OUR SCIENCE
Health effects depend on how microbes work together
Conventional microbiome analysis focuses on differences in the relative abundance of individual microbes between health states, population groups, or before and after an intervention. Post analysis, uses functional annotation to infer what might be happening.
But microbes do not act in isolation. They continuously interact with one another in many different ways.
A health condition or an intervention alter how microbes function together, even when their individual abundances remain unchanged.
Minutia.AI’s proprietary systems-biology AI uses Bayesian frameworks to analyse microbes activity in their broader community context.
By revealing changes in how microbial communities are organised, Minutia.AI uncovers biological signals that abundance analysis alone may miss, providing unique evidence to support health claims.
Diagram showing bacterium interactions in different contexts
OUR FLAGSHIP PRODUCTS
Discovery Platform
Discovery is a context aware AI layer that integrates seamlessly into your existing microbiome analysis workflows. It reveals how the same taxon can participate in different ecological settings across people, allowing to detect intervention associated changes that are not explained by relative abundance alone.
We explain why
Why Choose Discovery?
Find intervention associated & clinically relevant effects where conventional analysis did not through our systems level analysis of microbial context.
Support more precise prediction of intervention response and clinical outcomes with our proprietary model & high quality data.
Strengthen biological evidence & substantiate multiple intervention associated or clinically relevant effects by combining changes in taxon abundance as well as context.
Digital Twin
Minutia Twin creates a digital replica of an individual's microbiome, constructed from high quality partner data and our proprietary Bayesian causal network models of microbial interactions.
Our digital twin uses causal modelling to simulate how an intervention may change the gut microbiome and estimate its potential impact on a biological or clinical outcome. The twin is able to isolate the causal mechanism of change, linking intervention and outcome.
Why Choose Digital Twin?
Substantiate health claims with cause
The FDA uses bayesian modelling to determine cause and effect in functional foods, pharmaceuticals and licensed microbiome therapies. Our twin uses this same technology for your intervention modelling.
Pre-specify your mechanism
Substantiate your clinically relevant effects with our twin isolating the exact mechanism of change.
Risk-Free Experimentation
De-risk your trials by simulating multiple nutritional interventions and scenarios simultaneously without patient risk or resource waste. Pick the right subjects, the right study size & product to test using the twin.
Live Simulation
Health Probability
Healthy
78%
IBD Risk
22%