Chunhe Li

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

The paper uses polymer simulations to show that transcription factor condensates optimize enhancer-promoter communication through a 'golden mean' balance of clustering strength and genomic distance.

The paper develops chromatin models to study enhancer-promoter communication, finding that dynamic chromatin structures enhance transcriptional clustering and promote E-P interactions.

TMELand integrates data-driven GRN inference with model-driven landscape mapping to visualize and analyze cellular transition dynamics from single-cell transcriptomic data.

The paper constructs cell-state transition landscapes in melanoma to quantify dynamics of drug resistance, revealing alternative paths like dedifferentiation and hyper-pigmentation, and identifies optimal therapeutic strategies.

The paper identifies a network motif family that enables multistep cell fate transitions with restricted reversibility, validated in T cell development.