SBpipe

SBpipe automates repetitive model simulations and parameter estimation pipelines for systems biology to enable robustness, parameter profile, and correlation analyses of mathematical models.

Key Features:

• Automation of Repetitive Tasks: Executes repeat sequences of model simulations and parameter estimations driven by YAML configuration files.
• Pipeline Execution: Runs pipelines on multicore machines and on Sun Grid Engine (SGE) or Load Sharing Facility (LSF) clusters for parallel execution.
• Parameter Estimation Analysis: Provides parameter estimation pipelines that produce parameter profile likelihoods and parameter correlations using samples from computed estimates, and supports one- and two-parameter scans.
• Flexibility in Model Implementation: Supports models implemented in COPASI, Python, or any language accessible through a Python wrapper module.
• Dynamic Support for Additional Simulators: Allows additional software simulators to be added without disrupting existing pipeline functionality.
• Robustness Information Extraction: Extracts robustness information by aggregating results from repeated simulations and estimations.
• Comprehensive Reporting: Generates reports in LaTeX/PDF format summarizing automated analyses and results.

Scientific Applications:

• Model simulation and parameter estimation: Automates large-scale simulation and parameter estimation campaigns for mathematical models in systems biology.
• Uncertainty and robustness analysis: Produces parameter profile likelihoods, parameter correlations, and parameter-scan analyses to assess model uncertainty and robustness of predictions.

Methodology:

Pipeline-based execution driven by YAML configuration files; parallel execution on multicore, SGE, or LSF environments; parameter estimation workflows producing samples used for profile likelihood and correlation analysis; one- and two-parameter scan pipelines; model interfacing via COPASI, Python, or a Python wrapper; report generation in LaTeX/PDF.