UPMaBoSS

UPMaBoSS simulates cell-population dynamics by extending MaBoSS probabilistic Boolean network simulations with continuous or discrete-time Markov processes and a population-level layer for cell division, death, and intercellular communication.

Key Features:

• Probabilistic Simulation: Performs probabilistic simulations of Boolean networks using continuous or discrete-time Markov processes based on MaBoSS.
• Cell Population Dynamics: Integrates a population-level layer that models cell division, cell death, and intercellular communication.
• Logical Regulatory Graphs: Represents intracellular regulation with logical regulatory graphs complemented by logical rules.
• Case Study Application: Demonstrated on TNF-induced cell death to investigate mechanisms of resistance triggered by treatments, including ligand release and drug perturbations.
• Cell Fate Analysis: Enables analysis of cell fate decisions such as proliferation, differentiation, and apoptosis across cellular network models.

Scientific Applications:

• Cell fate decision modeling: Analyzes proliferation, differentiation, and apoptosis within Boolean network models at the population level.
• Perturbation and treatment response studies: Models population-level responses and resistance mechanisms to stimuli and treatments, exemplified by TNF-induced cell death and ligand- or drug-driven perturbations.

Methodology:

Uses MaBoSS probabilistic Markovian Boolean stochastic simulation applying continuous or discrete-time Markov processes to Boolean networks, employs logical regulatory graphs with logical rules, and incorporates a population-level layer modeling cell division, death, and intercellular communication.