WebMaBoSS

WebMaBoSS performs stochastic simulation and analysis of Boolean models using MaBoSS continuous-time Markov processes to derive semi-quantitative insights into gene regulatory and intracellular networks.

Key Features:

• Stochastic simulation engine: Uses MaBoSS and continuous-time Markov processes to simulate Boolean models stochastically.
• Boolean-to-semi-quantitative conversion: Converts discrete Boolean network behaviors into semi-quantitative outputs requiring minimal parameter fitting.
• Model compatibility: Supports import of models from Cell Collective and BioModels.
• Mutant screening: Performs mutant screening analyses by perturbing model nodes and evaluating outcome distributions.
• Parameter sensitivity assessment: Computes parameter sensitivity to evaluate robustness and influence of model parameters.
• Model and result management: Stores models and simulation results and provides result filtering and export of figures.

Scientific Applications:

• Gene regulatory networks: Analysis and stochastic simulation of gene regulatory network dynamics using Boolean representations.
• Intracellular signaling and processes: Modeling intracellular pathways and process dynamics with semi-quantitative outputs.
• Systems biology research: Hypothesis testing and exploration of network dynamics in systems biology studies.
• Drug discovery: Evaluating perturbation effects and candidate targets via mutant screening and sensitivity analysis.
• Personalized medicine: Applying stochastic Boolean model analyses to study patient-specific network behaviors.

Methodology:

Stochastic simulation via MaBoSS continuous-time Markov processes; conversion of Boolean networks to semi-quantitative outputs with minimal parameter fitting; mutant screening; and parameter sensitivity analysis.