Methods and systems for designing personalized treatments and diets based on microbiome function

Abstract

This is a method and system for designing personalized therapeutic interventions. The composition of the gut microbiota varies from person to person, and probiotics and dietary treatments may have different effectiveness for different individuals. The present disclosure recommends personalized therapeutic interventions for improving the gut health of an individual by designing personalized therapeutics and dietary treatments based on the function of the microbiome. The personalized therapeutic interventions are recommended based on several steps including generating a set of knowledge bases, identifying changes in the gut health of the individual by monitoring gut samples, and recommending personalized therapeutic interventions. The personalized therapeutic interventions include at least one of prebiotics, probiotics, and an optimized diet, where the optimized diet is estimated based on the optimization of the gut food score, and the gut food score is calculated based on the changes in the gut health of the individual.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY This application is This application claims priority from International Application No. PCT / IN2022 / 050094, filed on February 3, 2022, which is incorporated herein by reference in its entirety. This claims priority from Indian Application No. 202121004712 filed on February 3, 2021. [Background technology]

[0002] Technical Field The present disclosure relates generally to the field of microbiome-based therapeutics, and more particularly, to methods and systems for designing personalized therapeutics and diets based on microbiome function.

[0003] background Vertebrates, including humans, are home to a variety of microorganisms known as the "microbiota," and the collection of genetic material of the microbiota is called the "microbiome." However, the two terms "microbiota" and "microbiome" are widely used interchangeably. The gastrointestinal (GI) tract or gut is the most colonized body site harboring these microbial populations. Microbes are known to perform several metabolic activities, which can lead to interactions with an individual's microbiome and its genes, termed human microbiome interactions. The microbiota is known to affect the health of an individual / host in various ways, including regulating the immune system, energy harvesting, and influencing various signaling mechanisms. Therefore, altering the microbial community in an individual's gut can mitigate the risk and manifestations of certain diseases and disorders.

[0004] The composition of the gut microbiota varies from individual to individual depending on multiple factors, including geography, lifestyle, and dietary patterns. Moreover, the microbial composition of the microbiome can differ between individuals living in similar environments and even between twins with identical genetic makeup. Thus, in different populations or different individuals, different taxa may be involved in dysbiosis, and these microbial community shifts and dysbiosis are associated with a myriad of diseases / disorders. Thus, dysbiotic states may be characterized by the loss of microbial groups that contribute to beneficial functions, such as the biosynthesis of useful metabolites, or they may be accompanied by an increase in microbial groups that may produce metabolites that are detrimental to various aspects of human health. Dysbiosis may also involve an increase in the abundance of microorganisms that may produce metabolites that are detrimental to various aspects of human health. To further complicate matters, there may be variations associated with a particular dysbiotic state in the form of a decrease or enhancement of one or more functions. In other words, the combination of probiotic and prebiotic strains that are effective for each individual may differ, as different individuals have different microbiome compositions and genetic backgrounds. This highlights the importance of personalized microbiome therapeutics.

[0005] Recently, the cost of sequencing and analyzing the microbiome has become negligible. By identifying microbial-specific DNA or RNA marker sequences, it is now possible to obtain the gut microbiome composition of an individual. Samples for gut microbiota profiling can be obtained by non-invasive methods such as fecal sampling. The taxonomic information obtained is often compared with the composition obtained / known for a population of individuals suffering from or expected to be at risk for a particular disease / disorder. Indicators to assess gut health often include analysis of the abundance of microorganisms and the functions they perform.

[0006] It is known in the art that the risk or onset of certain diseases / disorders can be mitigated by altering the microbial community in an individual's gut. Such modifications include (a) targeting and reducing the abundance of pathogenic strains using antibiotics, (b) administering live therapeutics such as probiotics or dietary components such as prebiotics to increase the abundance of beneficial organisms for the gut, and (c) fecal microbial transplants.

[0007] It is well established that probiotics can be used to change the relative abundance of desirable microorganisms in the gut microbiota, as well as in some cases to alter metabolic structure and modulate the immune system. Despite this, some studies have shown limited efficacy of probiotics or no efficacy in certain individuals. One reason for this may be that many probiotics administer strains with the assumption that the same organisms may be low in abundance in all individuals or may have the same reduced functions in most individuals. Taken together, dysbiosis in the gut microbiota in an individual may be due to a decrease in beneficial functions, an increase in harmful functions, or a combination of both scenarios. Thus, a given probiotic or dietary treatment may not have the same effect in different individuals. Utilizing an individual's microbiome profile prior to administering a probiotic / nutraceutical and analyzing which functions are impaired or enhanced in a dysbiotic state for a particular individual may help improve the efficacy of probiotics. Thus, customized and individualized probiotics based on the microbiome functions or taxonomic changes associated with dysbiosis are a need in the art. Summary of the Invention

