Pedro Fontanarrosa & Chania Clare (Second author) - MIMIC: A Python Package for Simulating, Inferring, and Predicting Microbial Community Interactions and Dynamics

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 analysing 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. 

Microbial communities play a critical role in maintaining ecosystem functions, influencing human health (including digestion, immunity, and mental health), and impacting environmental processes such as nutrient cycling and pollution degradation. There is a potential to engineer microbial communities for specific therapeutic and environmental purposes and functions. The gut microbiota is a dynamic community composed of a variety of microbes, metabolites, and environmental perturbations, with complex direct and indirect interactions between all components. When balanced, this network can support healthy gut function and protect against infection (Cryan and O’Mahony, 2011; Deriu et al., 2013; Reissbrodt et al., 2009), but in dysbiosis it can contribute to a multitude of health problems, such as colorectal cancer (Fan et al., 2021), diabetes (Wang et al., 2012), cardiovascular disease (Koeth et al., 2013) and obesity (B¨ackhed et al., 2004). 

Within this community, each species has distinct nutrient preferences and colonisation strategies, which result in complex intra-species dynamics that are constantly subjected to a changing environment. The nature of these dynamics also changes over time (Caporaso et al., 2011), where some species can contribute to healthy microbiota until an opportunity arises and they become pathogenic and harmful to human health (Kamada et al., 2013). In order to understand and control this complex and crucial community, it is important to untangle the nature of these interactions.

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