# msmtools.flux.pathways¶

msmtools.flux.pathways(F, A, B, fraction=1.0, maxiter=1000)

Decompose flux network into dominant reaction paths.

Parameters: F ((M, M) scipy.sparse matrix) – The flux network (matrix of netflux values) A (array_like) – The set of starting states B (array_like) – The set of end states fraction (float, optional) – Fraction of total flux to assemble in pathway decomposition maxiter (int, optional) – Maximum number of pathways for decomposition paths (list) – List of dominant reaction pathways capacities (list) – List of capacities corresponding to each reactions pathway in paths

Notes

The default value for fraction is 1.0, i.e. all dominant reaction pathways for the flux network are computed. For large netorks the number of possible reaction paths can increase rapidly so that it becomes prohibitevely expensive to compute all possible reaction paths. To prevent this from happening maxiter sets the maximum number of reaction pathways that will be computed.

For large flux networks it might be necessary to decrease fraction or to increase maxiter. It is advisable to begin with a small value for fraction and monitor the number of pathways returned when increasing the value of fraction.

References

 [1] P. Metzner, C. Schuette and E. Vanden-Eijnden. Transition Path Theory for Markov Jump Processes. Multiscale Model Simul 7: 1192-1219 (2009)