A search for good multiple recursive random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An Efficient Method for Modeling Kinetic Behavior of Channel Proteins in Cardiomyocytes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Markov modeling of conformational kinetics of cardiac ion channels is a prospective means to correlate the molecular defects of channel proteins to their electrophysiological dysfunction. However, both the identifiability of the microscopic conformations and the estimation of the transition rates are challenging. In this paper, we present a new method in which the distribution space of the time constants of exponential components of mathematical models are searched as an alternative to the consideration of transition rates. Transition rate patterns were defined and quasi random seed sequences for each pattern were generated by using a multiple recursive generator algorithm. Cluster-wide Monte Carlo simulation was performed to investigate various schemes of Markov models. It was found that by increasing the number of closed conformations the time constants were shifted to larger magnitudes. With the inclusion of inactivation conformation the time distribution was altered depending on the topology of the schemes. Further results demonstrated the stability of the morphology of time distributions. Our study provides the statistical evaluation of the time constant space of Markov schemes. The method facilities the identification of the underlying models and the estimation of parameters, hence is proposed for use in investigating the functional consequences of defective genes responsible for ion channel diseases.