Pei Ye

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We present an approach to modeling complex biological systems that is based on Hybrid automata (HA). HA combine discrete transition graphs with continuous dynamics. Our goal is to efficiently capture the behavior of excitable cells previously modeled by systems of nonlin-ear differential equations. In particular, we derive HA models from the Hodgkin-Huxley(More)
We show how to automatically learn the class of Hybrid Au-tomata called Cycle-Linear Hybrid Automata (CLHA) in order to model the behavior of excitable cells. Such cells, whose main purpose is to amplify and propagate an electrical signal known as the action potential (AP), serve as the " biologic transistors " of living organisms. The learning algorithm we(More)
We present the Piecewise Linear Approximation Model of Ion Channel contribution (PLAMIC) to cardiac excitation. We use the PLAMIC model to conduct formal analysis of cardiac arrhythmic events, namely Early Afterdepolarizations (EADs). The goal is to quantify (for the first time) the contribution of the overall sodium (N a +), potassium (K +) and calcium (Ca(More)
—We present a novel approach to investigating key behavioral properties of complex biological systems by first using automated techniques to learn a simplified Linear Hybrid Automaton model of the system under investigation, and then carrying out automatic reachability analysis on the resulting model. The specific biological system we consider is the(More)
BACKGROUND Cigarette smoke a recognized risk factor for many systemic diseases and also oral diseases. Human beta defensins (HBDs), a group of important antimicrobial peptides expressed by the epithelium, are crucial for local defense and tissue homeostasis of oral cavity. The aim of this study was to evaluate potential effects of whole cigarette smoke(More)
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