How precisely can transporter mechanisms be determined from electrophysiology data? A Bayesian investigation

Membrane transporter proteins perform the critical task of moving nutrients, ions, and potentially toxic molecules across a membrane and have biomedical significance as drug targets and biomarkers. The mechanisms of these proteins are not fully understood, in part due to the difficulty in directly measuring the individual biochemical reaction steps and also because of the possibility of multiple pathways. Solid-supported membrane (SSM)-based electrophysiology methods have recently emerged as an efficient means of collecting aggregate ion flux data from transporter proteins under various conditions. However, it is unknown how much information is contained within these datasets and how well the relevant biophysical parameters can be estimated. Here we explore a Bayesian pipeline using sequential Monte Carlo sampling to estimate kinetic parameter distributions using synthetic data motivated by SSM-based electrophysiology experiments for a 1:1 transporter: the distribution widths directly characterize the degree to which parameters are ‘identifiable.’ Based on assumed models of varying complexity, we examine the effects of different experimental conditions and combinations of experiments on parameter estimation and uncertainty. We hope a Bayesian pipeline can be used to aid the design of experiments for transporter mechanism discovery.