1. Introduction

AutoReduce can be used to obtain smaller dynamical models from higher dimensional models. We consider a generalized nonlinear system description as

\[\dot{x} = f(x, \theta, u), \qquad y = h(x, \theta, u),\]

where x is the state vector, theta is the parameter vector, u is an optional input, and y is the measured or designed output.

1.1. Reduction Methods

AutoReduce currently provides:

  • Time-scale separation for quasi-steady-state approximation (QSSA).

  • Conservation-law reduction for invariant total quantities.

  • Local sensitivity analysis for ranking parameter effects and quantifying robustness of reduced models.

1.2. Model Sources

Models can be constructed directly with SymPy expressions. AutoReduce also provides compatibility modules for common scientific modeling workflows:

  • SBML import and export through python-libsbml.

  • BioCRNpyler integration through SBML files.

  • python-control NonlinearIOSystem.