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Learning and detecting emergent behavior in networks of cardiac myocytes

Learning and detecting emergent behavior in networks of cardiac myocytes,10.1145/1467247.1467271,Communications of The ACM,Radu Grosu,Scott A. Smolka,

Learning and detecting emergent behavior in networks of cardiac myocytes   (Citations: 2)
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Abstract. We address the problem of specifying and detecting emer- gent 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 hy- brid 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 imple- mented 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 eectiveness,of our ap- proach by applying Emerald,to the scalar electrical fields produced by our CellExcite simulator.
Journal: Communications of The ACM - CACM , vol. 52, no. 3, pp. 97-105, 2009
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    • ...our work in [26,13]), this paper presents, to the best of our knowledge, the first approach for automatically identifying parameter ranges of a biologically-relevant cardiac model, guaranteeing that the model accurately reproduces a particular cardiac disorder...

    Radu Grosuet al. From Cardiac Cells to Genetic Regulatory Networks

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