TMELand

TMELand integrates data-driven gene regulatory network (GRN) inference with model-driven landscape modeling to quantify and visualize Waddington's epigenetic landscape for analysis of cell differentiation and reprogramming dynamics.

Key Features:

• GRN Inference: Infers gene regulatory networks (GRNs) from transcriptomic and gene expression datasets using data-driven methods.
• Epigenetic Landscape Visualization: Quantifies and visualizes Waddington's epigenetic landscape to represent cell differentiation and reprogramming trajectories.
• State Transition Path Calculation: Computes state transition paths between attractors within the landscape to elucidate cellular transition dynamics.

Scientific Applications:

• Computational Systems Biology: Supports studies of dynamical trends and regulatory mechanisms underlying cell fate determination.
• Cell State Prediction and Transition Analysis: Enables prediction of cellular states and analysis of transition paths in differentiation and reprogramming.
• Single-cell Transcriptomics: Applies to single-cell transcriptomics by inferring GRNs from single-cell gene expression data and mapping corresponding landscapes.

Methodology:

Integrates data-driven GRN inference from transcriptomic/gene expression data with model-driven landscape modeling and computes state transition paths between attractors.