Andrew D. McCulloch

Virtual population studies and patient-specific cardiac digital twins require ensembles of hundreds of thousands of monodomain simulations, yet a single high-fidelity simulation can still take hours on modern HPC systems. We present Monolite, a lightweight finite-element monodomain solver implemented in C with CUDA offloading, designed to maximize simulation throughput on single-node multicore CPUs and GPUs while retaining support for anatomically realistic unstructured meshes with orthotropic fiber orientations. Monolite uses linear tetrahedral (P1) elements and reformulates right-hand-side vector assembly from repeated element-wise integration into precomputed sparse matrix-vector operations that merge the mass and stiffness matrices and restrict the source term to the stimulated region. Reverse Cuthill–McKee reordering of both vertices and elements improves memory locality, while a Structure-of-Arrays layout combined with lookup tables enables SIMD-vectorized ODE integration. Accuracy was verified against the Niederer benchmark, and performance was assessed on idealized (~11.2M degrees of freedom) and anatomically realistic (~2.2M) models using up to 256 CPU cores and an NVIDIA GH200 GPU. Lookup tables and SIMD vectorization accelerate ODE integration by a factor of 15.8, and the reformulated kernels become memory-traffic-bound, with the sparse matrix-vector kernels sustaining approximately 890~GB/s on an AMD EPYC 9745 node, comparable to the measured STREAM bandwidth. Under identical numerical settings and verified equal conjugate-gradient iteration counts, Monolite reduces ionic-model execution time relative to openCARP at every core count tested, and diffusion-solver time in most configurations. These results show that architecture-aware implementation alone can substantially raise the throughput of finite-element monodomain simulation without altering the underlying discretization.

Exercise stimulates skeletal muscle signalling and mitochondrial metabolism. Emerging evidence shows that mitochondrial dynamics (i.e. fission and fusion) could be regulated by exercise. Yet, key gaps remain in identifying (i) the signals that drive fission vs . fusion; (ii) how energy status and reactive oxygen species (ROS) shift control between dynamin‐related protein 1 (DRP1) and mitofusin (MFN)/optic atrophy 1 (OPA1); and (iii) which intensity–duration combinations yield similar cytosolic signals but different mitochondrial remodelling. Therefore, we developed an integrative computational framework connecting exercise regimens to mitochondria fission–fusion machinery by linking blood–myofibre energetics in cytosol and mitochondria to signalling pathways. The influence of sprint, resistance and endurance exercise regimens on mitochondrial fission and fusion has been simulated. Classified qualitative validation of the signalling network model achieved 80% accuracy. The model predicts regimen‐specific dynamics starting with an acute DRP1‐driven fission during exercise followed by MFN1/2–OPA1‐mediated re‐fusion as energy stress declines, consistent with a cyclical triage‐then‐rebuild paradigm. Changes are most pronounced and sustained with endurance, sharp but brief with sprint, and minimal with resistance. Global sensitivity analysis identified AMP‐activated protein kinase (AMPK)/peroxisome proliferator‐activated receptor gamma coactivator‐1α→MFN1/2 as dominant fusion drivers, ROS and AMPK→mitochondrial fission factor/DRP1 as primary fission switches, and Ca ^{2 +} –calmodulin, extracellular‐signal‐regulated kinase and liver kinase B1/AMPK as shared regulators. The model predicts that an endurance base, augmented with one or two weekly high intensity interval training/sprint interval training sessions could maximize AMPK–ROS pulses and mitochondrial fission–fusion. This framework unifies muscle's signalling logic with energetic state to explain how intensity–volume combinations, bout spacing and kinase modulation tune mitochondrial remodelling, yielding testable predictions for optimizing training and adjuvant therapies to enhance mitochondrial quality and performance. image Key points: Different exercise regimes such as sprint, resistance, and endurance can trigger different signalling pathways. Exercise also triggers mitochondrial remodelling in skeletal muscle. Using a systems biology model, we developed a systems biology model for skeletal muscle signalling and mitochondrial metabolism for exercise. Our model predicts the dynamics of mitochondrial fusion and fission in different exercise regimes and identifies which signalling pathways dominassste these remodelling mechanisms.

Here we use a statistical atlas of end-diastolic (ED) and end-systolic (ES) biventricular shapes- previously derived from the UK Biobank imaging substudy- to generate meshes for finite element (FE) simulations of ventricular wall mechanics. The models used the Holzapfel-Ogden constitutive law for passive material properties and a time-varying elastance model of systolic tension development. Simulated ED and ES deformations were projected onto the shape atlas and the principal components were used to train a multi-layer perceptron as a surrogate model. The input layer included shape modes of the unloaded ventricular geometry, and material parameters and ventricular pressures at ED and ES. After training with 444 simulations, the surrogate model achieved a mean square error in predicted displacements of < 2 mm and volumetric overlaps with FE-predicted deformed shapes > 97%, demonstrating good fidelity to the simulated ground truth. This approach may enable accurate prediction of ventricular wall mechanics without computationally expensive finite element analysis, offering a more feasible method for rapid, subject-specific cardiac modeling.

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.