In Silico Modeling of Metabolic Pathways in Probiotic Microorganisms for Functional Food Biotechnology

Abstract

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research.

1. Introduction

In recent years, increasing attention has been paid to the role of the microbiome in host physiology, immune responses, and the maintenance of metabolic homeostasis [1]. This growing interest has driven the expansion of research efforts focused on understanding and controlling microbial communities. Natural microbial consortia exhibit remarkable functional versatility, ranging from participation in environmental nutrient cycling to complex host–microbe interactions, particularly within the human gut [2]. However, the targeted engineering of such communities requires tools that not only reflect their behavior in natural conditions but also support the construction of synthetic consortia with defined functional properties. These artificial communities must demonstrate long-term stability, biosafety, scalability, and cost-effectiveness for practical applications [3].

Considering the strain-specific nature of microbial metabolism and the rapid growth of genomic and other omics datasets, mathematical modeling has emerged as a key approach for data integration and the rational design of microbial communities [4]. Among the most widely applied tools in microbial systems biology are genome-scale metabolic models (GSMMs), which represent all metabolic reactions encoded in the genome of a given organism [5]. These models incorporate data on genes, enzymes, metabolites, and cofactors, and are constructed under stoichiometric, thermodynamic, and resource-related constraints. As such, they belong to the class of constraint-based models [6].

One of the most commonly used methods for simulating such models is flux balance analysis (FBA) [7]. This approach applies linear programming to identify an optimal distribution of metabolic fluxes that achieves a specific cellular objective, most often the maximization of biomass production [8]. FBA assumes steady-state intracellular metabolite concentrations, and fluxes are calculated based on predefined constraints related to reaction stoichiometry and substrate availability. The accuracy of FBA predictions strongly depends on the quality and completeness of the underlying model [9].

A key advantage of FBA is its ability to simulate a wide range of in silico conditions, such as nutrient limitations, gene knockouts, or changes in medium composition [10]. The choice of the objective function significantly influences model behavior. In addition to the classical objective of biomass maximization, alternative formulations, such as MOMA (Minimization of Metabolic Adjustment) and ROOM (Regulatory On/Off Minimization), can be employed to evaluate mutant phenotypes or regulatory responses [11,12]. Several extensions of FBA have also been developed to incorporate system dynamics (dynamic FBA, dFBA) or regulatory mechanisms (regulatory FBA, rFBA; integrated FBA, iFBA) [13].

Nevertheless, FBA has certain limitations. The assumption of steady-state metabolite concentrations may not hold under rapidly changing environmental conditions, and the accuracy of predictions is highly sensitive to the correctness of imposed constraints [14]. Additionally, under non-standard conditions or when the genome is incompletely annotated, the model may yield inaccurate results [15]. Despite these limitations, constraint-based approaches remain essential tools for systems-level analysis and the rational design of microbial metabolism [16].

Modeling microbial communities, particularly multispecies consortia, such as the human gut microbiota, presents an even greater challenge. Microorganisms differ in the extent of genome annotation, and the level of detail in their metabolic models can vary considerably [17]. Moreover, such models must account for interspecies metabolite exchange, resource competition, and spatial niche separation [18]. These complexities necessitate the use of specialized modeling strategies.

Modern computational tools enable the representation of both the biochemical properties of individual strains and their interactions within the community context [19]. The integration of genomic and metabolic data through mathematical modeling offers new opportunities for the rational development of next-generation probiotics and for targeted microbiota modulation in support of human health and biotechnological innovation [20].

This review does not aim to cover all variants of flux balance analysis (FBA), but rather to highlight its application in engineering tasks related to probiotics. We propose that FBA and its advanced variants represent powerful tools for the rational design of next-generation probiotics, especially when used in conjunction with high-quality, well-annotated genome-scale metabolic models (GSMMs) and microbial community models.

2. Mechanisms of Microbiota–Host Interactions and Current Approaches to Probiotic Modulation

Current Understandings of the Role of the Microbiota in Health and Disease

The human microbiota is a complex community of microorganisms that inhabit various epithelial and external surfaces of the body, including the skin, oral cavity, gastrointestinal tract (GIT), reproductive system, and other anatomical niches [21,22,23]. Each of these niches is characterized by a unique microbial composition and functional specialization. Notably, microbial diversity is observed not only between different organs but also between adjacent regions within the same organ. For instance, the microbial composition of the duodenum differs from that of the ileocecal junction, despite their close anatomical proximity [24,25,26]. Microorganisms often engage in symbiotic relationships with their host, contributing to essential physiological processes while benefiting from a stable and nutrient-rich environment. This mutualism underpins key functions, such as digestion, vitamin synthesis, and immune regulation [27,28]. However, disruptions in the composition and stability of microbial communities can lead to dysbiosis, a condition characterized by microbial imbalance, often associated with adverse health outcomes [29]. This phenomenon is particularly evident in the gastrointestinal tract (GIT), where the microbiota regulates metabolism, modulates immune responses, and participates in inflammatory processes. Broad-spectrum antibiotic therapy or infectious diseases, for instance, can disturb microbial communities and trigger dysbiosis [30].

A growing body of research has established links between dysbiosis and various pathologies, including inflammatory bowel disease (IBD) [31,32], obesity [33], and cancer [34], as well as neurodegenerative and neuropsychiatric disorders [35]. Similar associations have been observed in other anatomical niches. For example, an imbalance in the skin microbiota has been correlated with acne and seborrheic dermatitis [36,37]; alterations in the oral microbiota are linked to gingivitis and periodontitis [38,39]; and vaginal dysbiosis is often characterized by a reduction in Lactobacillus spp. and increased microbial diversity [40].

These observations have stimulated growing interest in the molecular basis of host–microbiota interactions and the therapeutic potential of microbiome-targeted interventions. Among the most actively explored strategies is the use of probiotics and prebiotics aimed at restoring and maintaining a healthy microbial balance. Probiotics are defined as live microorganisms, typically isolated from fermented foods, which, when administered in adequate amounts, confer health benefits to the host [41]. Notably, probiotics do not always become permanent residents of the host microbiota, often exerting transient but functionally relevant effects.