[0008] overview The embodiments of the present disclosure present technical improvements as a solution to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for designing personalized treatments and diets based on the function of the microbiome is provided. The system includes a memory storing instructions, one or more communication interfaces, and one or more hardware processors coupled to the memory via the one or more communication interfaces, the one or more hardware processors are configured to receive a set of input data by the instructions, the set of input data includes a plurality of metabolic pathways, a plurality of taxa, information on nutrients, probiotics, prebiotics, supplements for supplementation or degradation of a plurality of metabolites, a plurality of food ingredients, a plurality of food items, a microbial abundance matrix of a healthy cohort, a plurality of dietary optimization prerequisite parameters, including data related to a plurality of metabolites. The system is further configured to generate a set of knowledge bases using the set of input data, the set of knowledge bases includes a genus-function map (FGmap), a genome-metabolite pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food ingredient network, a food ingredient network, and a ProbMap. The system is further configured to receive a plurality of gut samples from the individual at two timestamps, the two timestamps including timestamp 1 and timestamp 2 at which the two samples were collected. The system is further configured to generate, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including taxon set 1 and taxon set 2, where taxon set 1 and taxon set 2 are generated using timestamp 1 and timestamp 2.The system is further configured to generate pathway abundances for each of the multiple gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp. The system is further configured to identify perturbed pathways, where the perturbed pathways indicate a deterioration in the gut health of the individual, the perturbed pathways being identified using statistical techniques based on a comparison of pathway abundances for the multiple gut samples obtained at timestamp 1 and timestamp 2, and (a) Case A: a decrease in pathway abundance for a set of beneficial pathways, where the decrease indicates a decrease in the set of beneficial microorganisms, or (b) Case B. an increase in pathway abundance for a set of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms, or (c) Case C: a decrease in pathway abundance for a set of beneficial pathways and an increase in pathway abundance for a set of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms and a decrease in the population of beneficial microorganisms. The system is further configured to determine an initial set of personalized therapeutic and dietary recommendations, the initial set of personalized therapeutic and dietary recommendations including at least one of the following: personalized therapies and personalized dietary recommendations using a ProbMap, wherein the initial set of personalized therapies and dietary recommendations are rows corresponding to the identified perturbed pathways, and the personalized therapies are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having pathways for degradation or / and biosynthesis of likely metabolites, and the likely metabolites are metabolites corresponding to the perturbed pathways in the ProbMap.The system is further configured to identify a taxon set in the taxon abundance matrix obtained at timestamp 2, the taxon set including TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes a predefined first value for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes a predefined second value for one or more of the harmful pathways in one of the FGmap and the GMP. The system is further configured to identify a first set of neighbors for each taxon in TAXA_SET_P at timestamp 2 from the TAXA_NET graph, where the first set of neighbors are direct neighbors of TAXA_SET_P including TAXA_SET_FN, TAXA_SET_FN_COM, TAXA_SET_INTERSECT, TAXA_SET_NEW.The system is further configured to recommend, via the one or more hardware processors, a personalized therapeutic intervention for improving gut health for the individual, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail, (b) a probiotic cocktail, and (c) an optimized diet based on the identified first neighborhood using the set of knowledge base and the initial set of personalized therapeutic and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) for case A (reduced pathway abundance of a population of beneficial microorganisms), recommending a prebiotic cocktail, a probiotic cocktail, and an optimized diet, where the prebiotic cocktail, the probiotic cocktail, and the optimized diet are selected from the group consisting of: (b) for case B (increased pathway abundance of a set of harmful microorganisms), an optimized diet is recommended, where the optimized diet promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; and (c) for case C (decreased pathway abundance of a set of beneficial microorganisms and increased pathway abundance of a set of harmful microorganisms), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, a probiotic cocktail and an optimized diet replenish multiple beneficial microorganisms, promote the growth of beneficial microorganisms and inhibit the growth of harmful microorganisms.

[0009] In another aspect, a method is provided for designing personalized therapeutics and dietary treatments based on microbiome function. The method includes receiving a set of input data, the set of input data includes data related to a plurality of metabolites, a plurality of metabolic pathways, a plurality of taxa, nutrients, probiotics, prebiotics, information on supplements for supplementing or degrading a plurality of metabolites, a plurality of food components, a plurality of food items, a microbial abundance matrix of a healthy cohort, and a plurality of diet optimization premise parameters. The method further includes generating a set of knowledge bases using the set of input data, the set of knowledge bases includes a genus function map (FGmap), a genome metabolite pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap. The method further includes receiving a plurality of gut samples of an individual at two time stamps, the two time stamps include time stamp 1 and time stamp 2 at which the two samples were collected. The method further includes generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including taxon set 1 and taxon set 2, where taxon set 1 and taxon set 2 are generated using timestamp 1 and timestamp 2. The method further includes generating pathway abundances for each of the plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap, where the pathway abundances are generated using sample processing techniques, where the pathway abundances for each timestamp indicate the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp.The method further includes identifying perturbed pathways, where the perturbed pathways are indicative of a deterioration in the individual's gut health, where the perturbed pathways are identified based on a comparison of pathway abundances of multiple gut samples obtained at timestamp 1 and timestamp 2 using statistical methods, and are: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease is indicative of a decrease in the set of beneficial microorganisms; or (b) Case B. An increase in pathway abundance of a set of harmful pathways, where the increase is indicative of an increase in the population of harmful microorganisms; or (c) Case C: A decrease in pathway abundance of the set of beneficial pathways and an increase in pathway abundance of the set of harmful pathways, where the increase is indicative of an increase in the population of harmful microorganisms and a decrease in the population of beneficial microorganisms. The method further includes determining an initial set of personalized therapeutic and dietary recommendations, the initial set of personalized therapeutic and dietary recommendations including at least one of the following: determining personalized therapeutics and personalized dietary recommendations using a ProbMap, the initial set of personalized therapeutics and dietary recommendations being rows corresponding to the identified perturbed pathways, the personalized therapeutics being determined as (a) a plurality of bacterial strains having perturbed pathways, or (b) a plurality of bacterial strains having pathways for degradation or / and biosynthesis of likely metabolites, the likely metabolites being metabolites corresponding to the perturbed pathways of the ProbMap. The method further includes identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, the taxon set including TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes a predefined first value for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes a predefined second value for one or more of the harmful pathways in one of the FGmap and the GMP. The method further includes identifying, from the TAXA_NET graph, a set of first neighbors of each element of TAXA_SET_P at timestamp 2, where the set of first neighbors are direct neighbors of TAXA_SET_P including TAXA_SET_FN, TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW.The method further includes recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving gut health for the individual, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; and (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases, the initial set of personalized therapeutics and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) for case A (reduced pathway abundance of a population of beneficial microorganisms), recommending a prebiotic cocktail, a probiotic cocktail, and an optimized diet, wherein the prebiotic cocktail, the probiotic cocktail, and the optimized diet are recommended. (b) for case B (increased pathway abundance of a set of harmful microorganisms), an optimized diet is recommended, where the optimized diet promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; and (c) for case C (decreased pathway abundance of a set of beneficial microorganisms and increased pathway abundance of a set of harmful microorganisms), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, a probiotic cocktail and an optimized diet replenish multiple beneficial microorganisms, promote the growth of beneficial microorganisms and inhibit the growth of harmful microorganisms.

