Biology subjects

Preston, S. P.

Publications and source records attributed to Preston, S. P..

3 recordsLinked to original sources

bioRxiv2024

Optimising experimental designs for model selection of ion channel drug binding mechanisms

The rapid delayed rectifier current carried by the human Ether-a-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help to reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimising experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished. We demonstrate the performance of this methodology via a synthetic data study. If the underlying model of hERG current is known exactly, then the optimised protocols generated show noticeable improvements in our ability to select the true model when compared to a simple protocol used in previous studies. However, if the model is not known exactly, and we assume a discrepancy between the data-generating hERG model and the hERG model used in fitting the models, then the optimised protocols become less effective in determining the true binding dynamics. While the introduced methodology shows promise, we must be careful to ensure that, if applied in a real data study, we have a well-calibrated model of hERG current gating.

pharmacology and toxicology↗

bioRxiv2024

Evaluating the predictive accuracy of ion channel models using data from multiple experimental designs

Mathematical models are increasingly being relied upon to provide quantitatively accurate predictions of cardiac electrophysiology. Many such models concern the behaviour of particular subcellular components (namely, ion channels) which, together, allow the propagation of electrical signals through heart-muscle tissue--namely, the firing of action potentials. In particular, IKr, a voltage-sensitive potassium ion-channel current, is of interest owing to its central pores propensity for blockage by various small molecules. We use newly collected data obtained from an ensemble of voltage-clamp experiments to validate the predictive accuracy of various dynamical models of IKr. To do this, we fit models to each protocol individually, and quantify the error in the resultant model predictions. This allows the comparison of predictive accuracy for IKr models under a diverse collection of previously unexplored dynamics. Our results highlight heterogeneity between parameter estimates obtained from different cells, suggesting the presence of latent effects not yet accounted for in our models. This heterogeneity has a significant impact on our parameter estimates and suggests routes for model improvement.

biophysics↗

bioRxiv2017

Three-dimensional plant architecture and sunlit-shaded dynamics: A stochastic model to enable high-throughput physiological analysis

Background and AimsDiurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterise light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resultant data are complex and high dimensional. This necessitates development of more economical models for summarising the data and for simulating realistic light patterns over the course of a day.\n\nMethodsHigh-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 minute) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from.\n\nKey ResultsFor a wide range of contrasting 3D canopies, the stochastic model is able to summarise, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data.\n\nConclusionsThe model provides a way to summarise highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.

plant biology↗