Modern probiotic formulations may contain either a single microbial strain or multiple strains, thereby mimicking natural symbiotic communities. In contemporary research, increasing emphasis is placed not only on the correlations between microbiota composition and health status, but also on elucidating the molecular mechanisms underlying probiotic activity. For example, it has been demonstrated that Lactococcus lactis can reduce intestinal inflammation by modulating immune responses [42]. The therapeutic efficacy of probiotics has been confirmed in the treatment of ulcerative colitis [43], irritable bowel syndrome [44], and Crohn’s disease [45].

The taxonomy of probiotic microorganisms encompasses a broad range of genera, most of which belong to the phyla Firmicutes and Actinobacteria [46]. Among the most extensively studied and clinically validated probiotic genera are lactobacilli, Bifidobacteria, Bacillus, Streptococcus, and Enterococcus (Table 1) [47].

Each of these genera exhibits distinct phenotypic and metabolic traits that contribute to their successful colonization of the gastrointestinal tract and their beneficial effects on host health through multiple, interconnected mechanisms [48]. These genera demonstrate diverse ecological strategies and physiological capacities that enable their persistence within the human gut and support their multifaceted health-promoting effects. Their synergistic interactions with dietary components and resident microbial communities form the basis for their clinical application and guide future directions in microbiota-based therapeutic development (Table 1) [47].

Table 1. Key probiotic genera, functional roles, and metabolites in the gut environment.

Genus	Notable Species	Strain	Oxygen Requirement	Key Functions in the Gut	Notable Metabolites	References
Lactobacillus (now includes Lacticaseibacillus)	Lacticaseibacillus rhamnosus Lactobacillus acidophilus	GG NCFM	Facultative anaerobe	Acidification, pathogen inhibition, immune modulation	Lactic acid, bacteriocins, SCFAs	[49,50]
Bifidobacterium	Bifidobacterium breve, Bifidobacterium longum	M-16V BB536	Obligate anaerobe	Fiber fermentation, vitamin synthesis, immune development	Acetate, lactate, folate	[51,52]
Bacillus	Bacillus subtilis, Weizmannia coagulans	DE111 GBI-30	Aerobe/facultative	Enzyme production, pathogen exclusion, immune activation	Proteases, lipopeptides, IgA stimulation	[53,54]
Streptococcus	Streptococcus thermophiles	TH-4	Facultative anaerobe	Lactose hydrolysis, anti-inflammatory action	β-Galactosidase, EPS	[55]
Enterococcus	Enterococcus faecium	SF68	Facultative anaerobe	Bile salt hydrolysis, antimicrobial activity, lipid metabolism	Enterocins, BSH enzymes	[56]

Table 1. Key probiotic genera, functional roles, and metabolites in the gut environment.

Genus	Notable Species	Strain	Oxygen Requirement	Key Functions in the Gut	Notable Metabolites	References
Lactobacillus (now includes Lacticaseibacillus)	Lacticaseibacillus rhamnosus Lactobacillus acidophilus	GG NCFM	Facultative anaerobe	Acidification, pathogen inhibition, immune modulation	Lactic acid, bacteriocins, SCFAs	[49,50]
Bifidobacterium	Bifidobacterium breve, Bifidobacterium longum	M-16V BB536	Obligate anaerobe	Fiber fermentation, vitamin synthesis, immune development	Acetate, lactate, folate	[51,52]
Bacillus	Bacillus subtilis, Weizmannia coagulans	DE111 GBI-30	Aerobe/facultative	Enzyme production, pathogen exclusion, immune activation	Proteases, lipopeptides, IgA stimulation	[53,54]
Streptococcus	Streptococcus thermophiles	TH-4	Facultative anaerobe	Lactose hydrolysis, anti-inflammatory action	β-Galactosidase, EPS	[55]
Enterococcus	Enterococcus faecium	SF68	Facultative anaerobe	Bile salt hydrolysis, antimicrobial activity, lipid metabolism	Enterocins, BSH enzymes	[56]

These microorganisms possess unique physiological and metabolic characteristics that enable them to survive and function within the gastrointestinal tract (GIT). For example, many strains of lactobacilli and Bifidobacteria synthesize ATP-dependent proton pumps (e.g., the F₁F₀-ATPase), which facilitate proton efflux and help maintain intracellular pH under acidic stress conditions [57]. Another key adaptation mechanism involves the activity of bile salt hydrolase (BSH) enzymes, found in lactobacilli, Enterococcus, and Bifidobacteria, which contribute to the detoxification of bile acids [58].

In addition, many probiotics produce extracellular polysaccharides (EPSs) that protect cells from environmental stress, promote adhesion to epithelial surfaces, and mediate interactions with the host immune system [59]. Once colonized in the gut, probiotic microorganisms, such as Bifidobacteria and lactobacilli, do not act in isolation, but integrate into complex microbial communities. They participate in cross-feeding networks, quorum sensing, and competitive exclusion of pathogens [47,60].

One of the key functional outcomes of probiotic activity is the production of short-chain fatty acids (SCFAs)—primarily acetate, propionate, and butyrate. For instance, bifidobacteria ferment complex prebiotic carbohydrates to generate acetate and lactate, which are subsequently utilized by butyrate-producing bacteria, such as Faecalibacterium prausnitzii and Eubacterium rectale [59]. Butyrate, in turn, serves as a primary energy source for colonocytes, strengthens the intestinal barrier, reduces oxidative stress, and regulates pro-inflammatory cytokines [58]. Lactobacilli species commonly rely on glycolysis (Embden–Meyerhof–Parnas pathway), producing lactic acid that lowers the local pH and inhibits pathogen growth [46].

Probiotics also modulate the expression of genes involved in cell–cell junctions (e.g., occludin and claudin), thereby decreasing epithelial permeability and preventing endotoxemia [57]. By activating innate immune receptors, such as Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain (NOD)-like receptors, probiotics can stimulate anti-inflammatory pathways, promote the differentiation of regulatory T cells, and enhance secretory IgA production [47,59].

The systemic effects of probiotics include modulation of tryptophan metabolism and serotonin synthesis, thereby influencing the gut–brain axis, as well as the regulation of glucose metabolism, adipokine secretion, and bile acid composition [58,61]. Such physiological resilience and metabolic flexibility enable probiotics to exert multi-layered health benefits in the host.

Their metabolic byproducts include SCFAs, vitamins, bioactive amines, and neuroactive compounds such as gamma-aminobutyric acid (GABA), which is synthesized via glutamate decarboxylase activity [62,63,64].

Despite the promise of traditional probiotics, their clinical efficacy remains limited. Therapeutic outcomes are often unpredictable due to poor colonization efficiency, lack of strain-specific functionality, and insufficient adaptation to the host’s gastrointestinal environment. These limitations have driven the development of more targeted and functionally robust microbial therapeutics (Figure 1) [65].

Advances in metagenomics, synthetic biology, and metabolic modeling have enabled the engineering of probiotic strains with predefined therapeutic functions, such as pathogen suppression, metabolic correction, or immunomodulation [66]. These enhanced microbial therapeutics—often referred to as next-generation probiotics—are developed using genetic engineering strategies that allow for the precise modulation of the host microbiota, production of specific metabolites, or interference with host signaling pathways [67].

This approach opens new avenues for the development of personalized probiotics designed to treat specific diseases, including inflammatory bowel disease, obesity, diabetes, and neurodegenerative disorders [68].

3. Metabolic Modeling of Bacterial Systems for Biotechnological Applications

3.1. Metabolic Modeling of Single Bacterial Species for Biotechnological Purposes

Genome-scale metabolic models (GSMMs) of individual microbial species are widely employed in addressing various challenges in biochemical engineering. Within this engineering framework, microorganisms are conceptualized as “cellular factories,” in which complex biochemical processes can be formalized through metabolic models and optimized to achieve specific biotechnological objectives [69].

One prominent area of application for flux balance analysis (FBA) is bioremediation—the use of microorganisms to remove environmental pollutants [70]. Current research increasingly focuses on the design of synthetic microbial consortia capable of efficiently degrading contaminants while maintaining ecological balance. This requires not only the identification of species harboring appropriate catabolic pathways, but also an understanding of the potential metabolic interactions among community members [71].

An illustrative example is the modeling of polychlorinated biphenyl (PCB) bioaccumulations, which are persistent organic pollutants found in marine environments. It has been demonstrated that Pseudomonas putida is capable of degrading PCBs, and FBA has been employed to simulate the metabolic pathways involved. The model showed that optimal bacterial growth is achieved at high levels of PCB uptake, while the introduction of additional degradation pathways did not enhance growth, underscoring the need for a balanced design of metabolic networks [72].

Another key application of systems biology approaches, including FBA, lies in biofuel production [73,74]. Certain bacterial species are able to ferment plant biomass to produce compounds such as ethanol [75], butanol, and hydrogen gas via biological synthesis pathways [76,77]. The main challenge here is to optimize product yields while minimizing resource input, which requires both maximizing culture growth and enhancing flux through key metabolic routes. In addition, strain tolerance to toxic byproducts of fermentation must be taken into account [78,79].

To address these challenges, a range of metabolic engineering strategies has been developed. One promising approach is the creation of strains with overexpressed efflux systems that facilitate the removal of toxic compounds from the cell. Another effective strategy involves redirecting flux away from pathways responsible for generating inhibitors such as furans, aliphatic acids, and phenolic compounds—an especially critical consideration when working with lignocellulosic biomass derived from renewable resources [80].

Overall, FBA offers a powerful framework for analyzing and optimizing the metabolism of bacterial strains, enabling the prediction of metabolic flux redirection and the design of strategies to increase the yield of target compounds in industrial biotechnology processes.

3.2. Modeling of Multi-Species Bacterial Communities

The transition from single-species to multi-species metabolic modeling represents a crucial advancement in in silico approaches to studying microbial ecosystems. This shift enables a deeper understanding of the complex metabolic interactions among diverse microbiota members, as well as predictions of how external and internal factors influence the composition and functionality of microbial communities.

One of the key tools for modeling metabolic networks in microbial consortia is community flux balance analysis (cFBA) [81]. In contrast to classical FBA, cFBA accounts for the metabolism of individual species and the exchange of metabolites between them, applying stoichiometric and thermodynamic constraints to interspecies interactions [82]. This approach allows for the estimation of maximal community growth rates under defined conditions, simulation of intra- and interspecies metabolic fluxes, and prediction of the relative abundance of specific taxa. A classic example is the modeling of two-species symbiosis in which one organism utilizes glucose and ammonium to produce succinate, which is then consumed by a second organism to regenerate ammonium, forming a mutually beneficial metabolic loop [83].

To incorporate temporal dynamics into these systems, the community dynamic FBA (cDFBA) approach has been developed, extending cFBA with a time-dependent component [84]. For instance, modeling anaerobic ammonium oxidation (ANAMMOX) within biofilms has demonstrated that the survival of Anammox bacteria depends on microenvironmental fluctuations between aerobic and anaerobic zones [85].

More generalized frameworks are also emerging, combining metabolic modeling with game theory and the Nash equilibrium concept, wherein each species optimizes its own growth while competing for shared resources [86]. However, such models often fail to fully capture cooperative interactions and context-dependent adaptation, limiting their applicability in symbiotic systems [87].

With the continued development of genome-scale metabolic models (GSMMs) and the growing availability of multi-omics datasets, the integration of FBA into the analysis of complex microbial communities has greatly expanded. One of the most promising systems in this context is the human gut microbiota—a community characterized by high species richness and intense metabolic activity [88]. The composition of the gut microbiome is influenced by numerous factors, including diet, age, geography, health status, and medical interventions. Dysbiosis, or disruption of the microbial balance, has been associated with inflammatory bowel diseases, infections, immune dysregulation, and metabolic disorders [89].

The use of genome-scale metabolic models (GSMMs) and flux balance analysis (FBA) enables the prediction of microbiota responses to various interventions, such as antibiotic treatment, probiotic and prebiotic administration, and fecal microbiota transplantation (FMT) [90]. For instance, metabolic modeling of the gut microbiota in type 2 diabetes patients receiving metformin revealed that the drug promotes the growth of Escherichia spp., Akkermansia muciniphila, and Subdoligranulum variabile, while decreasing Intestinibacter bartlettii and other members of the Firmicutes phylum [91]. Further metabolic analysis linked these shifts to changes in the production of short-chain fatty acids (notably butyrate), along with altered lipopolysaccharide and carbohydrate metabolism—factors that may exacerbate intestinal inflammation and compromise epithelial barrier integrity [91].

A promising application of metabolic modeling is the design of personalized prebiotics aimed at selectively promoting the growth of beneficial bacterial strains. For example, Marinos et al. [92] constructed a model of gut bacterial interactions between MYb11 and MYb71 in the nematode Caenorhabditis elegans. In silico screening identified candidate prebiotic compounds—L-serine, L-threonine, D-mannitol, and gamma-aminobutyric acid (GABA)—that selectively enhanced the growth of the protective strain MYb11: these predictions were validated both in vitro and in vivo [92].

Further exploration of GSMM applications in the gut microbiota research highlights that their utility extends beyond describing metabolic interactions within microbial communities or between the microbiota and host [93]. These models serve as powerful tools for the rational design of therapeutic strategies to restore microbial homeostasis, including the development of next-generation probiotics and the engineering of synthetic microbial consortia with defined functional properties. Due to their capacity to integrate diverse omics data and simulate interspecies metabolite exchange, GSMMs enable the prediction of microbial population dynamics in response to environmental shifts within the gut, positioning them as essential components of personalized therapeutic development (Figure 2) [94].

Metabolic modeling of microbial communities integrates multi-omics data and computational tools to address a range of biological and biomedical questions. Insights into intra- and interspecies metabolic interactions, as well as host–microbiota relationships, combined with genome-scale metabolic models (GSMMs) and flux balance analysis (FBA), enable the rational design and evaluation of probiotics as specialized microbial consortia. By accounting for microbial diversity and abundance in the gut ecosystem, this approach supports the development of both conventional and next-generation probiotics (NGPs) for personalized therapeutic applications.

In recent years, metabolic modeling has increasingly been applied to assess the consequences of dysbiosis, analyze the structure and stability of microbial communities, and support the development of engineered microbial therapeutics [93]. In this context, mathematical approaches that predict microbial behavior following intervention are gaining importance. Notably, recent studies employing paired GSMMs and co-flux balance analysis (co-FBA) have demonstrated the feasibility of evaluating long-term engraftment and ecological stability of specific strains within the intestinal ecosystem [94,95].

Nevertheless, the behavior of dynamic microbial communities remains difficult to predict. The use of traditional probiotics, primarily single-species cultures derived from food-grade microorganisms, has shown limited effectiveness in modulating the microbiota and achieving long-term colonization [95]. As a logical next step, the design of multi-species synthetic communities guided by metabolic models has emerged. These synthetic consortia aim to address key pathogenic features of dysbiosis, such as reduced diversity of commensal microbes, increased abundance of opportunistic and pathogenic species, and the persistence of chronic inflammation. Integrating metabolic modeling into the design phase enables consideration of critical parameters, including the colonization capacity of strains, their resilience under harsh gastrointestinal conditions, long-term stability, and safety for the host organism [96].

Furthermore, metabolic models offer a powerful means to integrate extensive biological data into unified computational frameworks capable of predicting the performance of probiotic formulations and synthetic communities. This is particularly important when analyzing host–microbiota interactions, including their effects on immune responses, intestinal barrier integrity, and systemic metabolism [94]. Research in this area also encompasses the development of advanced probiotic delivery systems aimed at maximizing microbial viability during gastrointestinal transit and ensuring successful colonization in the gut.

Given the growing recognition of the microbiome as a central determinant of human health and a viable therapeutic target, the development of effective and safe probiotic formulations has become a priority in modern microbiology [97]. Functional goals of probiotics include the suppression of pathogenic microorganisms, normalization of host metabolism, reinforcement of the intestinal epithelial barrier, modulation of immune responses, and participation in the synthesis of neuroactive compounds that influence the gut–brain axis [47]. The immunomodulatory properties of the microbiota are especially relevant in the context of immune dysfunction. It has been established that short-chain fatty acids (SCFAs) and tryptophan-derived metabolites play a pivotal role in the intersystem communication between the microbiota and the host immune system [52]. These metabolites activate receptors such as the aryl hydrocarbon receptor (AhR), histamine receptors, and purinergic signaling components, thereby contributing to inflammation resolution [98]. Several studies have demonstrated that increased abundance of specific bacterial strains is associated with the attenuation of inflammatory symptoms in conditions such as multiple sclerosis, rheumatoid arthritis, and inflammatory bowel diseases, including ulcerative colitis [57].

Although the molecular mechanisms underlying the immunomodulatory effects of the microbiota and probiotics are not yet fully elucidated, there is compelling evidence that probiotics can induce a regulatory T cell (Treg) phenotype via the TGF-β signaling pathway [99]. This highlights the potential of probiotics as immunomodulatory agents in the treatment of inflammatory and autoimmune disorders. Additionally, probiotics have been shown to influence the phenotype of B cells involved in the humoral immune response [100]. Taken together, these findings suggest that probiotics may facilitate a shift from pro-inflammatory to regulatory immune responses, positioning them as valuable components in the integrative management of immune-related diseases.

The advancement of metabolic modeling and its integration into microbiota research is paving the way for innovations in personalized medicine and microbial bioengineering. The construction of synthetic microbial communities with defined metabolic functions guided by GSMM-based modeling not only enhances our understanding of the ecological architecture of the microbiome but also provides a rational framework for therapeutic manipulation of microbial composition and activity.

3.3. Metabolic Modeling of Probiotics: CBM, FBA, and MFA Approaches and Their Practical Applications

As discussed earlier, flux balance analysis (FBA) is one of the core tools in systems biology, and its principles and applications to both single-species and multi-species microbial communities have been covered in detail. This section focuses on the comparative evaluation of FBA, metabolic flux analysis (MFA), and dynamic FBA (dFBA), as well as their practical use in probiotic biotechnology.

Metabolic flux analysis (MFA) and flux balance analysis (FBA) represent two principal constraint-based modeling (CBM) strategies for the quantitative assessment of metabolic fluxes [101]. Additionally, we examine the specific features and advantages of these approaches when applied to the engineering of probiotic strains and the optimization of fermentation processes.

Constraint-based modeling (CBM) is a powerful tool in systems biotechnology that enables the optimization of metabolic processes in probiotic microorganisms by constructing cellular metabolic networks based on constraints, such as mass balance, nutrient availability, and regulatory mechanisms. This modeling framework allows for the prediction of metabolic shifts in response to changes in cultivation conditions or genetic modifications—critical for enhancing the viability, productivity, and functional performance of probiotic strains [102].

A central method within CBM is flux balance analysis (FBA), which is used to evaluate the distribution of metabolic fluxes within cellular networks, particularly at the genome scale. FBA facilitates the calculation of the theoretical maximum yield of target metabolites and is widely applied in the design of microbial production strategies. It was first implemented by Papoutsakis et al. in 1984 to predict butyrate yield, and later extended by Varma and Palsson to model growth and metabolite production in various microorganisms [103]. However, the predictive accuracy of FBA may decrease when modeling mutant or genetically engineered strains [104]. In such cases, supplementary approaches are beneficial—for example, Flux Variability Analysis (FVA), which captures the range of feasible fluxes for a given objective function, and the Minimization of Metabolic Adjustment (MOMA) method, which improves prediction accuracy under stress or perturbation conditions [102].

Unlike FBA, metabolic flux analysis (MFA) is an experimental approach aimed at quantifying actual intracellular fluxes. It provides a dynamic view of metabolism and allows for the assessment of how environmental conditions influence biosynthesis pathways (Figure 3) [104].

MFA has been applied, for instance, to optimize β-lactamase production in Bacillus li-cheniformis, as well as to increase L-lactate and cephalosporin C yields in industrial strains [105]. However, MFA often requires simplification of complex metabolic networks and can be strengthened through integration with tools such as FVA. A major development in the field was the creation of the MFlux platform, which aggregates data from over 100 published studies and enables pathway prediction in a wide range of heterotrophic bacteria [106].

The incorporation of temporal dynamics into metabolic models has been achieved through dynamic flux balance analysis (dFBA), which combines the strengths of both FBA and MFA, while accounting for metabolite concentration changes and enzyme kinetics over time [104]. dFBA has been successfully employed to model diauxic growth in Escherichia coli and to predict the production of compounds such as shikimic acid and ethanol [107]. Moreover, dFBA is increasingly integrated with machine learning algorithms, yielding hybrid models capable of accurately predicting cellular behavior, including that of genetically engineered strains [106]. A comparative overview of FBA, MFA, and dFBA methodologies is presented in

Table 2. Comparative features of FBA, MFA, and dFBA for fermentation process analysis.

Criterion	FBA (Flux Balance Analysis)	MFA (Metabolic Flux Analysis)	dFBA (Dynamic Flux Balance Analysis)
Model type	Static, stoichiometric.	Experimental, statistical.	Dynamic, kinetic.
Use of experimental data	No (based on stoichiometry and constraints).	Yes (requires metabolite concentrations, 13C-labeling, etc.).	Partial (combines theoretical models with experimental data).
Temporal resolution	Not considered.	Indirectly, via discrete time-point measurements.	Explicitly incorporated; models time-course dynamics.
Applicability to GM strains	Limited, especially in “knockout” scenarios.	High, provided experimental data is available.	High, particularly when integrated with machine learning.
Complexity level	Low to moderate.	Moderate.	High.
Advantages	Rapid estimation of theoretical yield. Effective for pathway optimization.	Reflects actual cellular behavior. Suitable for industrial strain analysis.	Incorporates kinetics and dynamic behavior. Enables more realistic system-level predictions.
Limitations	Ignores system dynamics. Limited accuracy under stress or mutation.	High data requirement. Complex interpretation.	Computationally intensive. Requires accurate parameterization and calibration.
Typical applications	Theoretical optimization of biosynthetic pathways (e.g., biofuels, amino acids).	Fermentation condition optimization (e.g., β-lactamase, organic acids).	Dynamic system modeling (e.g., shikimic acid production, protein secretion).
Representative studies	[103]	[105]	[107]

.

Recent advances in constraint-based modeling (CBM) are closely linked to the development of genome-scale metabolic models (GEMs), which encompass all possible metabolic reactions of a cell based on its genomic information. A major milestone in this field has been the establishment of the AGORA database, which includes over 800 curated models of gut microorganisms, including probiotic species [10]. The updated version, AGORA2, features more than 7000 reconstructions that account for bile acid metabolism, drug transformation, and virulence factors, thereby providing a robust foundation for personalized modeling of the human microbiome [108].

An illustrative example of GEM application is the development of a genome-scale model for Lactobacillus rhamnosus GG (LGG), which revealed the strain’s metabolic versatility and its ability to utilize a wide range of carbohydrates. The model exhibited good agreement with experimental data, supporting the utility of GEMs in investigating probiotic metabolism. Another significant contribution to standardizing GEM-based approaches is the BiGG Models database, which provides access to high-quality, annotated models and facilitates comparative studies across probiotic strains [109].

Implementation of CBM and GEM approaches relies on dedicated computational tools. Among the most widely used platforms are COBRApy (Python-based), CarveMe, RAVEN, and ModelSEED. These tools enable precise customization of cultivation parameters (e.g., medium composition, pH, temperature, or aeration), prediction of productivity under various conditions, rational selection of optimal strains and consortia, and optimization of the biosynthesis of target metabolites, such as amino acids, vitamins, and short-chain fatty acids [110].

Taken together, the combined application of FBA, MFA, dFBA, and GEM methodologies plays a central role in probiotic biotechnology. Their integration with genomic, metabolomic, and phenotypic data paves the way for the development of highly efficient, reproducible, and personalized cultivation strategies for probiotic microorganisms. These approaches underpin the creation of functional probiotics with predictable performance and high efficacy, which is particularly relevant in the context of advancing medical therapies and functional nutrition.

3.4. Modeling of Probiotic Fermentation Processes: From Mechanistic to Data-Driven Approaches

Modeling of fermentation processes plays a critical role in biotechnology, enabling the prediction of biosystem behavior, optimization of cultivation conditions, and effective control of production workflows [111]. In the context of probiotic fermentation, this task becomes particularly significant due to the high sensitivity of microbial cultures to environmental changes, the need to maintain their viability, and the requirement for stable production of target metabolites.

The most traditional approach involves mechanistic models, which are based on biochemical and physiological principles. These models offer high predictive power and are capable of describing complex, nonlinear dynamics in bioreactors [104]. However, constructing such models requires detailed information on microbial growth kinetics, metabolic pathway structures, mass transfer, and other process parameters, which often demands costly and labor-intensive experimental studies [112]. Furthermore, the validation and tuning of mechanistic models are frequently associated with computational challenges and instability, particularly when working with newly isolated or genetically engineered probiotic strains.

As an alternative, data-driven approaches based on the analysis of large experimental datasets using machine learning (ML) methods are gaining increasing attention (Figure 4).

The data-driven modeling workflow typically includes the collection of large-scale experimental datasets, data preprocessing and normalization, feature selection, model training using machine learning algorithms, model validation, and performance assessment. These steps enable accurate prediction of fermentation dynamics, strain behavior, and metabolite production, providing a flexible alternative to traditional mechanistic models.

These models rely on mathematical and statistical algorithms capable of uncovering hidden relationships between input and output parameters without requiring prior mechanistic knowledge. Machine learning (ML) facilitates the accelerated optimization of bioprocess parameters, prediction of biomass growth and metabolite production, and real-time monitoring of process variables. However, such models are often sensitive to the domain of the training data and may lose predictive accuracy when extrapolated, such as when changing the microbial strain or the nutrient formulation [113]. Moreover, the lack of biological interpretability demands caution when applying these models beyond their validated scope.

Supervised learning is commonly used for regression and classification tasks (e.g., predicting cell viability under different environmental conditions), whereas unsupervised learning is applied for strain clustering and temporal data pattern analysis [114]. Growing interest in stochastic models stems from their ability to account for biological fluctuations, particularly relevant when dealing with small molecule numbers. For example, the Gillespie algorithm effectively simulates the stochastic nature of intracellular processes such as gene expression and metabolite biosynthesis [115,116]. Additionally, Hidden Markov Models (HMMs) have proven useful for analyzing time-series metabolomics data. Fang et al. [117] demonstrated that the use of HMMs enabled the identification of metabolic states during probiotic fermentation with over 75% accuracy, offering a promising tool for non-invasive process monitoring.

The advancement of deep learning techniques has enabled the handling of high-dimensional and complex datasets, such as omics profiles. Deep neural networks are used to identify relationships between transcriptomic data, metabolic pathways, and environmental conditions. For example, Zampieri et al. [118] integrated genomic, transcriptomic, and metabolomic data to predict the metabolic responses of probiotics to various dietary components. Furthermore, ML algorithms have been applied to genome functional annotation. Sabater et al. [119] used random forests and neural networks to predict glycosidase activity in Bifidobacterium, potentially enabling probiotic selection tailored to individual dietary needs.

Hybrid models, which combine the explanatory power of mechanistic models with the flexibility of ML algorithms, are of particular interest. Bartell et al. [120] proposed a hybrid architecture where a genome-scale metabolic model (GEM) is used to generate simulated data, which is then used to train a machine learning model capable of rapid adaptation to new environmental or stress conditions. This approach helps overcome data scarcity and enhances model generalizability.

Multiscale models, such as the COMETS (Computation Of Microbial Ecosystems in Time and Space) platform, allow for the spatial-temporal simulation of microbial communities, including interactions between probiotics and other members of the gut microbiota. For instance, Kumar et al. [121] used COMETS to identify cross-feeding interactions between Bifidobacterium and butyrate-producing species, highlighting the importance of metabolic cooperation in gut ecosystem dynamics.

Despite the rapid development of modeling approaches, experimental validation remains a critical step in ensuring model reliability. The integration of computational models with laboratory testing enables the verification and refinement of in silico predictions. Lin et al. [122] demonstrated that combining simulation with experimental data can reveal novel adaptive mechanisms, such as acid tolerance in Lactobacillus casei, and contribute to the improvement of industrial processes. Therefore, the reliability and practical applicability of models are directly dependent on the degree of their experimental confirmation and their interpretability in a biological context.

Overall, the modeling of probiotic fermentation processes is increasingly leveraging hybrid, stochastic, and data-driven approaches capable of capturing the complexity of biological systems and their interactions with the environment. Integrating such methods with experimental biotechnology creates new opportunities for process optimization, personalization of probiotic products, and the development of intelligent fermentation control systems [117].

