How is AI transforming our understanding of the human microbiota?

How is AI transforming our understanding of the human microbiota?

Kevin Meza Achahue, Scientist - R&D Specialist at Bifidice. 3 minutes reading.

Probably the field that has experienced the greatest advancement and popularity in this century is Artificial Intelligence (AI), a multidisciplinary discipline centered on computer science that enables the development of systems and technologies capable of performing tasks that normally require human intelligence. In some way, the goal is for these systems to "think" similarly to humans, allowing them to learn from data, adapt to new situations, and improve over time without direct human intervention.

With multiple applications ranging from text recognition and natural language processing to aerospace development, AI is setting the pace for many scientific developments, and the world of gut microbiota and probiotics is no exception. Let's look at some applications and the aspirations of this developing field!

Artificial Intelligence and the Secrets of Gut Microbiota

Studying an individual's microbiota is no easy task. In addition to the technical difficulties involved in understanding the microbiota, such as the inability to culture certain strains, especially anaerobic microorganisms or those with very specific nutritional needs, or to accurately recreate the human microbiota in animal models [1][2], there’s a significant challenge: the enormous and complex amount of data that needs to be analyzed.

Methods are being developed that enable the establishment of previously unseen connections between various microbial species

Modern studies investigating the composition of the microbiota generally generate a large amount of data from the DNA sequencing of resident bacterial species and go through several bias-prone stages, resulting in less accurate outcomes than expected [1][3]. However, thanks to Artificial Intelligence, methods are being developed that allow for the processing and understanding of this information, enabling the establishment of previously unseen connections between various microbial species and their environment [4].

AI techniques are allowing for more precise classifications of the microbiome composition, even at the strain level, which also enables the correlation of different compositions with health and disease states. For example, deep learning has been widely used to stratify patients based on the composition of their gut microbiome, aiding in the diagnosis and treatment of diseases such as inflammatory bowel disease, obesity, and colorectal cancer [5]. These predictive models are opening the door to personalized medicine, where supplements like probiotics are gaining increasing relevance.

The Personalization of Probiotics

The ability to more comprehensively characterize each person's microbiota is facilitating the creation of personalized therapeutic interventions to control potential dysbiosis. AI is being used to understand the possible responses of human microbiomes to supplements, foods, and various medications [6]. 

Some researchers can even predict how a group of probiotics can work together to maximize the production of bioactive metabolites with therapeutic properties.

An interesting and developing idea is the creation of models that can predict how certain probiotic strains will affect specific microbiotas. By simulating these interactions, AI can help optimize doses and formulations to achieve maximum efficacy, which can expedite the development of personalized probiotics. But that’s not all. Thanks to machine learning algorithms, some researchers can even predict how a group of probiotics can work together to maximize the production of bioactive metabolites with therapeutic properties. This enables the creation of new probiotic combinations designed for specific health conditions [7][8].

From strain selection and engineering to microbiome analysis and predictive models, AI is driving a paradigm shift in digestive wellness. As we embrace these new and bold strategies, we stand on the brink of a gut health revolution, poised to reshape individual well-being and the future business and industrial landscape.



[1] Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021). Identifying biases and their potential solutions in human microbiome studies. Microbiome, 9(1), 113. 

[2] National Academies of Sciences, Engineering, and Medicine; Division on Earth and Life Studies; Board on Life Sciences; Board on Environmental Studies and Toxicology; Committee on Advancing Understanding of the Implications of Environmental-Chemical Interactions with the Human Microbiome. (2017). Environmental chemicals, the human microbiome, and health risk: A research strategy. National Academies Press (US). Available from 

[3] Singh, R. P., Shadan, A., & Ma, Y. (2022). Biotechnological applications of probiotics: A multifarious weapon to disease and metabolic abnormality. Probiotics and Antimicrobial Proteins, 14(6), 1184–1210.

[4] Medina, R. H., Kutuzova, S., Nielsen, K. N., Johansen, J., Hansen, L. H., Nielsen, M., & Rasmussen, S. (2022). Machine learning and deep learning applications in microbiome research. ISME Communications, 2, Article 98. 

[5] Marcos-Zambrano, L. J., Karaduzovic-Hadziabdic, K., Loncar Turukalo, T., Przymus, P., Trajkovik, V., Aasmets, O., Berland, M., Gruca, A., Hasic, J., Hron, K., Klammsteiner, T., Kolev, M., Lahti, L., Lopes, M. B., Moreno, V., Naskinova, I., Org, E., Paciência, I., Papoutsoglou, G., ... Truu, J. (2021). Applications of machine learning in human microbiome studies: A review on feature selection, biomarker identification, disease prediction and treatment. Frontiers in Microbiology, 12, 634511. 

[6] Puschhof, J., & Elinav, E. (2023). Human microbiome research: Growing pains and future promises. PLoS Biology, 21(3), e3002053. 

[7] Abouelela, M. E., & Helmy, Y. A. (2024). Next-generation probiotics as novel therapeutics for improving human health: Current trends and future perspectives. Microorganisms, 12(3), 430. 

[8] Westfall, S., Carracci, F., Estill, M., Zhao, D., Wu, Q.-L., Shen, L., Simon, J., & Pasinetti, G. M. (2021). Optimization of probiotic therapeutics using machine learning in an artificial human gastrointestinal tract. Scientific Reports, 11, 1067. 

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