Evaluation and reduction of stochastic reaction networks, differential equations, and Boolean networks

An all-in-one tool for the numerical solution, stochastic simulation, and minimization of dynamical systems with import/export options for a variety of third-party formats including SBML and Matlab.

Quick start

Supported through the Apache Commons Math library and through a Java porting of the SUNDIALS library.

Gillespie's Direct Method, Gibson and Bruck's Next Reaction Method, tau-leaping.

**Forward equivalence** produces a reduced model where every macro-variable represents the sum of variables in each block.

**Backward equivalence** ensures that all variables in a block have the same solution at all times points.

**Species equivalence** identifies a partition of the species of in a network and produces a reduced one where the marginal probability distribution of each macro-species corresponds to the probability distribution of the sum of the species in each block.

**Forward differential equivalence** and **backward differential equivalence** are generalizations of the forward and backward equivalences when the derivatives have rational expressions, minimum/maximum functions, etc.

**ε-forward and ε-backward equivalence**: relaxations of forward and backward equivalence where the macro-variables approximately represent the sum of original variables within some computable bound.

**Backward Invariance** is a generalization of backward equivalence when the derivatives are linear and the differential equations are complemented by algebraic constraints (e.g., a constraint x1=x2+x3 implies that whatever value these 3 variables take, x1+x2 must always be equal to x1, constraining the space of admissible solutions).

**Boolean backward equivalence**: is a recasting of backward equivalence to Boolean networks. These are a popular qualitative model of biological models where variables and update functions are Boolean.