https://github.com/lcsb-biocore/cufluxsampler.jl

GPU-accelerated algorithms for flux sampling in CUDA.jl
https://github.com/lcsb-biocore/cufluxsampler.jl

cobra cuda gpu julia metabolic-network metabolism sampling

Last synced: 20 days ago
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GPU-accelerated algorithms for flux sampling in CUDA.jl

• Host: GitHub
• URL: https://github.com/lcsb-biocore/cufluxsampler.jl
• Owner: LCSB-BioCore
• License: apache-2.0
• Created: 2023-01-19T07:41:10.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-19T12:14:35.000Z (6 months ago)
• Last Synced: 2024-11-20T00:15:34.816Z (about 1 month ago)
• Topics: cobra, cuda, gpu, julia, metabolic-network, metabolism, sampling
• Language: Julia
• Homepage:
• Size: 173 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# CuFluxSampler.jl

| Documentation |

|:---:|

| [![stable documentation](https://img.shields.io/badge/docs-stable-blue)](https://lcsb-biocore.github.io/CuFluxSampler.jl/stable) [![dev documentation](https://img.shields.io/badge/docs-dev-cyan)](https://lcsb-biocore.github.io/CuFluxSampler.jl/dev) |

Flux samplers for

[COBREXA.jl](https://github.com/LCSB-BioCore/COBREXA.jl/),

accelerated on GPUs via [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl).

The repository contains the following modules with samplers:

- Affine-combination-directed Hit&Run (module `CuFluxSampler.AffineHR`)

- Artificially-Centered Hit&Run (module `CuFluxSampler.ACHR`)

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 `AffineHR` may be

  changed by `mix_points` parameter, and you can alternatively supply your own

  mixing matrix in `mix_mtx`.

- You can turn on/off the stoichiometry checks with `check_stoichiometry` and

  tune it with `epsilon` (in both `ACHR` and `AffineHR`)

- You can add tolerance bounds on stoichiometry in order to expand the feasible

  region a little to allow randomized runs to succeed; see

  `check_stoichiometry` and `direction_noise_max` parameters.

- You can set a seed for the GPU-generated random numbers using `seed`

Running the package code and tests requires a CUDA-capable GPU.

#### Acknowledgements

`CuFluxSampler.jl` was developed at the Luxembourg Centre for Systems

Biomedicine of the University of Luxembourg

([uni.lu/lcsb](https://www.uni.lu/lcsb)).

The development was supported by European Union's Horizon 2020 Programme under

PerMedCoE project ([permedcoe.eu](https://www.permedcoe.eu/)),

agreement no. 951773.