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Algorithm that allows to change the values of a system's variables by discrete (integral) amounts.

Algorithm that allows to change the values of a system's variables by continuous (non-integral) amounts.

Algorithm that takes into account the location of the reacting components.

Partial differential equations are used to describe variations in concentrations over both, space and time.

Stochastic simulation algorithm using the an approach alike the one described in Gillespie's papers of 1976 and 1977.

Algorithms that considers the system to be simulated as made of well-stirred components, that is the activity of the components is the same independently of their location.

Algorithm aimed to speed-up Gillespie's direct method.

Algorithm that simulates the temporal evolution of a system using determined descriptions, that from a precise initial state always provide the same ending state.

Algorithm that simulates the temporal evolution of a system using probabilistic rules, that from a precise initial state may provide different ending state.

Algorithm that uses timesteps of constant length to update the state of a system during the whole simulation.

Algorithm that does not use fixed timesteps to update the state of a system during the whole simulation, but on the contrary adapts the length of the timesteps to the local situation.

Explicit tau-leaping method.

Implicit tau-leaping method.

Gillespie-like algorithm that produces exact realisation of the vector of entity amounts at each time by choosing rigorously time and reaction at each iteration.

The root of the branch where all the deprecated terms are stored.

One of the methods, implemented in SUNDIALS (a suite of advanced computational codes for solving large-scale problems that can be modeled as a system of nonlinear algebraic equations, or as initial-value problems in ordinary differential or differential-algebraic equations).