Contact-Guided 3D Genome Structure Generation of E. coli via Diffusion Transformers
This work addresses the problem of generating diverse and physically consistent 3D genome structures for biologists studying E. coli, offering an incremental improvement over single deterministic structure generation.
This study developed a conditional diffusion-transformer framework to generate ensembles of 3D E. coli genome conformations, guided by Hi-C contact maps. The model successfully reproduces input Hi-C distance-decay and structural correlation metrics on held-out ensembles while maintaining conformational diversity.
In this study, we present a conditional diffusion-transformer framework for generating ensembles of three-dimensional Escherichia coli genome conformations guided by Hi-C contact maps. Instead of producing a single deterministic structure, we formulate genome reconstruction as a conditional generative modeling problem that samples heterogeneous conformations whose ensemble-averaged contacts are consistent with the input Hi-C data. A synthetic dataset is constructed using coarse-grained molecular dynamics simulations to generate chromatin ensembles and corresponding Hi-C maps under circular topology. Our models operate in a latent diffusion setting with a variational autoencoder that preserves per-bin alignment and supports replication-aware representations. Hi-C information is injected through a transformer-based encoder and cross-attention, enforcing a physically interpretable one-way constraint from Hi-C to structure. The model is trained using a flow-matching objective for stable optimization. On held-out ensembles, generated structures reproduce the input Hi-C distance-decay and structural correlation metrics while maintaining substantial conformational diversity, demonstrating the effectiveness of diffusion-based generative modeling for ensemble-level 3D genome reconstruction.