Vijay Rajagopal

The paper uses a finite element model informed by myofibril orientations in sheep cardiomyocytes to show that myofibril networks generate unique shear stresses during contraction.

The paper extends bond graph methodology to model chemomechanical transduction in actin filament polymerisation, showing it matches the Brownian ratchet approach and can model flexibility and compliance.

BondGraphs.jl provides a Julia framework for energy-based modelling in systems biology, enforcing thermodynamic constraints through bond graphs.

CardioVinci uses deep learning to automate the analysis of 3D cardiac ultrastructure changes from large EM imaging datasets.

EM-net is a deep learning model for electron microscopy image segmentation that outperforms existing methods with smaller ground-truth datasets.

EM-stellar benchmarks deep learning methods for EM image segmentation, showing performance varies with image properties and no method consistently outperforms others.

This study uses a mathematical model to test if altering mitochondrial fission/fusion rates affects bioenergetics in cardiomyocytes, finding that bioenergetics are robust to such changes.

This paper proposes a mechanochemical model showing how membrane tension helps migrating cells adapt to varying chemoattractant levels by inhibiting GTPase signaling and maintaining polarity.

The paper uses computational models to show that mitochondrial arrangement affects metabolite distribution and force dynamics in cardiac cells, with implications under hypoxia.