[0010] In yet another aspect, a non-transitory computer-readable medium for designing personalized therapeutics and dietary treatments based on microbiome function is provided. The program includes receiving a set of input data, the set of input data includes data related to a plurality of metabolites, a plurality of metabolic pathways, a plurality of taxa, nutrients, probiotics, prebiotics, information on supplements for supplementing or degrading a plurality of metabolites, a plurality of food components, a plurality of food items, a microbial abundance matrix of a healthy cohort, and a plurality of diet optimization premise parameters. The program further includes generating a set of knowledge bases using the set of input data, the set of knowledge bases includes a genus function map (FGmap), a genome metabolite pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap. The method further includes receiving a plurality of gut samples of an individual at two time stamps, the two time stamps including time stamp 1 and time stamp 2 at which the two samples were collected. The program further includes generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including taxon set 1 and taxon set 2, where taxon set 1 and taxon set 2 are generated using timestamp 1 and timestamp 2. The program further includes generating pathway abundances for each of the plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap, where the pathway abundances are generated using sample processing techniques, where the pathway abundances for each timestamp indicate the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp.The program further includes identifying perturbed pathways, where the perturbed pathways are indicative of a deterioration in the individual's gut health, where the perturbed pathways are identified using statistical methods based on a comparison of pathway abundances of multiple gut samples obtained at timestamp 1 and timestamp 2, and are: (a) Case A: a decrease in pathway abundance of a set of beneficial pathways, where the decrease is indicative of a decrease in the set of beneficial microorganisms; or (b) Case B: an increase in pathway abundance of a set of harmful pathways, where the increase is indicative of an increase in the population of harmful microorganisms; or (c) Case C: a decrease in pathway abundance of a set of beneficial pathways and an increase in pathway abundance of a set of harmful pathways, where the increase is indicative of an increase in the population of harmful microorganisms and a decrease in the population of beneficial microorganisms. The program further includes determining an initial set of personalized therapeutic and dietary recommendations, the initial set of personalized therapeutic and dietary recommendations including at least one of the following: determining personalized therapeutics and personalized dietary recommendations using a ProbMap, the initial set of personalized therapeutics and dietary recommendations being rows corresponding to the identified perturbed pathways, the personalized therapeutics being determined as (a) a plurality of bacterial strains having perturbed pathways, or (b) a plurality of bacterial strains having pathways for degradation or / and biosynthesis of likely metabolites, the likely metabolites being metabolites corresponding to the perturbed pathways of the ProbMap. The program further includes identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, the taxon set including TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes a predefined first value for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes a predefined second value for one or more of the harmful pathways in one of the FGmap and the GMP. The program further includes identifying, from the TAXA_NET graph, a set of first neighbors for each taxon in TAXA_SET_P at timestamp 2, the set of first neighbors being direct neighbors of TAXA_SET_P including TAXA_SET_FN, TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW.The program further includes recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving gut health for the individual, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail, (b) a probiotic cocktail, and (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and the initial set of personalized therapeutic and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) for Case A (reduced pathway abundance of a population of beneficial microorganisms), recommending a prebiotic cocktail, a probiotic cocktail, and an optimized diet, where the prebiotic cocktail, the probiotic cocktail, and the optimized diet are recommended; (b) for case B (increased pathway abundance of a set of harmful microorganisms), an optimized diet is recommended, where the optimized diet promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; and (c) for case C (decreased pathway abundance of a set of beneficial microorganisms and increased pathway abundance of a set of harmful microorganisms), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, a probiotic cocktail and an optimized diet replenish multiple beneficial microorganisms, promote the growth of beneficial microorganisms and inhibit the growth of harmful microorganisms.

[0011] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. [Brief description of the drawings]

[0012] Brief description of the drawings The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:

[0013] [Figure 1]FIG. 1 illustrates an exemplary system for designing personalized therapeutics and diets based on microbiome function, according to some embodiments of the present disclosure.

[0014] [Figure 2A] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2B] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2C] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2D] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2E] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2F] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2G]2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure. [Figure 2H] 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H are flow diagrams illustrating a method (200) for designing personalized therapeutics and dietary therapies based on microbiome function, according to some embodiments of the present disclosure.

[0015] [Figure 3A] 3A and 3B are flow diagrams illustrating a method (300) for generating a genomic metabolic pathway (GMP) map according to some embodiments of the present disclosure. [Figure 3B] 3A and 3B are flow diagrams illustrating a method (300) for generating a genomic metabolic pathway (GMP) map according to some embodiments of the present disclosure.

[0016] [Figure 4] FIG. 4 is a flow diagram illustrating a method (400) for generating a pathway abundance matrix using sample processing techniques according to some embodiments of the present disclosure.

[0017] [Diagram 5] FIG. 5 is a flow diagram illustrating a method (500) for recommending an optimized diet based on an optimization technique according to some embodiments of the present disclosure.

[0018] [Figure 6] FIG. 6 is a diagram illustrating a set of neighbors for designing personalized therapeutics and dietary treatments based on microbiome function, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Detailed Description of the Embodiments Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the leftmost digit(s) of a reference number identifies the figure in which the reference number first appears. Where convenient, the same reference numbers will be used throughout the drawings to refer to the same or similar parts. Examples and features of the disclosed principles have been described herein, but modifications, adaptations, and other implementations may be made without departing from the scope of the disclosed embodiments.

[0020] Glossary The term "microbiota" in the context of this disclosure refers to a collection of microorganisms, such as bacteria, archaea, protists, fungi, and viruses, that inhabit a specific ecological niche (e.g., human body sites such as skin, gut, and oral cavity) or environmental / geographical site. The term "gut microbiome" in the context of this disclosure refers to the multiple genetic materials of the microbiota (or microbial community) present in the gut / gut / intestine of a human or vertebrate host. An imbalance or disruption of the microbiome is called "dysbiosis." The two terms "microbiota" and "microbiome" are widely used interchangeably.

[0021] The term "metabolite" in the context of this disclosure refers to a chemical entity, product, or compound formed by metabolic processes in a microorganism. Metabolites known to have beneficial effects in humans in terms of nutrition, immune regulation, maintenance of organ function, regulation of gene function, etc. are referred to as beneficial metabolites. Metabolites known to have detrimental effects on the host, such as increasing inflammatory responses, damaging gut integrity, and dysregulating genes, are referred to as harmful metabolites.

