Building dynamical models of multi-step state transitions from single cell gene expression trajectories.

ABSTRACT: Multi-step cell state transitions often occur in biological processes, such as cell differentiation and disease progression, yet the regulatory mechanisms governing these transitions remain unclear. Here, we introduce NetDes, a computational method that integrates top-down and bottom-up systems biology to infer core transcription factor regulatory networks and build ODE-based dynamical models from single-cell gene expression trajectories. We demonstrate that NetDes predicts regulatory interactions and reproduces gene expression dynamics through benchmarking using in-silico time trajectories with decoys, tests on gene circuit simulations of embryonic phenotypic switching, and application to time-series scRNA-seq data from human iPSC differentiation. Compared to existing approaches, NetDes h

SUBMITTER: You Y 

PROVIDER: S-EPMC12709465 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature