Gut bacteria are fundamental to many aspects of human health, from digestion to immunity. Yet, the complexities of these microbial communities – along with the vast number of different species and metabolites they produce – make it challenging to study how they interact with the body.
Researchers at the University of Tokyo have applied a type of artificial intelligence (AI) technique to explore large datasets on gut bacteria, aiming to uncover relationships that traditional analytical tools have struggled to reveal.
Their advance is published in Briefings in Bioinformatics.
Challenges in mapping microbial interactions
The human body hosts around 30 to 40 trillion cells, yet the intestines house approximately 100 trillion bacteria. These microbes play critical roles in digesting food, but they also influence metabolism, immune responses, and even mental health.
The bacteria produce a wide variety of metabolites, which act as molecular messengers throughout the body. Understanding the intricate relationships between these bacteria and their metabolites could open the door to personalized treatments for a range of health conditions.
“The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,” said project researcher Tung Dang, from the Tsunoda lab in the Department of Biological Sciences. “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.”
However, identifying meaningful patterns within the vast amounts of data generated by gut microbiome studies is a complex task. The sheer number of bacteria and metabolites involved, combined with their interactions, presents a formidable analytical problem.
Bayesian neural networks to the rescue
To tackle this challenge, Dang and his team began to explore whether state-of-the-art AI tools could be applied to this problem. The result – a variable Bayesian neural network model known as VBayesMM.
“Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers,” said Dang.
Bayesian neural network
A type of artificial intelligence model that uses probability theory to manage uncertainty in predictions. This method is particularly useful in complex datasets, where traditional models may not adequately account for uncertainties or variability.
“When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns,” said Dang.
One of the main advantages of VBayesMM is that it can handle and communicate uncertainty, which can give researchers more confidence in its outputs than a tool which cannot. The system is also optimized to cope with heavy analytical workloads, such as the huge datasets that must be processed to understand the gut microbiome.
Limitations and future improvements
Despite its advantages, the system is not without limitations. One key challenge is the need for more detailed bacterial data compared to the metabolites they produce. When the available bacterial data is insufficient, the system’s accuracy drops. Additionally, the model assumes that microbes act independently, though in reality, they interact in highly complex ways, making it difficult to model these relationships fully. Despite its optimization for heavy workloads, the system does still carry a relatively high computational cost which may be a barrier to some groups.
Looking ahead, Dang and his team plan to integrate more comprehensive datasets that include a broader range of bacterial metabolites.
“We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet,” said Dang.
“We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial ‘family tree’ relationships to make better predictions, and further reducing the computational time needed for analysis.”
With these improvements and adjustments, the team hopes that the insights gained from this work could lead to new clinical treatments based on the manipulation of the microbiome.
“For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications,” Dang said.
Reference: Dang T, Lysenko A, Boroevich KA, Tsunoda T. VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data. Brief Bioinform. 2025;26(4). doi: 10.1093/bib/bbaf300
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