Alternatively, machine learning (ML)-based big data models are being applied more frequently for predicting cultivation outcomes and optimizing production workflows. These models effectively extract hidden patterns from data, offering a fast and flexible tool for forecasting fermentation performance [123]. The combination of mechanistic and data-driven models in hybrid frameworks opens new avenues for deep analysis and optimization of probiotic production. The mutual integration of ML with constraint-based modeling (CBM) facilitates a better understanding of metabolic pathways and enhances productivity [106].

In addition, computational fluid dynamics (CFDs) modeling allows for the consideration of physical bioreactor parameters, including medium flow, temperature and nutrient gradients, and shear stress—factors particularly important for scaling up probiotic production from laboratory to industrial levels [124].

Collectively, the future of probiotic production optimization lies in the integration of mechanistic modeling, big data analytics, and CFD simulation. Advancements in computational and biological technologies will support the implementation of real-time process monitoring and control, ultimately increasing production efficiency, stability, and scalability. This comprehensive approach will help reduce costs, improve product quality, and accelerate the adoption of innovation in industrial probiotic manufacturing.

4. Future Research Perspectives

In light of the growing volume of biological data and increasing computational capabilities, it is timely to discuss the future directions of in silico approaches and their integration with multi-omics and artificial intelligence (AI) technologies. As highlighted throughout this review, in silico modeling in the field of functional food biotechnology is poised for transformative advancements that are likely to surpass current achievements.

Despite significant progress in modeling biological processes and predicting metabolic outcomes, many unresolved challenges remain. These must be addressed to fully harness the potential of computational tools in this area. One of the key directions for further development is the integration of multi-layered omics data, including transcriptomics, proteomics, metabolomics, and genomics, into next-generation models [125,126]. The current limitations of relying on static, single-type datasets should be overcome through integrative modeling, enabling more accurate and dynamic simulations that better reflect the complexity of biological systems [125].

Another promising avenue is the application of artificial intelligence (AI) methods, such as machine learning (ML) and deep learning, for analyzing and predicting the behavior of complex microbial ecosystems [126,127]. These techniques are capable of identifying nonlinear patterns and novel properties in large datasets that often escape conventional statistical analysis. This opens the door to the rational design of targeted microbial consortia and enhanced efficiency of biotechnological products [128]. Moreover, the convergence of AI and mechanistic modeling holds great promise for the development of hybrid models that combine empirical data with foundational biological principles [129].

Ultimately, one of the long-term goals is the creation of personalized nutrition programs based on an individual’s unique microbiome, using both AI-derived insights and integrated multi-omics models [130,131]. This personalized approach stands in contrast to traditional “one-size-fits-all” dietary recommendations and reflects a broader trend in precision medicine, where interventions are tailored to the specific composition and functionality of a person’s gut microbiota [132].

Collectively, these innovative methodologies will enable researchers and industry to leverage big data, advanced computational infrastructure, and sophisticated algorithms to overcome existing constraints—such as generic modeling assumptions and limited biological resolution. The following section will further explore key areas of advancement based on the latest achievements in systems biology, computational modeling, and microbial biotechnology.

4.1. Integration of Multi-Omics Data

Integrating comprehensive biological data—such as genomic, transcriptomic, proteomic, metabolomic, and metagenomic information—is becoming one of the most important directions for improving computer-based modeling in biotechnology. Since fermentation and microbial systems are complex and regulated at many molecular levels, using data from only one type of analysis often gives an incomplete picture of how the system actually functions [125,126].

For example, genomic information shows the potential capabilities of a microbial strain but does not explain how it behaves under specific conditions. Meanwhile, transcriptomic and metabolomic data reflect how cells respond in real time, yet they can be difficult to interpret without a broader biological context [127]. When these data are brought together, models become more accurate and better reflect the regulation of microbial metabolism and physiology.

Modern approaches in systems biology increasingly rely on combining different types of biological data within a single modeling framework. These next-generation models are designed to simulate microbial function in a more complete and realistic way [128,129]. A good example is metabolic modeling that includes not just genome data but also information about gene expression and metabolite levels. This helps researchers calculate metabolic activity more precisely and understand how microbes respond to changing environments [130,131].

In fermentation studies, this multi-level approach makes it possible to move from simply predicting what a strain might be capable of to understanding what it is doing in the moment, and even what it will likely do next. Research shows that traits such as growth, product output, and resistance to stress often depend on interactions between different biological layers. That is why data from just one level is often not enough for accurate predictions [132,133].

New tools are being developed to support this type of data integration, often combining biological networks with advanced computational methods. One particularly exciting development is the creation of so-called “digital twins”—computer models that reflect the behavior of real microbial systems by combining information about their genes, molecular activity, and metabolism. These models have already shown strong results in optimizing strains and predicting their behavior under different conditions [134].

Network-based approaches that capture interactions across multiple biological layers are increasingly valuable for revealing patterns that remain hidden when analyzing each type of data in isolation. Unlike models focused solely on metabolism, recent studies have shown that integrated approaches offer greater accuracy. For example, Rieckmann et al. (2017) demonstrated that combining different types of biological data led to much more reliable predictions of how prebiotics influence the gut microbiota [135]. Similarly, an integrated platform was developed to predict individual responses to dietary interventions by accounting for the metabolic interactions between the host and the microbiome [136].

These multi-level strategies allow for more precise characterization of bioactive compounds and their mechanisms of action, addressing one of the major challenges in developing functional products. As computational capacity continues to expand, the ability to incorporate time-based dynamics across different biological scales promises new insights into how diet influences health through complex biological networks.

While genome-scale metabolic models (GSMMs) provide a robust framework for simulating microbial metabolism, their accuracy can be significantly enhanced through context-specific reconstruction methods. These approaches use transcriptomic, proteomic, or metabolomic data to adapt the models to specific environmental or physiological conditions, thereby improving their biological relevance. For example, tools such as GIMME, iMAT, and INIT enable the generation of metabolic models that reflect condition-dependent gene activity and metabolic fluxes.

