Advertisement

Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes

  • R. Grosu
  • E. Bartocci
  • F. Corradini
  • E. Entcheva
  • S. A. Smolka
  • A. Wasilewska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4981)

Abstract

We address the problem of specifying and detecting emergent behavior in networks of cardiac myocytes, spiral electric waves in particular, a precursor to atrial and ventricular fibrillation. To solve this problem we: (1) Apply discrete mode-abstraction to the cycle-linear hybrid automata (clha) we have recently developed for modeling the behavior of myocyte networks; (2) Introduce the new concept of spatial-superposition of clha modes; (3) Develop a new spatial logic, based on spatial-superposition, for specifying emergent behavior; (4) Devise a new method for learning the formulae of this logic from the spatial patterns under investigation; and (5) Apply bounded model checking to detect (within milliseconds) the onset of spiral waves. We have implemented our methodology as the Emerald tool-suite, a component of our eha framework for specification, simulation, analysis and control of excitable hybrid automata. We illustrate the effectiveness of our approach by applying Emerald to the scalar electrical fields produced by our CellExcite simulator.

Keywords

Spiral Wave Probability Mass Function Excitable Cell Atomic Proposition Kripke Structure 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aiello, M., Benthem, J., Bezhanishvili, G.: Reasoning about space: The modal way. J. Log. Comput. 13(6), 889–920 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Bartocci, E., Corradini, F., Entcheva, E., Grosu, R., Smolka, S.A.: CellExcite: A tool for simulating in-silico excitable cells. BMC Bioinformatics (to appear, 2007)Google Scholar
  3. 3.
    Biere, A., Cimatti, A., Clarke, E., Strichman, O., Zhu, Y.: Bounded model checking. In: Adv. in Comp., Highly Depend. Software, vol. 58, Academic Press, London (2003)Google Scholar
  4. 4.
    Bray, M.A., Lin, S.F., Aliev, R.R., Roth, B.J., Wikswo, J.P.J.: Experimental and theoretical analysis of phase singularity dynamics in cardiac tissue. J Cardiovasc Electrophysiol 12(6), 716–722 (2001)CrossRefGoogle Scholar
  5. 5.
    Caires, L., Cardelli, L.: A spatial logic for concurrency (part I). Inf. Comput. 186(2), 194–235 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Caires, L., Cardelli, L.: A spatial logic for concurrency (part II. Theor. Comput. Sci. 322(3), 517–565 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    deOliveira, I., Cugnasca, P.: Checking safe trajectories of aircraft using hybrid automata. In: Anderson, S., Bologna, S., Felici, M. (eds.) SAFECOMP 2002. LNCS, vol. 2434, Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Frank, E., Hall, M.A., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I.H., Trigg, L.: WEKA – a machine learning workbench for data mining. In: The Data Mining and Knowledge Discovery Handbook, pp. 1305–1314. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ghosh, R., Tiwari, A., Tomlin, C.: Automated symbolic reachability analysis; with application to delta-notch signaling automata. In: Maler, O., Pnueli, A. (eds.) HSCC 2003. LNCS, vol. 2623, pp. 233–248. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Grosu, R., Bartocci, E., Corradini, F., Entcheva, E., Smolka, S., True, M., Wasilewska, A., Ye, P.: EHA: An environment for the specification, simulation, analysis and control of networks of excitable hybrid automata (2007), http://www.cs.sunysb.edu/~eha
  11. 11.
    Grosu, R., Mitra, S., Ye, P., Entcheva, E., Ramakrishnan, I.V., Smolka, S.A.: Learning cycle-linear hybrid automata for excitable cells. In: Bemporad, A., Bicchi, A., Buttazzo, G. (eds.) HSCC 2007. LNCS, vol. 4416, pp. 245–258. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Kwon, Y., Agha, G.: Scalable modeling and performance evaluation of wireless sensor networks. In: IEEE RT Tech. and App. Symp., pp. 49–58 (2006)Google Scholar
  13. 13.
    Lu, Y.: Concept hierarchy in data mining: Specification, generation and implementation. Master’s thesis, Simon Fraser University (December 1997)Google Scholar
  14. 14.
    Pereira, F.L., deSousa, J.B.: Coordinated control of networked vehicles: An autonomous underwater system. Aut. and Remote Ctrl. 65(7), 1037–1045 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    Shusterman, E., Feder, M.: Image compression via improved quadtree decomposition algorithms. IEEE Trans. on Image Processing 3(2), 207–215 (1994)CrossRefGoogle Scholar
  16. 16.
    Umeno, S., Lynch, N.: Safety verification of an aircraft landing protocol: A refinement approach. In: Bemporad, A., Bicchi, A., Buttazzo, G. (eds.) HSCC 2007. LNCS, vol. 4416, Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Wasilewska, A., Ruiz, E.M.: A classification model: Syntax and semantics for classification. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 59–68. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Wedge, N.A., Branicky, M.S., Cavusoglu, M.C.: Computationally efficient cardiac biolectricity models toward whole-heart simulation. In: Proc.of Intl. Conf. IEEE Engineering in Medicine and Biology Society, pp. 1–4 (2004)Google Scholar
  19. 19.
    Ye, P., Entcheva, E., Grosu, R., Smolka, S.: Efficient modeling of excitable cells using hybrid automata. In: Proc. of CMSB 2005, the 3rd Workshop on Computational Methods in Systems Biology, Edinburgh, Scotland, pp. 216–227 (April 2005)Google Scholar
  20. 20.
    Ye, P., Entcheva, E., Smolka, S., Grosu, R.: A cycle-linear hybrid-automata model for excitable cells. The IET J. of Systems Biology (SYB) (accepted, 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • R. Grosu
    • 1
  • E. Bartocci
    • 1
    • 2
  • F. Corradini
    • 2
  • E. Entcheva
    • 3
  • S. A. Smolka
    • 1
  • A. Wasilewska
    • 1
  1. 1.Department of Computer ScienceStony Brook UniversityStony BrookUSA
  2. 2.Department of Mathematics and Computer ScienceUniversity of CamerinoCamerino (MC)Italy
  3. 3.Department of Biomedical EngineeringStony Brook UniversityStony BrookUSA

Personalised recommendations