# Trajectory Reconstruction

Single-cell RNA sequencing destroys each cell as it measures it and samples only discrete timepoints, so one cell's expression profile cannot be followed continuously. The Latent Cell-State Model approximates cell-state transitions as a Markov chain and adds a low-dimensional continuous latent variable to generate expression counts given cell state, time and gene. On the pancreatic development and Waddington-OT reprogramming datasets, maximum mean discrepancy between generated and real samples at held-out timepoints was 0.011 and 0.006 (pancreatic days 3 and 6) and 0.030 and 0.018 (reprogramming days 3.5 and 4), lower than the three compared methods.

Single-cell RNA sequencing destroys each cell as it measures it and samples only discrete timepoints, so one cell's expression profile cannot be followed continuously. The Latent Cell-State Model approximates cell-state transitions as a Markov chain and adds a low-dimensional continuous latent variable to generate expression counts given cell state, time and gene. On the pancreatic development and Waddington-OT reprogramming datasets, maximum mean discrepancy between generated and real samples at held-out timepoints was 0.011 and 0.006 (pancreatic days 3 and 6) and 0.030 and 0.018 (reprogramming days 3.5 and 4), lower than the three compared methods.

The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.

The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.