MIMIC: A Comprehensive Python Package for Simulating, Inferring, and Predicting Microbial Community Interactions
The study of microbial communities is vital for understanding their impact on environmental, health, and technological domains. The Modelling and Inference of MICrobiomes Project (MIMIC) introduces a Python package designed to advance the simulation, inference, and prediction of microbial community interactions and dynamics. Addressing the complex nature of microbial ecosystems, MIMIC integrates a suite of mathematical models, including previously used approaches such as Generalized Lotka- Volterra (gLV), Gaussian Processes (GP), and Vector Autoregression (VAR) plus newly developed models for integrating multiomic data, to offer a comprehensive framework for analysing microbial dynamics. By leveraging Bayesian inference and machine learning techniques, MIMIC accurately infers the dynamics of microbial communities from empirical data, facilitating a deeper understanding of their complex biological processes, unveiling possible unknown ecological interactions, and enabling the design of microbial communities. Such insights could help to advance microbial ecology research, optimizing biotechnological applications, and contributing to environmental sustainability and public health strategies. MIMIC is designed for flexibility and ease of use, aiming to support researchers and practitioners in microbial ecology and microbiome research. This software package contributes to microbial ecology research and supports ecological predictions and applications, benefiting the scientific and applied microbiology communities.
The repository is organized into the following main directories:
AUTHORS.rst: A list of authors and contributors to the project.build/: Contains files generated by the build process.CONTRIBUTING.rst: Guidelines for contributing to the project.docs/: Contains the project's documentation.examples/: Contains example scripts and notebooks demonstrating how to use the package.HISTORY.rst: A log of changes made in each version of the project.LICENSE: The license for the project.mimic/: The main directory for the project's source code.README.rst: The main README file for the project, providing an overview and basic usage examples.requirements.txt: A list of Python dependencies required to run the project.setup.py: The build script for the project.tests/: Contains unit tests for the project's code.Prerequisites ^^^^^^^^^^^^^ Conda package manager is recommended due to dependencies on PyMC.
Python Packages """""""""""""""" The Python packages needed to run this package are listed in the requirements.txt file in the same workspace. To install them, run:
.. code-block:: bash
pip install -r requirements.txt
Compilers """"""""""
.. Solvers .. """""""" .. Solver 1 .. Solver 2
Steps ^^^^^
environment.yml file.pip install -r requirements.txt from the root directory of the repository.pip install -e . from the root directory of the repository.To get started with MIMIC, you can explore a variety of detailed examples and comprehensive documentation.
The documentation is regularly updated with the latest information on usage, features, and examples to help you effectively utilize the MIMIC package in your research or applications.
We welcome contributions to the MIMIC project. Please refer to our Contribution Guidelines <CONTRIBUTING.rst>_ for more information.
This project is licensed under the LICENSE <LICENSE>_.
This project is based on methods proposed in this paper <https://onlinelibrary.wiley.com/doi/full/10.1002/bies.201600188>_.
For questions or feedback, please contact us <mailto:christopher.barnes@ucl.ac.uk>_.