Gut bacteria play a major role in human health, influencing everything from digestion to immunity and mood. Yet the scale and complexity of data relating to the microbiome and a series of complex health conditions has made gaining precise insights challenging.
The intestines harbour about 100 trillion bacterial cells. These organisms produce and modify thousands of compounds called metabolites. These small molecules act as chemical messengers, circulating through the body and influencing metabolism, immunity, and even brain function.
Understanding how specific bacteria produce particular metabolites could unlock new ways to support overall health. Recently, artificial intelligence has been developed to decode the intricate ecosystem of gut bacteria and their chemical signals.
By using a Bayesian neural network called VBayesMM, scientists from the University of Tokyo have been able to identify biological links to studies of obesity, sleep disorders, and cancer.
Bayesian neural networks
A Bayesian neural network is an artificial neural network that treats its weights and biases as probability distributions rather than fixed, absolute numbers. This probabilistic approach enables the model to quantify its prediction confidence.
This helps the AI to help separate aleatoric uncertainty (noise inherent in the data itself) from epistemic uncertainty (uncertainty due to a lack of training data).
Aleatoric uncertainty refers to the inherent randomness or variability in a system that cannot be reduced, even with more data. With microbiology, examples include:
- Variation in bacterial growth rates under the same conditions,
- Random differences in colony counts between replicate plates,
- Biological variability in patient microbiomes,
- Noise in experimental measurements (e.g., pipetting variation, sampling error).
In contrast, epistemic uncertainty refers to uncertainty that arises from lack of knowledge or incomplete understanding of a system. Such uncertainty can be reduced with more data, better models, or improved measurement.
VBayesMM, uses a Bayesian approach to detect which bacterial groups significantly influence particular metabolites. The system also measures uncertainty in its predictions, helping prevent overconfident but incorrect conclusions.
The aim is to provide confidence that discovers real biological relationships rather than meaningless statistical patterns.
Seeking to stabilise microbial data chaos
According to lead researcher Tung Dang: “The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases.”
Dang adds: “By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.”
Ongoing progress
The system is not foolproof and it requires further iteration to become fully useful, For instances, one limitation is that VBayesMM treats bacteria as independent actors, even though they often interact in complex, interdependent networks.
Nonetheless, by using AI to navigate the vast and intricate world of gut microbes, researchers are moving closer to unlocking the microbiome’s potential to transform personalized medicine.
The research appears in the journal Briefings in Bioinformatics, with the research paper titled “VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.”
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