[0022] The term "function" in the context of this disclosure refers to the presence of genes on the microbial genome that encode proteins or enzymes that form a pathway for converting a candidate metabolite into a particular candidate product.

[0023] The term "homolog" in the context of this disclosure refers to two proteins that are derived from a common evolutionary origin or ancestor, and the use of the term may also be extended to proteins with greater than 60% protein sequence similarity that have arisen by convergent evolution to perform similar functions.

[0024] The term "pathway" in the context of this disclosure refers to a series of reactions in which a starting substrate is converted into a product in one reaction, using a gene encoding a protein / enzyme for this conversion. The product thus biosynthesized is incorporated into the next reaction in the series by a gene encoding the protein / enzyme. A "cognate homolog" of a pathway refers to a candidate protein / enzyme that actually functions in the pathway among all the genome-encoded homologs of this protein / enzyme.

[0025] The term "operon" or "gene cluster" in the context of this disclosure refers to the presence of genes involved in a pathway in contiguous locations on a microbial genome.

[0026] The term "protein domain" or "domain" in the context of this disclosure refers to a conserved region of a protein that can exist independently and perform a similar function. Protein domains that make up a protein can be identified using various methods, such as sequence homology, hidden Markov models, etc., to understand or annotate the function of a protein.

[0027] The term "hash" in the context of this disclosure refers to key-value pairs and their associations, which may also be referred to as an associative array or dictionary.

[0028] The term "taxonomic abundance" in the context of the present disclosure refers to the occurrence / abundance of a taxonomic group in a microbiome sample that may be taken from a body site. Taxonomic abundance may be obtained as a taxonomic level of genus, species, or strain in the form of a matrix containing abundances with taxon names as rows and samples as columns. Certain genes, such as bacterial 16S rRNA, are used as marker genes to understand the lineage of taxa present in a microbiome sample. In the present disclosure, taxa include microorganisms classified according to a taxonomic hierarchy, which includes one or more of a plurality of phyla, classes, orders, families, genera, species, or strains. Reference to any one of these taxa may be considered applicable to all taxa.

[0029] The term "time point" in the context of this disclosure refers to samples obtained from the same individual with data at different time points, while the term "population(s)" refers to data corresponding to a set of identical / similar / comparative / matched individuals who may belong to a particular analytical characteristic(s) such as (but not limited to) health status, age, geography, treatment administration, etc.

[0030] The term "probiotic" in the context of this disclosure refers to live microorganisms that may provide a health benefit when administered to a human or any vertebrate host. The term "prebiotic" or "nutraceutical" refers to a food ingredient that may induce bacterial growth or its activity to maintain the health of the host. The term "synbiotic" refers to a combination or mixture of probiotics (live microorganisms that promote health) and prebiotics (nutraceuticals that enhance the growth of beneficial microorganisms) administered to exert a synergistic beneficial effect. The terms "metabiotics" or / and "postbiotics" or / and "biogenics" refer to components secreted by live microorganisms alone or with their metabolites / products / byproducts that help enhance / reduce certain host physiological functions that regulate / restore the health of the host. The term "paraprobiotics" or "ghost probiotics" are modified, immobilized, inactivated, non-viable forms of probiotics that, when ingested, restore the beneficial functions of the gut microbiota.

[0031] The term "fecal microbial transplant" or "FMT" in the context of the present disclosure refers to the administration of fecal components from a donor into the intestinal tract of a recipient individual whose intestinal microbiota exhibits dysbiosis. In one embodiment, FMT can also refer to "a-FMT (autologous FMT)," which involves banking an individual's stool during a "healthy" state and using the stool components when needed under dysbiotic conditions. FMT helps to change the recipient's intestinal microbiota and alleviate the intestinal dysbiosis caused by disease or treatment in the recipient.

[0032] The term "bioengineering" or "genetic engineering" refers to the process of altering / modifying the genetic makeup of one or more microorganisms, resulting in improved use or applicability of said microorganisms to their host / industry / environment.

[0033] The terms "perturbed function" or "perturbed pathway" or "probable pathway" refer to an increase in a detrimental pathway, or the biosynthesis of a detrimental metabolite, or the degradation of a beneficial metabolite. They can also refer to a decrease in a beneficial pathway or beneficial metabolite, or a decrease in the degradation of a detrimental metabolite. Metabolites that are significantly increased or decreased between two or more gut samples are referred to as probable or perturbed metabolites.

[0034] The proposed invention allows for the identification of microbial functions that need to be enhanced or attenuated to restore the balance of the microbiome. Moreover, the invention allows health care providers to personalize probiotics for individuals based on these identified functions. Furthermore, it can predict appropriate nutritional supplementation and dietary patterns that may prove efficient for a particular individual. Such medicines may prove effective in designing personalized preventive therapeutics for individuals or groups of individuals with similar microbiome profiles. In one embodiment, such personalized probiotics can be administered to individuals alone or in combination with other ongoing treatment regimens.

[0035] As mentioned in the background section, dysbiosis in an individual may result from a decrease in beneficial functions, an increase in harmful functions, or a combination of both scenarios. Therefore, a probiotic or dietary treatment may not have the same effect in different individuals. Utilizing an individual's microbiome profile before administering a probiotic / nutraceutical and analyzing the functions in which biodiversity is impaired or enhanced for a particular individual may help improve the efficacy of probiotics. Therefore, there is a need for personalized and individualized probiotics based on microbiome functions or taxonomic changes associated with dysbiosis.

[0036] The disclosed invention is different from the known methods in the art of designing a composition(s) for a therapeutic strategy, including dietary therapy, probiotics, prebiotics, metabiotics, synbiotics, as described herein. In one embodiment, the composition may be composed of probiotics and / or prebiotics and / or dietary suggestive agents and / or nutritional supplements, etc. Any other composition capable of altering the function of the microbiome as described herein is within the scope of the present invention. 1. Identify the combination of impaired pathways, including beneficial metabolic pathways and their constituent metabolites that are decreased and harmful metabolic pathways and their constituent metabolites that are increased, among the metabolic abnormalities present in the gut microbiota of an individual. 2. Using the microbiome profile and derived metabolic pathway information, we identify perturbed pathways. The perturbed pathway information, and therefore the perturbed function of the microbiome, can be applied to design therapeutic strategies based on the function of the microbiome. The metabolic pathways identified as perturbed serve as indicators of the perturbed function. 3. Utilizing the knowledge base to derive the composition of a personalized therapeutic regimen, including one or more of a microbial cocktail, dietary components, dietary supplements, and prebiotics, required to correct the effects of said disturbed metabolic pathway, thereby deriving metabolic products associated with dysbiosis.

[0037] Advantages of the Invention 1. The present disclosure provides methods to identify perturbed microbial functions in an individual's microbiome dysbiosis state and use the functions to determine personalized treatment regimens to improve the health of the individual from a microbiome dysbiosis state to a balanced state. 2. The composition is designed to replenish the intestine with beneficial metabolites that are impaired or with taxa responsible for the production of those metabolites.Similarly, the composition can be designed to suppress the presence of harmful metabolites or the presence of taxa responsible for the biosynthesis of harmful metabolites.

[0038] The personalized therapeutic intervention is designed to replenish the impaired beneficial metabolites or the taxa responsible for the production of the metabolites in the gut. Similarly, compositions can be designed to suppress the presence of harmful metabolites or the taxa responsible for the biosynthesis of harmful metabolites.

[0039] Given the importance of the microbiome and its impact, the use of probiotics to change the relative abundance of desirable microorganisms in the gut microbiota, as well as potentially alter metabolic composition and immune system modulation, is well established. However, since the composition and genetic background of the microbiome differs from person to person, ideally the combination of effective probiotic and prebiotic strains would be different / personalized / customized depending on the person's gut microbiota to provide maximum benefit to the individual.

[0040] Referring now to the drawings, and more particularly to FIGS. 1-6, in which like reference characters indicate corresponding features consistently throughout the figures, preferred embodiments are shown and will be described in the context of the following exemplary systems and / or methods.

[0041] FIG. 1 is a functional block diagram of a system 100 for designing personalized treatments and diets based on microbiome function, according to some embodiments of the present disclosure.

[0042] In one embodiment, system 100 includes processor(s) 104, communication interface device(s), alternatively referred to as input / output (I / O) interface(s) 106, and one or more data storage devices or memories 102 operatively coupled to processor(s) 104. System 100 with its multiple hardware processors is configured to perform functions of one or more functional blocks of system 100.

[0043] Referring to the components of the system 100, in one embodiment, the processor(s) 104 may be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operational instructions. Among other capabilities, the one or more hardware processors 104 are configured to fetch and execute computer-readable instructions stored in the memory 102. In one embodiment, the system 100 may be implemented in a variety of computing systems including laptop computers, notebooks, handheld devices such as mobile phones, workstations, mainframe computers, servers, network clouds, and the like.

[0044] The I / O interface(s) 106 may include a variety of software and hardware interfaces, such as, for example, a web interface, a graphical user interface, a touch user interface (TUI), and may facilitate communications within a wide variety of network N / W and protocol types, including, for example, wired networks such as LAN, cable, and wireless networks such as WLAN, cellular, or satellite. In one embodiment, the I / O interface(s) 106 may include one or more ports for connecting multiple devices (nodes) of the system 100 to each other or to another server.

[0045] The memory 102 may include any computer-readable medium known in the art, including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memory, hard disks, optical disks, and magnetic tapes.

[0046] Additionally, the memory 102 may include a database 108 configured to include information regarding the metabolites, including the beneficial and harmful microorganisms, along with the pathway functions of the metabolites. The memory 102 may include information related to the input(s) / output(s) of each step performed by the processor(s) 104 of the system 100 and the methods of the present disclosure. In one embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I / O interface 106.

[0047] The functionality of the components of system 100 is explained in conjunction with the flow diagrams of FIGS. 2A-2H, 3A-3B, 4 and 5.

[0048] System 100 supports a variety of connectivity options, such as BLUETOOTH, USB, ZigBee, and other cellular services. The network environment allows for the connection of the various components of system 100 using any communication link, including the Internet, a WAN, a MAN, and the like. In an exemplary embodiment, system 100 is implemented to operate as a stand-alone device. In another embodiment, system 100 may be implemented to operate as a loosely coupled device to a smart computing environment. The components and functionality of system 100 are described in further detail.

[0049] The various modules of system 100 are configured to design personalized treatments and diets based on microbiome function and are implemented as at least one of logically self-contained portions of a software program, self-contained hardware components, and / or self-contained hardware components having logically self-contained portions of a software program embedded in each of the hardware components that, when executed, performs the methods described herein.

[0050] The functionality of the functional modules of the system 100 stored in the memory 102 are further described in conjunction with the flow diagrams of Figures 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H. Figures 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H, with reference to Figure 1, are flow diagrams illustrating a method 200 for designing personalized therapeutics and diets based on microbiome function using the system 100 of Figure 1, according to one embodiment of the present disclosure.

[0051] The steps of the disclosed method will now be described with reference to the flow diagrams depicted in Figures 2A, 2B, 2C, 2D, 2E, 2F, 2G, and 2H for the components of the system (100) for designing personalized treatments and diets based on microbiome function. Although process steps, method steps, techniques, and the like may be described in sequential order, such processes, methods, techniques may be configured to function in alternative orders. In other words, any order or sequence of steps that may be described does not necessarily indicate a requirement for the steps to be performed in that order. The steps of the processes described herein may be performed in any order that is practical. Additionally, some steps may be performed simultaneously.

[0052] In step 202 of the method ( 200 ), a set of input data is received at the input / output interface 106 .

[0053] The input data set consists of data relating to multiple metabolites, multiple metabolic pathways, multiple taxa, nutrients, probiotics, prebiotics, information on supplements for the replacement or degradation of multiple metabolites, multiple food components, multiple food items, a microbial abundance matrix of a healthy cohort, and multiple dietary optimization prerequisite parameters.

[0054] The set of input data comprises data relating to a plurality of metabolites, the set of beneficial metabolites comprising a set of harmful metabolites, the set of beneficial metabolites being beneficial to the individual's gut health, and the set of harmful metabolites being harmful to the individual's gut health.

[0055] The input data set further comprises data relating to a plurality of metabolic pathways, each of which may be either (a) beneficial pathways or (b) detrimental pathways.

[0056] The set of input data further comprises a beneficial pathway comprising one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites.

[0057] The set of input data further comprises a deleterious pathway comprising one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of deleterious metabolites.

[0058] The set of input data further includes a plurality of beneficial microorganisms possessing the beneficial pathway and a plurality of harmful microorganisms possessing the harmful pathway.

[0059] The set of input data further comprises: the plurality of taxa comprising a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, and the plurality of microorganisms comprises a set of a plurality of genes having different genomic locations, a nucleotide sequence of the bacterial strain, and a protein sequence.

[0060] The set of input data further includes a plurality of dietary optimization precondition parameters, where the plurality of dietary optimization precondition parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix that aggregates the content of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters related to carbohydrate, protein, fat and calories, and user selected foods.

[0061] A list ML of metabolites important for or detrimental to gut health is cataloged using literature mining techniques. In this disclosure, gut health refers to those functions that help maintain the balance of the gut microbiota and thus the health of an individual. In one embodiment, a query string is used as an input to search in literature engines such as Pubmed or Pubtator, or patent search engines, but is not limited to these. In an embodiment, the query string can be "gut + health + metabolite" or "metabolite name + gut". Metabolites shown in the literature to be beneficial for gut health include short chain fatty acids such as butyrate, propionate, and acetate. Pathways that lead to the release of ammonia, such as urea metabolism by the enzyme urease, amino acid metabolism of undigested proteins that reach the gut, and metabolism of biogenic amines such as putrescine, spermine, and cadaverine, have been found to be detrimental to gut health. Similarly, increases in metabolites such as trimethylamine oxide and phenylacetate have been found to be involved in cardiovascular and liver diseases, respectively. ListML also includes information on whether a metabolite has been reported in the literature to be harmful or beneficial to gut health.

[0062] The pathways ( ) of production / degradation / metabolism of each metabolite (MP) constituting the ML are obtained by modern literature mining techniques. Furthermore, genes constituting a metabolic pathway (MP) may have homologs in various other pathways in the same bacterial genome. Therefore, it is important to distinguish the homologs involved in MP (hereafter also referred to as cognate homologs) from other homologs. In one embodiment, genes that function as pathways and code for proteins or enzymes involved in the degradation or production or metabolism of compounds are often present in juxtaposed positions on the bacterial genome, and these genes are collectively referred to as operons or gene clusters. The occurrence of genes coding for metabolic pathways in gene clusters can be used to identify the cognate homologs belonging to the pathway from other homologs on the genome. A query string Q is used as an input for metabolites to search against literature search engines that are repositories of scientific literature (including but not limited to pathway repositories such as Pubmed / Pubtator and KEGG). [NameString'] + [Gene cluster OR operon] + [Microbe] where NameString = (M i ) + [Degradation OR Biosynthesis OR Metabolism] or any patent document search engine.

[0063] Use of any combination of the query strings, or any substring of the query string, is within the scope of the present invention. The result sets obtained from literature searches of these databases include a list of organisms in which pathways have been experimentally characterized (ORG out ) along with a list of abstracts corresponding to each journal publication (A out) as output. The use of other databases to obtain pathway information and the results of literature mining in other formats are within the scope of the present invention. In one embodiment, the format of the results of literature mining can be one or more of full publications, reviews, online information, and book articles. Furthermore, the manual search for the pathways of degradation / biosynthesis of metabolite Mi (which are a series of steps or chemical reactions of degradation / biosynthesis of candidate metabolites encoded by genes on bacterial genomes) and the genes / enzymes involved in the process is performed by searching A for each input query string Q in the previous step. out The result of this process is a matrix called Metabolite Map (Mmap) with rows for each metabolite and columns for its biosynthetic / degradative pathway and the organisms in which it has been characterized. A sample scenario of a prototype table for the Mmap is shown in Table 1 below: [Table 1]

[0064] In one embodiment, the metabolic pathway found in beneficial microorganisms (found in resident bacteria) that synthesizes beneficial metabolites is a series of reactions that lead to butyrate production, starting with pyruvate as the initial sub-raw material. In addition, resident or beneficial microorganisms also have pathways that degrade harmful metabolites. In one embodiment, one such pathway includes a series of metabolic reactions that lead to ammonia oxidation. Other pathways responsible for the biosynthesis of metabolites that are beneficial to health or the degradation of metabolites that are harmful to health (such as TMAO) are within the scope of the term "beneficial pathway" in the present invention.

[0065] Butyrate production via the pyruvate pathway: The substrate pyruvate is converted to crotonyl-CoA in three steps catalyzed by three enzymes: thiolase (Thl, EC 2.3.1.9), hydroxybutyryl dehydrogenase (Hbd, EC 1.1.1.157), and clonotase / enoyl-CoA hydratase (Cro, EC 4.2.1.17). Crotonyl-CoA is converted to butyryl-CoA by butyryl-CoA dehydrogenase (Bcd). Butyryl-CoA is metabolized to butyrate via one of two routes: (i) with the help of two enzymes, phosphate butyryltransferase (Ptb) and butyrate kinase (Buk), or (ii) with the help of butyryl-CoA acetate-CoA transferase (But). Butyrate production via the pyruvate pathway is found specifically in resident bacteria, and this pathway is referred to herein as the "beneficial pathway."

[0066] Ammonia oxidation pathway: Ammonia oxidation produces nitrate through a three-step process. The enzyme ammonia monooxygenase (Amo, EC 1.14, 99.39) catalyzes the conversion of ammonia to hydroxylamine, which is converted to nitrite by hydroxylamine dehydrogenase (Hao, EC 1.7.2.6). Nitrite is further metabolized to nitrate by nitrate reductase (EC 1.7.5.1). Alternatively, ammonia oxidation produces carbamoyl phosphate by carbamoyl phosphate synthase (EC 6.3.4.16). The ammonia oxidation pathway is referred to throughout this specification as the "beneficial pathway."

