A new review published in Food Production, Processing and Nutrition synthesizes current evidence on the integration of bioinformatics and machine learning tools for the characterization of probiotics. The study, authored by Goli Asgari, Mohammadamin Rahmani, Alireza Madandar, and colleagues, examines how computational approaches are transforming the discovery, optimization, and validation of beneficial microorganisms. As probiotic applications expand beyond gastrointestinal health to include metabolic, inflammatory, and infectious diseases, the need for efficient methods to evaluate strain viability and functionality has become critical. The review highlights that conventional research faces limitations in experimental speed and predictive accuracy, necessitating the adoption of advanced artificial intelligence and machine learning frameworks to accelerate discovery.
Probiotics are defined by the International Scientific Association for Probiotics and Prebiotics as live microorganisms that confer a health benefit on the host when administered in adequate amounts. These organisms, commonly including lactic acid bacteria, modulate the gut microbiota and support host physiological functions. The review notes that genera such as Lactobacillus and Bifidobacterium have demonstrated clinical benefits in conditions including diarrhea, gastroenteritis, inflammatory bowel diseases, allergic disorders, and metabolic dysfunction. These therapeutic effects are mediated through mechanisms such as pathogen inhibition, immune regulation, and the enhancement of gut barrier integrity, which serves as the first line of defense against pathogens and food-derived antigens.
Despite their therapeutic potential, the stability of probiotics during food processing, storage, and gastrointestinal transit remains a significant challenge. Environmental factors such as heat, acidity, oxygen exposure, and formulation conditions can significantly reduce probiotic viability. The review details that maintaining probiotic stability requires careful control of storage conditions, including temperatures below 4 °C and a pH range between 3 and 7. Failure to meet these conditions can lead to significant loss of viability, which is why understanding the biological effects of these microorganisms alongside the environmental stresses they face is essential for ensuring effective therapies.
Machine learning techniques are presented as a vital link in probiotic research, connecting the characterization of microorganisms to the development of functional products. Supervised learning methods are commonly used to link specific microbial genes, pathways, or taxa to advantageous host phenotypes and clinical outcomes. For instance, regression models can predict continuous outcomes such as the survival rate of probiotic strains under specific pH or temperature conditions, while classification algorithms can distinguish between probiotic and non-probiotic strains. These models enable rapid, accurate, and scalable analysis of multidimensional probiotic datasets, overcoming the traditional trial-and-error limitations that have historically constrained the field.
Bioinformatics provides complementary insights by offering deeper understanding of strain-level genomic traits and functional pathways. The review highlights several specialized databases that support the identification and characterization of probiotic strains, including the PROBIO database, which compiles functional information on 448 commercially available probiotic products and 167 clinically tested strains. Additionally, the Probiotic Bacteria Database (PBDB) aggregates 1,730 probiotic strains isolated from fermented foods, facilitating the prediction of novel probiotic candidates. These resources allow researchers to evaluate probiotic mechanisms, compare strain functionality, and explore candidates for new applications using standardized computational approaches.
Specific machine learning algorithms have been applied to various aspects of probiotic research, from strain identification to formulation optimization. Support Vector Machines (SVMs) are used to classify data in multidimensional space, identifying optimal hyperplanes that maximize the margin between different classes. Random Forest algorithms, which combine multiple decision trees through ensemble learning, have been used effectively in probiotic genomics for selecting important genetic markers and classifying strain functionalities. The review notes that these tools can identify conserved genomic signatures that serve as robust predictive features, with some studies achieving over 90% accuracy in identifying probiotic strains using tRNA genomic features.
The integration of machine learning with experimental gut models has enabled more precise prediction of probiotic behavior. For example, the review cites research by Westfall et al. that combined the ABIOME artificial gastrointestinal system with the MARS algorithm to identify probiotic formulations capable of modulating anti-inflammatory metabolites. This computational modeling approach guides the development of personalized probiotic therapies based on metabolic output. Similarly, McCoubrey et al. applied Active ML to evaluate how excipients influence the growth of Lactobacillus paracasei, using Random Forest and PCA to pinpoint key formulation components affecting bacterial proliferation. These examples underscore the importance of ML-assisted optimization in enhancing probiotic survival during oral delivery.
Genomic analysis and strain identification are further supported by the emerging field of probiogenomics, which sits at the intersection of probiotic research and comparative genomics. By examining the genomes of well-characterized probiotic strains and comparing them to related non-probiotic bacteria, researchers can identify gene clusters and pathways responsible for acid and bile tolerance, epithelial adhesion, and immune modulation. This genomics-driven approach enables the prediction of beneficial traits and supports the discovery of new probiotic strains with enhanced stability and targeted therapeutic effects. The review emphasizes that such methods strengthen both strain authentication and functional characterization, ensuring that probiotic products are scientifically validated.
However, the review also identifies several limitations that currently hinder the widespread adoption of these computational tools. One of the main problems is the lack of standardized data, which affects the performance of models and the reproducibility of results. The quality of input data is of particular importance, as it directly affects the outcomes of the models. Additionally, many studies suffer from small sample sizes, which increase the risk of overfitting and reduce the validity of the results. The lack of clinical validation for computational models is another important limitation, with experimental validation showing that only three out of four predictions were correct in some studies. These shortcomings suggest that while computational models are powerful, they require further refinement and independent validation.
