iQcell

iQcell infers executable logical gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data and simulates their dynamics to predict effects of gene perturbations on developmental trajectories.

Key Features:

• Inference of Executable Logical GRNs: iQcell constructs executable logical GRNs directly from scRNA-seq data for dynamic analysis.
• Simulation Capabilities: The platform performs dynamic simulations of inferred GRNs to examine temporal behavior and responses to gene perturbations.
• Validation Against Known Data: iQcell has been applied to scRNA-seq datasets from early mouse T-cell development and recovered over 75% of previously established causal gene interactions.
• Qualitative Recapture of Perturbation Effects: Dynamic simulations qualitatively recapitulate known effects of gene perturbations on T-cell developmental trajectories.
• Versatility Across Developmental Systems: The approach is applicable to a range of developmental systems beyond the demonstrated mouse T-cell context.

Scientific Applications:

• Developmental Biology: Inferring and simulating GRNs to study regulatory mechanisms underlying developmental trajectories.
• Stem Cell Research: Modeling gene regulatory dynamics to inform differentiation programs and experimental perturbations in stem cell systems.
• Hypothesis Generation: Producing testable hypotheses about regulatory interactions and developmental programs based on data-driven GRNs.
• Design of Stem Cell–Based Technologies: Using simulated perturbation outcomes to guide design and optimization of stem cell interventions relevant to regenerative medicine.

Methodology:

Inference of executable logical GRNs from scRNA-seq data, dynamic simulation of inferred GRNs to model temporal dynamics and perturbation effects, and validation by comparison to known causal interactions in datasets such as early mouse T-cell development (recovering over 75% of known interactions).