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

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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.\nAVAILABILITY 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.", "claims": [{"id": "claim-method", "statement": "First, we simulated microbial abundances using the VAR model with specific interaction parameters, then used Bayesian inference to recover these parameters.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40408146", "locator": "sections[6] \"3 Results\" paragraph 2", "quote": "First, we simulated microbial abundances using the VAR model with specific interaction parameters, then used Bayesian inference to recover these parameters."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.15149003.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40408146", "locator": "sections[15] \"Data availability\" paragraph 2", "quote": "The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.15149003."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MIMIC used Bayesian inference to recover interaction parameters from synthetic data and infer microbial–metabolite interactions from a real-world wastewater-treatment dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40408146", "locator": "sections[6] \"3 Results\" paragraph 2", "quote": "First, we simulated microbial abundances using the VAR model with specific interaction parameters, then used Bayesian inference to recover these parameters."}, {"asset": "source:pmid:40408146", "locator": "sections[6] \"3 Results\" paragraph 2", "quote": "Second, we applied MIMIC to a real-world dataset of microbial abundances and metabolite concentrations from a wastewater treatment facility (Herold et al. 2020)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "MIMIC used Bayesian inference to recover interaction parameters from synthetic data and infer microbial–metabolite interactions from a real-world wastewater-treatment dataset.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40408146"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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