From average transient transporter currents to microscopic mechanism -- A Bayesian analysis

Abstract

Electrophysiology studies of secondary active transporters have revealed quantitative, mechanistic insights over many decades of research. However, the emergence of new experimental and analysis approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with > 10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide selection of experimental conditions, and (iii) the ability of SSME data to distinguish among mechanisms. When the general mechanism - event order - is known in advance, we show that a subset of kinetic parameters can be "practically identified" within[~] 1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting additional experiments are required to fully characterize parameters. We additionally demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular, but subtly challenging, Bayesian calculations for structural and systems biology.