Shuttleworth, J. G. | bioRxiv Science

bioRxiv2026

A general mathematical framework for modelling subnetworks of the nuclear auxin pathway

Auxins are a family of plant hormones involved in various processes across plant tissues and species. The Nuclear Auxin Pathway (NAP) consists of interacting transcription factors (ARFs) and repressors (Aux/IAAs), which govern an individual cell's response to changes in auxin concentration. These components are present in all land plants, and many species possess multiple copies of each signalling component. We present a general framework for ODE-based models of NAP submodules with the flexibility to model the promotion and repression of target genes by any combination of transcriptional regulators. We analyse published data and show that auxin treatment in Arabidopsis thaliana roots triggers a range of characteristically distinct temporal response profiles-for both target genes and the signalling components themselves. Using our modelling framework, we recapitulate aspects of this behaviour by presenting examples of real and theoretical NAP subnetworks, and by analysing the effect that these network dynamics have on auxin-mediated transcriptional responses. This work demonstrates the utility of our modelling framework as a general-purpose tool for understanding the function of certain protein-protein and protein-DNA interactions through their effects on the NAP. This exploration of the rich dynamics of more complex signalling pathways promises to advance our understanding of the NAP.

plant biology↗

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↗