Bean, D., Heimbach, J., Ficorella, L., Micklem, G., Oliver, S., Favrin, G.: esyN: Network building, sharing, and publishing. PLoS ONE 9, e106035 (2014)CrossRefGoogle Scholar
  13. 13.
    Microsoft: Microsoft bot framework (2016). https://dev.botframework.com/
  14. 14.
    Microsoft: Microsoft cognitive services (2016). https://www.microsoft.com/cognitive-services/
  15. 15.
    SAP: Chevrotain a JavaScript parsing DSL (2014). https://github.com/SAP/chevrotain/
  16. 16.
    van der Plas, L., Tiedemann, J.: Finding synonyms using automatic word alignment and measures of distributional similarity. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions. COLING-ACL 2006, Stroudsburg, PA, USA, pp. 866–873. Association for Computational Linguistics (2006)Google Scholar
  17. 17.
    Richardson, R., Smeaton, A., Murphy, J.: Using wordnet as a knowledge base for measuring semantic similarity between words (1994)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zara Ahmed
    • 1
  • David Benque
    • 2
  • Sergey Berezin
    • 3
  • Anna Caroline E. Dahl
    • 4
  • Jasmin Fisher
    • 1
    • 5
    Email author
  • Benjamin A. Hall
    • 6
  • Samin Ishtiaq
    • 1
  • Jay Nanavati
    • 1
  • Nir Piterman
    • 7
  • Maik Riechert
    • 1
  • Nikita Skoblov
    • 3
  1. 1.Microsoft ResearchCambridgeUK
  2. 2.Royal College of ArtLondonUK
  3. 3.Moscow State UniversityMoscowRussia
  4. 4.Center for Technology in Medicine and Health, KTH Royal Institute of TechnologyHuddingeSweden
  5. 5.Department of BiochemistryUniversity of CambridgeCambridgeUK
  6. 6.MRC Cancer UnitUniversity of CambridgeCambridgeUK
  7. 7.University of LeicesterLeicesterUK

Personalised recommendations