Sabine Muller | Searcharxiv

arXiv2025

Protocellular energetics: Free energy estimates for all metabolic, self-assembly and vesicle fission processes

As minimal cells or protocells are dramatically simpler than modern unicells it is possible to quantitatively estimate free energy changes for every process in the lifecycle of a protocell and compare these with estimates of the free energy changes for lifecycles in modern unicells. We present quantitative estimates of all metabolic changes in part by new density function theory (DFT) estimations, in part by compiling previously measured or estimated free energy changes, and in part by new thermodynamic calculations for all self-assembly, vesicle bending, and fission energies.

physics.bio-ph↗

arXiv2025

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth

Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

cs.CV↗