Fluid transport in epithelia is driven by ion transport. Markov models have been used to fit the statistical properties of ion channel data such as single channel recordings. Surprisingly, these data have not been used to learn about the physical properties of ion channel proteins. I will review how nonlinear methods of analyzing single channel data, such as testing for long term correlations, non-normalizable probability density functions of open and closed time distributions, and phase space analysis can provide information about the physical properties about ion channels such as their energy structure, time dependent dynamics, and the relative role of random thermal vs. deterministic atomic, electrostatic, and hydrophobic forces in switching channels from one state to another.

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1998-1999 Mathematics in Biology