The repository contains the following modules with samplers:
- Affine-combination-directed Hit&Run (module
- Artificially-Centered Hit&Run (module
Both modules export a specific function for running the sampler atop COBREXA.jl
MetabolicModel structure, typically called
sample. See the code comments
and documentation for details.
Samplers support many options that can be turned on and off, in general:
- Number of points used for mixing the new run directions in
AffineHRmay be changed by
mix_pointsparameter, and you can alternatively supply your own mixing matrix in
- You can turn on/off the stoichiometry checks with
check_stoichiometryand tune it with
- You can add tolerance bounds on stoichiometry in order to expand the feasible
region a little to allow randomized runs to succeed; see
- You can set a seed for the GPU-generated random numbers using
Running the package code and tests requires a CUDA-capable GPU.
CuFluxSampler.jl was developed at the Luxembourg Centre for Systems
Biomedicine of the University of Luxembourg
The development was supported by European Union's Horizon 2020 Programme under
PerMedCoE project (permedcoe.eu),
agreement no. 951773.