Treffer: MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.

Title:
MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.
Authors:
Fontanarrosa P; Research Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, United Kingdom., Clare C; Research Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, United Kingdom., Fedorec AJH; Research Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, United Kingdom., Barnes CP; Research Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, United Kingdom.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2025 May 06; Vol. 41 (5).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Software* , Microbiota* , Computational Biology*/methods , Microbial Interactions*, Bayes Theorem ; Machine Learning ; Computer Simulation
References:
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Grant Information:
BB/W013770/1 Bioengineered Cells & Systems; BB/T008709/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council
Entry Date(s):
Date Created: 20250523 Date Completed: 20250528 Latest Revision: 20250531
Update Code:
20250531
PubMed Central ID:
PMC12119135
DOI:
10.1093/bioinformatics/btaf174
PMID:
40408146
Database:
MEDLINE

Weitere Informationen

Summary: 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 multi-omic data, to offer a versatile framework for analyzing microbial dynamics. By leveraging Bayesian inference and machine learning techniques, MIMIC provides the ability to infer 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 contribute 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.
Availability and Implementation: MIMIC is freely available under the MIT License at https://github.com/ucl-cssb/MIMIC. It is implemented in Python (version 3.7 or higher) and is compatible with Windows, macOS, and Linux operating systems. MIMIC depends on standard Python libraries including NumPy, SciPy, and PyMC. Comprehensive examples and tutorials (including the main text demonstrations) are provided as Jupyter notebooks in the examples/directory and at the MIMIC Docs website, along with detailed installation instructions and real-world data use cases. The software will remain freely available for at least two years following publication. A code snapshot for this publication is also available at Zenodo: https://doi.org/10.5281/zenodo.15149003.
(© The Author(s) 2025. Published by Oxford University Press.)