[0067] In one embodiment, a set of genes encoding metabolic pathways found in microorganisms considered pathogens that biosynthesize metabolic products harmful or detrimental to health or degrade metabolic products beneficial to health are considered "harmful pathways". In one embodiment, harmful pathways include ammonia release pathways, biogenic amine production pathways, and trimethylamine oxide production pathways. Other pathways responsible for the biosynthesis of metabolic products harmful to health or the degradation of metabolic products beneficial to health are within the scope of the term "harmful pathways" in the present invention.

[0068] Pathways that release ammonia include: (a) Urea Decomposition: The hydrolysis of urea is catalyzed by the enzyme urease (EC 3.5.1.5), which breaks down urea to produce ammonia and carbon dioxide. This pathway is referred to herein as the "hazardous pathway" because it releases ammonia. (b) Lysine degradation: Lysine is metabolized to crotonyl-CoA through five intermediate steps involving five enzymes: lysine 2,3-aminomutase (KamA), lysine 5,6-aminomutase (Kam D,E), 3,5-diaminohexanoate dehydrogenase (Kdd), 3-keto-5-aminohexanoate cleavage enzyme (Kce), and 3-aminobutyryl-CoA ammonia-lyase (Kal). In the fifth step, ammonia is released as a by-product. The product of this step, crotonyl-CoA, is further converted to butyryl-CoA by butyryl-CoA dehydrogenase (Bcd, EC 4.3.1.14). Butyryl-CoA is metabolized to butyrate via one of two pathways: (i) via two reactions involving two enzymes, butyryl phosphate transferase (Ptb, EC 2.3.1.19) and butyrate kinase (Buk, EC 2.7.2.7), and (ii) via one reaction involving butyryl-CoA acetate-CoA transferase (But, EC 2.8.3.8). The lysine degradation pathway is an ammonia releasing pathway and is therefore referred to herein as the "toxic pathway". (c) Glutarate degradation: 2-oxoglutarate is degraded by 2-hydroxyglutarate dehydrogenase (L2Hgdh, EC 1.1.99.2), glutaconate-CoA transferase (Gct, 2.8.3.12), 2-hydroxyglutaryl-CoA dehydrogenase (HgCoAd, EC 4.2.1.-) and glutaconyl-CoA decarboxylase (Gcd, EC 4.1.1.70). In the first step, ammonia is released as a by-product. Crotonyl-CoA is further converted to butyryl-CoA by butyryl-CoA dehydrogenase (Bcd, EC 4.3.1.14). Butyryl-CoA can be metabolized to butyrate via one of two pathways: (i) with the aid of two enzymes, butyryl-phosphate transferase (Ptb, EC 2.3.1.19) and butyrate kinase (Buk, EC 2.7.2.7), or (ii) with the aid of butyryl-CoA acetate CoA transferase (But, EC 2.8.3.8). The glutarate degradation pathway is an ammonia releasing pathway and is therefore referred to herein as the "hazardous pathway." (d) Histidine Degradation: Histidine is converted to glutamate through four intermediate steps. In the first step, histidine is metabolized to urocanate by the enzyme histidine ammonia-lyase (Hal, EC 4.3.1.3), which releases ammonia as a by-product. Urocanate is converted to 4-imidazolone-5-propanoate by urocanase (UroC1, EC 4.2.1.49). This product is further metabolized to N-formimino-L-glutamate with the help of imidazolonepropionase (AmdhD1, EC 3.5.2.7). This product is then converted to glutamate via one of two enzymes: glutamate formimidoyltransferase (FtcD, EC 2.1.2.5) or formimidoylglutamase (HutG, EC 3.5.3.8). The histidine degradation pathway is referred to herein as the "toxic pathway" because it is the pathway that releases ammonia. (e) Formation of Tetrahydrofolate (THF): Histidine is converted to glutamate through four intermediate steps. In the first step, histidine is metabolized to urocanate by the enzyme histidine ammonia-lyase (Hal, EC 4.3.1.3), and ammonia is released as a by-product in this step. Urocanate is converted to 4-imidazolone-5-propanoate by urocanase (UroC1, EC 4.2.1.49). This product is further metabolized to N-formimino-L-glutamate with the help of imidazolonepropionase (AmdhD1, EC 3.5.2.7). N-formimino-L-glutamate is converted to N-formimino-L-tetrahydrofolate by the enzyme glutamate formimidoyltransferase (FtcD, EC 2.1.2.5) and the coenzyme tetrahydrofolate. This product is metabolized to 5-10-methyl-tetrahydrofolate with the aid of the enzyme formimidoyltetrahydrofolate cyclodeaminase (FtcD, EC 4.3.1.4). The tetrahydrofolate production pathway is an ammonia releasing pathway and is therefore referred to herein as the "hazardous pathway". (f) Degradation of Glutamic Acid (Glutamate): Glutamic acid (glutamate) is metabolized to acetate and pyruvate through four intermediate steps. Glutamic acid is first converted to L-threo-3-methylasperate involving the enzyme methylasperate mutase (MutE, EC 5.4.99.1). The product from this step is converted to mesaconic acid by methylasperate ammonia-lyase (Mal, EC 4.3.1.2), after which ammonia is released as a by-product. Mesaconic acid is metabolized to S-citramalate by the enzyme mesaconic acid hydratase (EC 4.2.1.34). S-citramate is then converted to acetate and pyruvate by citramate lyase (EC 4.1.3.22). The glutamic acid degradation pathway is referred to herein as the "harmful pathway" because it is the pathway that releases ammonia. Trimethylamine TMA Metabolism: TMA metabolism involves two processes: trimethylamine N-oxide (TMAO) degradation and TMAO biosynthesis. (a) TMAO biosynthesis: TMA is converted to the harmful metabolite TMAO in a single reaction by the enzyme trimethylamine monooxygenase (Tmm, EC 1.14.13.148). TMA is also converted to dimethylamine by the enzyme trimethylamine dehydrogenase (Tmd, EC 1.5.8.2). Dimethylamine is then degraded to methylamine by dimethylamine monooxygenase (DmnABC, EC 1.14.13.238). The enzyme methylamine dehydrogenase (EC 1.4.9.1) further degrades methylamine, releasing ammonia and formaldehyde. These steps in the aforementioned pathways are referred to herein as the “harmful pathways” since they lead to the formation of harmful metabolites and by-products. (b) Degradation of TMAO: TMAO is converted to TMA with the help of TMAO reductase (EC 1.7.2.3). In addition, the enzyme trimethylamine oxide addlase (EC 4.1.2.32) can also degrade TMAO to dimethylamine and formaldehyde. Since TMAO is a harmful metabolite, its degradation pathway is referred to herein as the "beneficial pathway."

