MaBoSS

MaBoSS simulates continuous and continuous/discrete-time Markov processes on Boolean networks to model stochastic dynamics of signaling pathways and cell populations for systems biology investigations.

Key Features:

• Hybrid Modeling Framework: Implements continuous-time Markov processes on Boolean state spaces with explicit specification of transition rates at each node.
• Dynamic Simulation: Uses Kinetic Monte-Carlo methods (Gillespie algorithms) to simulate temporal evolution of probability distributions, capturing transient and stationary states.
• Stochastic Cell Population Modeling: Simulates populations of cells with stochastic intracellular mechanisms applicable to studies of cancer, HIV, and autoimmune disorders.
• Biological Case Studies: Applied to models such as the p53/Mdm2 interaction and mammalian cell cycle regulation to analyze signaling pathways and perturbation effects.
• Mutation and Treatment Simulation: Supports simulation of mutations and drug treatments and enables sensitivity analyses of network responses.
• Time-dependent Probabilistic Predictions: Produces time-dependent probabilities for biological entities including genes, proteins, and phenotypes to support theoretical predictions.

Scientific Applications:

• Systems Biology and Signaling Pathways: Modeling and analysis of signaling networks to validate network models and predict effects of perturbations.
• Disease Mechanism Analysis: Investigation of cellular deregulation and disease-related pathways in cancer, HIV, and autoimmune disorders.
• Cell Population Dynamics: Study of stochastic intracellular processes and population-level behaviors through transient kinetics and stationary distributions.
• Hypothesis Generation and Experimental Planning: Generation of theoretical predictions from time-dependent probabilities to inform experimental design.

Methodology:

Continuous and discrete/continuous-time Markov processes on Boolean networks with explicit node transition rates, using Kinetic Monte-Carlo (Gillespie) simulations to compute temporal probability distributions and stochastic simulation of cell populations.