maud-metabolic-models 0.4.0.1

Bayesian statistical models of metabolic networks

Maud is a work-in-progress 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.

Install

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. Fortunately, cmdstanpy comes with commands that can install these for you. On windows the necessary dependencies can be installed with the following powershell commands:

python -m cmdstanpy.install_cxx_toolchain
python -m cmdstanpy.install_cmdstan --compiler

On macos or Linux, you must install the c++ requirements manually (see [here](https://cmdstanpy.readthedocs.io/en/v0.9.67/installation.html#install-cmdstan)) for instuctions. Cmdstan can then be installed using this shell command:

install_cmdstan

Usage

To run the simple linear model, use the following command:

maud sample

This will compile the Stan program at src/maud/inference_model.stan, then run the resulting binary file using the data at src/maud/data/example_inputs/linear, storing the results in a folder whose name starts with model_output_linear.

The sample command can be configured in a few ways - to check out all the options try running

maud sample --help

Copyright

• Copyright (c) 2019, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark.

• Free software distributed under the GNU General Public License 3.0