A package to reconstruct metabolic interaction networks in microbial communities using decomposed in silico Minimal exchanges

1)Use DiME (in silico Minimal Exchanges)

You should have your solver see instructions inside docker

2. latest version of pytfa to be included in your docker file

   if not change entrypoint in the docker file

3)Files are stored as .h5 binary files for this

ReMIND: Reconstruction of Microbial Interaction Networks using Decomposed in silico minimal exchanges ========================================== This repository contains the workflow to reconstruct metabolic interaction networks in microbial communities using decomposed in silico Minimal exchanges

More information can be found in the The package is available for both python and matlab. The results in the preprint are generated with the python version.

The package is developed using python 3.6 and run in Docker (20.10.21) containers. Tested with solvers cplex (v12.8.0) and gurobi(v9.1.2)

Recommended to be run in docker containers, with dedicated solver installed. Setting up the python API of cplex in case docker based installation is not used

Generated data used in the manuscript is available under data subfolder under hierarchical data format.

Requirements

You will need to have Git-LFS in order to properly download some binary files:

git clone https://github.com/EPFL-LCSB/remind.git /path/to/remind
cd /path/to/remind
git lfs install
git lfs pull

Further the following pip-python packages are required (can be found in detail in requirements.txt

• optlang
• cobra==0.17.1
• numpy<=1.17.4
• pandas
• matplotlib
• tables
• sklearn
• ipython
• jedi==0.17.2
• tqdm
• scipy
• holoviews
• matplotlib_venn
• pytfa

Container-based install

You might want to use this program inside of a container. The docker/_ subfolder has all the necessary information and source files to set it up.

cd remind/python/remind/docker
./build.sh
./run.sh

Building the docker image takes approximately 5 mins.

Setup

If container-based installation is not preferred you can also install this module from source using pip: For Python 3, you might have to use pip3 instead of pip

git clone https://github.com/EPFL-LCSB/remind.git /path/to/remind/python
pip3 install -e /path/to/remind

The installation process should not exceed a minute if the requirements are installed. If they are not, it might take longer as the installer installs them first.

Quick start

As a demo following examples can be run for the 2-member honeybee gut community after building the environment or inside the docker This tutorial aims to show the step to use our framework. Can be adapted to any community with the desired extracellular environment. As mentioned above in these scripts most data is saved as .h5 binary files. For this you will need hdf files downloaded if you are running inside docker. You can find the instructions in instructions_hdf5.txt file. Or change the storing in the scripts get_dimes_tutorial.py build_community_model_from_dimes_tutorial.py and run_ilp_tutorial_community_model.py from "to_hdf" to another format (e.g. csv) "to_csv".

cd /
cd remind/projects/tutorial/

First get the DiMEs for both members by running the following bash script. Number of alternatives are limited to 10 for tutorial purposes can be changed inside the get_dimes_tutorial.py script. script get_dimes_tutorial.py by modifying the max_alternative.

./bash_tutorial_dimes.sh

After generating the DiMEs merge the DiMEs and build the community model and save it with the following script inside Ipython.

ipython
run build_community_model_from_dimes_tutorial.py

The next step is to use the built community model and reconstruct the interaction networks with a user defined objective function via the ILP formulation. For this you can refer to the run_ilp_tutorial_community_model.py script. for various objective functions. To run for the indicated objective functions run the following bash script.

./bash_tutorial_ilp.sh

After running the ILP for various objective functions you can analyse the data inside Ipython:

ipython
run analysis_ilp_solutions_tutorial.py
#check the alternative cooperation patterns
print(frame_int_coop.pos_int)

To then generate the figures in the manuscript you can check the scripts inside the figures subfolder.

License

The software in this repository is put under an APACHE licensing scheme - please see the LICENSE file for more details.