Recent reviews and benchmarking studies have highlighted the utility and performance of such methods. Moškon et al. [137] demonstrated how context-specific GSMMs could be applied to decipher metabolic signatures associated with disease states, such as COVID-19, by integrating multi-omics data. Similarly, Pacheco et al. [138] systematically evaluated multiple reconstruction algorithms and provided practical guidance for their use in large-scale biological studies.

Integrating these context-aware modeling approaches into probiotic research enables more accurate predictions of strain behavior under realistic environmental conditions and supports the rational design of functionally optimized microbial strains.

4.2. Personalized Nutrition

The emerging concept of personalized nutrition, centered around the development of functional products and dietary interventions tailored to an individual’s gut microbiome, represents a highly promising direction in functional food biotechnology. A growing body of evidence suggests that responses to the same probiotics, prebiotics, or dietary regimes can vary significantly between individuals due to differences in host genetics, metabolic profiles, and microbial composition [139,140]. As a result, while microbiome-based personalization offers the potential for more effective and targeted interventions, generalized dietary recommendations often lead to inconsistent or suboptimal outcomes.

A key component of this paradigm is in silico modeling, which provides tools for predicting how a person’s unique microbial ecosystem may respond to different dietary components or formulations of functional products [141]. By integrating personal omics data, such as gut metagenomic profiles, into computational models and applying artificial intelligence trained on large datasets describing microbiome–diet interactions, it becomes possible to anticipate which specific nutrients or probiotic strains may be most beneficial for an individual [142].

The feasibility of this approach has already been demonstrated in early studies. Machine learning-based recommendation systems have been shown to successfully shift gut microbiome composition and improve health outcomes in humans [143]. Clinical trials have further revealed that diets personalized using artificial intelligence can significantly improve measures such as postprandial glycemia compared to standardized dietary guidelines [144]. These findings indicate that nutrition guided by biological data and computational modeling is not only feasible but may surpass traditional dietary strategies (Figure 5).

Multi-omics data derived from an individual’s gut microbiome, such as genomic, transcriptomic, and metabolomic layers, are processed using an artificial intelligence system to generate personalized dietary recommendations.

Such progress could pave the way for the development of highly individualized functional products. For instance, companies may be able to design a probiotic yogurt, a custom supplement blend, or a fiber-enriched beverage tailored to the microbial needs of a specific customer, based on their gut microbiome profile. These examples illustrate how the convergence of biology and big data is driving scalable personalization, building upon the previously discussed integration of multi-omics and AI-based modeling [145].

Nonetheless, significant challenges remain. These include the need for large, diverse, and high-quality datasets capturing individual microbiome variability, the robust validation of predictive models to ensure clinical relevance, and important considerations related to data privacy and ethics [146]. Yet the future appears promising: as modeling techniques advance and frameworks for data sharing evolve, microbiome-based personalized nutrition is becoming an increasingly attainable strategy for improving health through individualized functional products. In many ways, microbiome analysis may become the foundation of next-generation nutrition, enabling personalized solutions as unique as our microbial fingerprints.

In this context, whole-body metabolic models (WBMs) are emerging as powerful tools for advancing personalized nutrition. By simulating the metabolic interactions between the host and its microbiome across multiple organs and tissues, these models can predict individual responses to specific dietary components or probiotic formulations with remarkable precision.

For example, Martinelli et al. [147] applied a WBM framework to reveal how microbiome–genome interactions contribute to altered metabolite levels in Alzheimer’s disease, demonstrating the potential of such models in systemic disease analysis and diet–health relationships. Similarly, Li et al. [148] developed a personalized gut microbiota modeling approach that integrates metagenomic data with genome-scale metabolic models to assess individual metabolic responses to dietary inputs. Alessi et al. [149] further expanded this concept by using in silico dietary simulations to identify sex-specific metabolic risk profiles, emphasizing the role of WBMs in guiding personalized dietary strategies for preventing metabolic syndrome.

These studies underscore the growing relevance of system-level models in personalizing functional foods and dietary interventions, supporting the shift toward nutrition strategies tailored to individual microbial and metabolic signatures.

5. Conclusions

The growing complexity of microbial systems and the increasing demand for precision in functional food biotechnology have positioned in silico modeling as an indispensable component of probiotic research and development. By integrating genomic, metabolic, and environmental data, computational approaches, such as flux balance analysis (FBA), metabolic flux analysis (MFA), dynamic FBA (dFBA), and genome-scale metabolic modeling (GEM), provide powerful tools for decoding probiotic functionality, predicting community-level interactions, and optimizing strain performance under diverse conditions.

This review demonstrates that the convergence of multi-omics integration, artificial intelligence, and hybrid modeling strategies has begun to overcome long-standing limitations of traditional fermentation and formulation processes. Particularly promising is the shift toward personalized approaches that use an individual’s microbiome profile to inform dietary interventions, thereby moving beyond generalized recommendations toward targeted, effective, and adaptive nutritional strategies.

However, significant challenges remain. The biological complexity of host–microbe interactions, variability in microbiome composition, and limitations in model interpretability and standardization continue to constrain the translation of in silico predictions into clinically actionable outcomes. Moreover, the reliance on high-quality, diverse datasets for training and validating predictive models raises concerns related to scalability, data availability, and ethical considerations.

Looking forward, future success in probiotic biotechnology will depend not only on computational sophistication but also on the robust experimental validation of predictive models, the development of dynamic feedback systems, and the incorporation of real-time data streams into model refinement. The implementation of digital twins, virtual representations of microbial systems continuously updated with empirical data, offers one such opportunity to bridge the gap between simulation and application.

In summary, the integration of systems biology, computational modeling, and experimental data holds the potential to revolutionize the design and application of functional probiotics. By embracing a multidisciplinary framework, researchers and industry can move toward a new generation of safe, effective, and personalized probiotic solutions that align with the broader goals of precision health and sustainable food innovation.