Bayesian statistical models of metabolic networks
Project description
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 3.7 virtual environment and then activate it:
sudo pip3.7 install virtualenv # if virtualenv isn't installed already
python3.7 -m virtualenv maud_venv # choose any name you like!
source maud_venv/bin/activate
To install the latest Maud and its python dependencies to your new virtual environment from the latest master branch, run this command:
pip install https://github.com/biosustain/Maud/archive/master.zip
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 tests/data/linar.toml, storing
the results in csv files starting 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
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