A computational modeling language for complex laboratory experiments

Abstract BioScape P , a probabilistic version of BioScape, is a new modeling language for a state-dependent stochastic simulation of parallel processes in three dimensional space. Normally a modeling language describes an initial concentration of entities after which all changes are driven by the simulation of reactions. We instead design conditional simulation commands which depend on a global state. Our contribution is a new command in the form when R run A 1 ⋯ A n , which will cause A 1 ⋯ A n entities to be added to the system when property R in the context of a global state is satisfied. Commands and reactions are further equipped with user-defined update functions to produce side effects on the global state. The global state for simulation is defined to include at least a simulation clock to enable time dependent computation. BioScape P matches the realistic nature of experimentation, by defining uncertainty from two sources: stochastic movement generating reactions on proximity, and probabilistic choice, where an entity has the ability to be involved in more than one reaction. To capture the richer notion of state dependent conditional commands, which need to be evaluated periodically, we must define a system of multi-level semantics consisting of two layers: World Level Semantics and Individual Level Semantics. Both levels take turns to evaluate their respective domains. The World Level Semantics evaluates the aforementioned when-run conditional commands, while the Individual Level Semantics simulates reactions, entity movement, and maintains the simulation clock and timed entities