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.

Most digital electronic circuits utilize a timing reference to synchronize the progression of signals and enable sequential memory elements. These designs may not be realizable in biological substrates due to the lack of a reliable high-frequency clock signal. Asynchronous designs eliminate the need for a clock with data encodings and request/acknowledge handshake protocols. This paper proposes a workflow to automate the design of asynchronous genetic circuits. This workflow extends genetic design tools by leveraging asynchronous logic design methods customized for this technology. This workflow is demonstrated on a genetic sensor that uses filtering and cellular communication to improve its reliability.