LASSIM

LASSIM infers large-scale gene regulatory networks by fitting mechanistically defined non-linear ordinary differential equations to time-resolved, quantitative multi-omics and expression datasets for genome-wide mechanistic modeling and systems pharmacokinetic applications.

Key Features:

• Mechanistic Modeling: Employs mechanistically defined non-linear ordinary differential equations that incorporate structural knowledge of regulatory interactions to model GRN dynamics.
• Integration with Multi-Omics Data: Integrates time-resolved quantitative multi-omics data as well as multiple steady-state and dynamic response expression datasets.
• Core Gene Simplification: Reduces network complexity by focusing on a limited subset of pre-specified core genes hypothesized to regulate peripheral gene transcription events.
• Parallel Optimization: Performs parallel parameter optimization to model regulation of peripheral genes by core system genes, enabling efficient large-scale parameter estimation.
• High-Performance Computing Compatibility: Implemented using the PyGMO Python package and optimized for multicore computers and high-performance clusters.

Scientific Applications:

• Genome-wide GRN inference: Infers large-scale non-linear models of biological processes and genome-wide gene regulatory networks.
• Naïve Th2 cell differentiation modeling: Reconstructs a genome-wide model of Th2 transcriptional regulation by integrating Th2-specific bindings, time-series data, and siRNA-mediated knock-down experiments.
• Systems pharmacokinetics: Supports systems pharmacokinetic analyses through integration of quantitative time-resolved multi-omics and expression data.
• Immune-related disease research: Provides mechanistic models relevant to immune-related diseases via inference of the Th2 transcription regulatory system.

Methodology:

LASSIM first infers a non-linear ODE system from pre-specified core gene expressions and subsequently performs parallel parameter optimization to model regulation of peripheral genes by the core system genes.