Future directions in the application of bioinformatics and machine learning tools include the use of the iProbiotics tool for rapid screening of probiotics, which facilitates the identification of new and effective microorganisms. The continued use of these tools can help accelerate the discovery of probiotics and expand the possibility of identifying a larger number of beneficial microbiomes. Furthermore, the use of metaproteomics and AI algorithms to analyze multiomics data could help develop accurate and customized probiotics for personalized healthcare. This approach moves from generic probiotics to more specific and personalized therapies, leveraging the enormous diversity of microbiomes that remains largely unexplored.
The review concludes that the integration of bioinformatics and machine learning has significantly strengthened the ability to evaluate and optimize probiotics. ML approaches enable accurate prediction of metabolic behavior, survival, and growth characteristics, while genomic-based tools enable high-precision identification of promising probiotic strains. Together, these computational advances support the development of targeted and personalized probiotic therapies and enhance our understanding of probiotic mechanisms of action. Continued research and innovation in both biotechnology and computational science are essential to fully realize the therapeutic potential of probiotics and to advance their application in promoting human health.
The review situates the modern probiotic concept within a historical framework that spans more than a century. The term originates from Latin and Greek roots meaning “for life,” and its scientific usage emerged in the twentieth century. Lilly and Stillwell first introduced the term in 1965 to describe substances produced by one organism that stimulate the growth of another. Fuller later refined the definition in 1992 to “live microbial feed supplements that benefit the host by improving intestinal microbial balance.” Early empirical observations also hinted at therapeutic potential; for instance, during World War II, the antimicrobial activity of Bacillus subtilis in traditional remedies was noted, illustrating the long-standing use of naturally occurring bacteria to counter gastrointestinal infections.
Probiotics exert biological effects through multiple specific mechanisms that support host physiology. They contribute to host metabolism by participating in the synthesis and utilization of nutrient substrates such as glucose, amino acids, and short-chain fatty acids (SCFAs). Gut bacteria metabolize complex carbohydrates into SCFAs, including acetate, propionate, and butyrate, which serve as key energy sources for colonocytes and play important roles in immune modulation and gut homeostasis. Additionally, probiotics enhance gut defenses through the production of antimicrobial substances, including organic acids and bacteriocins, which inhibit pathogenic colonization. They also modulate the intestinal immune system by interacting with innate immune receptors, activating Toll-like receptors, and stimulating cytokine production and macrophage phagocytosis.
The enteric nervous system, often referred to as the “second brain,” forms a highly coordinated network that interacts closely with the gut microbiota to regulate motility, secretion, and visceral signaling. Probiotics and microbial metabolites can influence this gut–brain axis, affecting both digestive and neurological functions. Beyond neurological connections, probiotic supplementation has been linked to systemic benefits, including improved bone density. These effects are attributed to the production of SCFAs and the reduction of pro-inflammatory cytokines, both of which mitigate risk factors associated with bone loss. Probiotics also exert hypocholesterolemic effects through mechanisms such as bile acid deconjugation, cholesterol assimilation, and the binding of cholesterol to microbial cell surfaces.
Preserving probiotic viability during manufacturing requires specific technical strategies to counteract environmental stressors. Two common drying techniques, freeze-drying and fluid-bed drying, differ in their impact on bacterial viability. Studies indicate that probiotics generally exhibit greater stability when processed via fluid-bed drying, possibly due to structural changes such as capsule shrinkage and collapse that enhance protection during storage. When drying is necessary, fluid-bed drying is preferred; however, in cases where a specific drying method is not required, microencapsulation is considered the most effective strategy. Microencapsulation has been shown to significantly improve probiotic viability and stability by providing physical protection during storage and processing. Other approaches to improve survival under stress include adapting probiotics to sub-lethal stress before processing and reducing oxidative damage through antioxidants.
Recent advances in CRISPR-Cas technologies have enabled precise genetic engineering of probiotic microorganisms, significantly expanding their functional and therapeutic potential. CRISPR-Cas systems have been applied to improve probiotic safety, enhance stress tolerance, and introduce or modulate beneficial metabolic and immunomodulatory traits without relying on antibiotic resistance markers. In probiotic Lactobacillus and related genera, CRISPR-based genome editing and transcriptional regulation allow for strain-specific modifications and the development of “designer probiotics.” These tools facilitate the removal of undesirable genes and the stabilization of engineered traits, supporting the development of next-generation probiotics for targeted health and industrial applications.
Subject of Research: Integration of bioinformatics and machine learning in probiotic research
Article Title: Probiotics in disease prevention and treatment: integration of bioinformatics and machine learning tools for their characterization
Article References: Asgari, G., Rahmani, M., Madandar, A., Aldaghi, T., Khalili-Tanha, G., & Nazari, E. (2026). Probiotics in disease prevention and treatment: integration of bioinformatics and machine learning tools for their characterization. Food Production, Processing and Nutrition, 8(1), Article 49. https://doi.org/10.1186/s43014-026-00389-6
Image Credits: AI Generated
DOI: 10.1186/s43014-026-00389-6
Keywords: probiotics, machine learning, bioinformatics, gut microbiota, strain selection, genomics, disease prevention, artificial intelligence, disease, prevention, treatment, integration
News Source: Blake Davidson. (November 13, 2026). AI and Bioinformatics Accelerate Probiotic Strain Selection. Scienmag.
