A0368
Title: Modeling dependence on the superposition of Markov chains: An application to ion channels
Authors: Laura Jula vanegas - University of Goettingen (Germany) [presenting]
Abstract: Hidden Markov Models (HMM) are widely used for modeling temporal data in biostatistics (particularly for Ion channels), among other fields. Recent work has shown that the long-held belief that multiple ion channels in a membrane behave independently is often false. Models for dependence of multivariate Markov chains usually rely on the observation of each individual chain, whereas in our application we only have recordings for the superposition (sum) of the chains. We developed a coupled Hidden Markov Model for multiple dependent Markov chains, where the only information needed comes from the superposition of the chains. The model can explain a wide range of behavior, including negative and positive coupling. Our work shows the presence of negative coupling behavior in RyR2 Channels found in cardiac muscle.
