The Digital Microbiome Platform

Open tools and shared workflows for predictive microbiome science

DigiMic is the authoritative gateway to microbial modelling packages, implementation-independent scientific workflows, training materials, and project support.

Choose a package Explore workflows

Packages and tools

From biological parameterisation to community simulation

Choose a modelling implementation, or follow the emerging GEM-based parameterisation layer that will supply biologically grounded consumer-resource model parameters to either language.

Python 3.11+ · active development

DigiMicPy

Construct validated MiCRM parameters, generate reproducible synthetic communities, and simulate consumer-resource dynamics in Python.

Package documentation Source code
Julia 1.8+ · early stage

DigiMic.jl

Generate model parameters, run SciML-based consumer-resource simulations, and explore analysis and environmental-stressor utilities.

Source and setup
Incoming package · GEM → CRM

TPCP: GEM-based CRM parameterisation

TPCP is being developed as a general parameterisation package that translates genome-scale metabolic models (GEMs) and supporting biological or environmental information into parameters for consumer-resource models (CRMs). Its outputs are intended for use with either DigiMicPy or DigiMic.jl.

Temperature-aware growth and metabolic responses are a key use case, alongside parameterisation of resource uptake, secretion and leakage, biomass production, maintenance, and medium constraints.

TPCP migration status

Documentation ownership

Shared science here, implementation details with each package

This platform owns the project vision, shared scientific definitions, interpretation, reporting guidance, team, training directory, and support routes. Each package owns its installation, API, implemented equations, executable examples, and development documentation.

Read the documentation policy

Shared scientific workflows

One reference across implementations

Use the platform pages for scientific assumptions, interpretation, and reporting choices, then follow package documentation for executable instructions.

Start with what you need

Find the right route through DigiMic

Choose an implementation

Compare the available Python and Julia packages and find their owned documentation.

Packages and training

Apply a workflow

Start from a shared scientific definition before selecting package-specific instructions.

Research workflows

Get help or contribute

Report a package issue, propose a model extension, or bring a dataset or research question.

Support and contribution

Project vision

A bridge from metabolism to microbiome prediction

DigiMic is developing the parameterisation and modelling layers needed to connect biological information with community-scale predictions and experimental validation.

See what exists today and what comes next
DigiMic workflow from metabolic parameterisation through microbiome modelling to experimental validation