Skip to main content

Bayesian statistical models of metabolic networks

Project description

GNU General Public License 3.0 Black Contributor Covenant Version 1.4 Documentation Status

Maud is an application that fits Bayesian statistical models of metabolic networks using Python and Stan.

Maud aims to take into account allosteric effects, ensure that the laws of thermodynamics are obeyed and to synthesise information from both steady state experiments and the existing literature.

Installation

First create a fresh Python virtual environment and then activate it:

python -m venv .venv --prompt=maud
source .venv/bin/activate

To install Maud and its python dependencies to your new virtual environment, run this command:

pip install maud-metabolic-models

Cmdstanpy depends on cmdstan, which in turn requires a c++ toolchain. On some computers you will have to install these in order to use Maud. You will hit an error at the next step if this applies to your computer. Luckily cmdstanpy comes with commands that can do the necessary installing for you. On windows the c++ toolchain can be installed with the following powershell commands:

Usage

Maud is used from the command line. To see all the available commands try running

maud --help

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

maud-metabolic-models-0.7.1.0.tar.gz (73.7 kB view details)

Uploaded Source

Built Distributions

maud_metabolic_models-0.7.1.0-py3-none-win_amd64.whl (16.1 MB view details)

Uploaded Python 3 Windows x86-64

maud_metabolic_models-0.7.1.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB view details)

Uploaded Python 3 manylinux: glibc 2.17+ x86-64

maud_metabolic_models-0.7.1.0-py3-none-macosx_10_9_x86_64.whl (10.3 MB view details)

Uploaded Python 3 macOS 10.9+ x86-64

File details

Details for the file maud-metabolic-models-0.7.1.0.tar.gz.

File metadata

File hashes

Hashes for maud-metabolic-models-0.7.1.0.tar.gz
Algorithm Hash digest
SHA256 6425c0ea4ab9827e6b3fb8d4bb13e3720beb2c86b3786effd9258e11c3b78234
MD5 a5b4b0ed74962b611877030dea18c2ce
BLAKE2b-256 93f2b29635b5a4365c21739363c2fe8f412be39fc235e148c341f779cddc65fc

See more details on using hashes here.

File details

Details for the file maud_metabolic_models-0.7.1.0-py3-none-win_amd64.whl.

File metadata

File hashes

Hashes for maud_metabolic_models-0.7.1.0-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 8770d75082600c20ac95ad8700c167420d90eee92ecb8e14b1fce6aba26dcc4b
MD5 80598273de66ef3e3ab797e5ea4cc274
BLAKE2b-256 cf06082a59fa741ff1c281774cdbdec70484bc114f14650311a6465132d442d2

See more details on using hashes here.

File details

Details for the file maud_metabolic_models-0.7.1.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maud_metabolic_models-0.7.1.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 566de9a1517d0dca4ed72df3185233b94e18ca0757e9f97ec84dcc9af92061f6
MD5 8fca7bd0d32450776f3769d24a88d315
BLAKE2b-256 2e2178d24c7858ed3b45c77b68946b6f77bc6cf74ed1599e94bb43f17d79c42c

See more details on using hashes here.

File details

Details for the file maud_metabolic_models-0.7.1.0-py3-none-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for maud_metabolic_models-0.7.1.0-py3-none-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 fdfbabba052ad3fe558ae6290db9a1653a5daf7f4dbe46f9b1914c4aea1f5eaa
MD5 f522141ede8b6ad071ede8097b666a6a
BLAKE2b-256 68669e6222c2acd194f6c61001c7f26c94b5e95b8dd69045924f6f210df0f41b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page