Welcome to the (not so) SKiMPy documentation!¶
SKiMPy (Symbolic Kinetic Models in Python) implements various methods and resources that allow the user to build and analyze large scale kinetic models efficiently, including i) automated draft model construction from FBA and TFA using Cobrapy [1] and pyTFA [2], respectively, ii) an extensive library of kinetic mechanisms, iii) efficient model parametrization using ORACLE [3–7], iv) local stability analysis [8], v) global stability analysis, vi) local sensitivity analysis, e.g., metabolic control analysis [9], vii) global sensitivity analysis and uncertainty propagation [10,11] viii) modal analysis [8], iix) non-linear ODE integration [12,13], ix) identification of conserved pools [14]. Therefore, the presented Python package implements an object-oriented interface to construct the symbolic expressions from a library of kinetic mechanisms using sympy [15] and then precompiling these expressions into machine code using Cython [16]. SKiMpy also integrates the SUNDIALS ODE-Solver package [13] using the interface provided by the ODES package [17].
For installtion instructions please refer to README.rst