ALT Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structures beyond conventional function annotation. We propose TriProRep, a structure-aware pretraining method that jointly models three aligned residue-level views: amino-acid identity, backbone geometry, and local full-atom geometry, discretely encoded via VQ-VAE tokenizers. By pretraining to recover original tokens from generator-corrupted views, TriProRep learns to distinguish plausible but incorrect cross-view augmentations from the original protein. We further introduce RepSP, a benchmark for evaluating protein representations in structure-predictive settings. RepSP tests three uses of representations: homodimer co-folding from apo-chain representations, residue-level prediction of homodimer-derived interaction properties, and representation-aligned monomer structure pre

ALT Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are a promising therapy for regenerating myocardium after infarction, but their use is limited by graft-related arrhythmias that frequently occur shortly after transplantation. Experimental studies indicate that these arrhythmias can originate within the graft, which may act as an ectopic pacemaker, yet the mechanisms governing successful excitation of host tissue remain poorly understood. In particular, the role of electrical coupling at the graft-host interface is important, but difficult to measure directly or control. Computer modelling can help here. Here, we present a computational framework that enables systematic investigation of graft-host electrical interactions using a physiologically interpretable parameterisation. We model the graft-host interface as an internal boundary with a defined specific conductance, allowing direct control over coupling strength in units that correspond to measurable tissue properties. W

ALT Cell invasion and spatial pattern formation are two distinct manifestations of cellular self-organisation in development, regeneration, and disease. Here, we develop and analyse a unified theoretical framework that links these two seemingly different behaviours within a single mechanistic model for adhesion-mediated self-organisation in growing cell populations. Using a multiscale analysis, we show that the balance between cell-cell adhesion, self-diffusion, and proliferation controls the emergence of distinct collective dynamics. We find that for weak adhesion, tissues invade through stable monotone fronts. As adhesion increases, invasion slows, fronts become unstable, leading to aggregates and spatial patterns emerging behind the advancing edge. In two spatial dimensions, these instabilities generate fingering morphologies reminiscent of dysregulated invasion in cancer. Crucially, we show that density-dependent regulation of adhesion suppresses these instabilities and restores cohesi