[0070] Pathways for the production of biogenic amines include: (a) Production of putrescine (i) Arginine is converted to ornithine by the enzyme arginase (EC 3.5.3.1). Ornithine is then metabolized to putrescine by ornithine decarboxylase (Odc1, EC 4.1.1.17). (ii) Arginine is converted to agmatine by the enzyme arginine decarboxylase (EC 4.1.1.19). Agmatine is then metabolized to putrescine by agmatinase (EC 3.5.3.11). (iii) Arginine is converted to agmatine and then metabolized to N-carbamoylputrescine involving agmatinase (EC 3.5.3.12). N-carbamoylputrescine is further converted to putrescine by N-carbamoylputrescine amidase (EC 3.5.1.53). The pathway for the production of putrescine is referred to throughout this specification as the "harmful pathway." (b) Production of spermine: Putrescine is metabolized to spermine (or spermidine) with the aid of the enzyme spermidine synthase (SpeE, EC: 2.5.1.16). The spermine production pathway is referred to herein as the "hazardous pathway." (c) Cadaverine Production Cadaverine is produced from lysine by the enzyme lysine decarboxylase (EC 4.1.1.18). The cadaverine production pathway is referred to throughout this specification as the "hazardous pathway."

[0069] In one embodiment, microbial strains harboring said harmful pathways are referred to as pathogens or harmful microorganisms, and microorganisms harboring said beneficial pathways are referred to as beneficial microorganisms or commensals.

[0070] In step 204 of the method (200), a set of knowledge bases is generated using a set of input data. The set of knowledge bases includes a genus function map (FGmap), a genome metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food ingredient network, a food ingredient network, and a ProbMap, and the generation of the set of knowledge bases includes several steps, which are described below with steps 204A to 204E:

[0071] In step 204A of the method (200), a FG map and a GMP map are generated.

[0072] An FGmap is a matrix that associates functions of multiple bacterial genera belonging to one of multiple taxa with multiple metabolic pathways as columns. The first predefined index indicates one of the following: (a) Presence of metabolic pathways and (b) Absence of metabolic pathways.

[0073] In one embodiment, the first predefined index indicates the presence or absence of a metabolic pathway, with a value of 1 for the first predefined index indicating the presence of a metabolic pathway in the bacterial genus and a value of 0 indicating the absence of a metabolic pathway.

[0074] GMP is a matrix with multiple bacterial strains as rows and multiple metabolic pathways as columns, and a predefined second indicator indicates either (a) the presence of a metabolic pathway, or (b) the absence of a metabolic pathway.

[0075] In one embodiment, the second predefined indicator indicates the presence or absence of a metabolic pathway, where a value of the second predefined indicator of 1 indicates the presence of a metabolic pathway in the bacterial strain and a value of 0 indicates the absence of a metabolic pathway.

[0076] In an embodiment, the first predefined threshold criterion is defined based on the occurrence of a list of protein domains within a predefined window. The second predefined threshold criterion is defined based on a threshold ratio of biosynthetic / degradative pathways. Example scenarios for the prototypes of GMP and FGmap are shown in Tables 2 and 3 below, respectively: [Table 2] [Table 3]

[0077] The process of generating GMP is described in conjunction with the flow diagram of Figures 3A and 3B.

[0078] In step 302 of the method (300), a matrix metabolite map (Mmap) is generated. The Mmap is generated based on a list of a plurality of metabolites, a plurality of metabolic pathways, and organisms associated with each of the plurality of metabolic pathways, where an Mmap is generated for each of the plurality of metabolites. The Mmap is generated for each of the plurality of metabolites in the list ML, with details of the metabolic pathway involved in the degradation or biosynthesis of each of these plurality of metabolites obtained after a literature mining step, details of the organisms in which said metabolic pathways have been experimentally characterized. The details of said metabolic pathways include the genes / enzymes / proteins involved in the individual reactions of the pathway.

[0079] In one embodiment, a manual search for the degradation / biosynthesis pathway of metabolite Mi (which is a series of steps or chemical reactions for the degradation / biosynthesis of a candidate metabolite encoded by genes on the bacterial genome) and the genes / enzymes involved in the process is performed for each input query string Q in the previous step. The result of this process is a matrix called Metabolite Map (Mmap) with rows for each metabolite and columns for its degradation pathway and the organisms for which the pathway has been characterized. A sample scenario of a prototype table for Mmap is shown below in Table 4: [Table 4]

[0080] In step 304 of the method (300), a bacterial genome map (BGM) is generated. The BGD includes a comprehensive list of bacterial strains, and the BGM includes information related to the bacterial strains, including names of the bacterial strains, identification numbers of the bacterial strains, gene locations of the bacterial strains, and protein domains of the bacterial strains.

[0081] The BGD consists of a comprehensive list of genomic positions and corresponding functional protein sequences, while the BGM provides functional annotation of genes in the form of constituent functional protein sequences. The BGD consists of a comprehensive list of bacteria (strain names) used to analyze the genome / genome sequence. The Bacterial Genome Map (BGM) is a compilation of a list of genes in each bacterial genome listed in the BGD, ordered by genomic position, and information on the functional annotation of each gene, in the form of constituent protein domains identified in each gene.

[0082] In the embodiment, an example scenario of the sample BGM map shown in Table 5 is shown. [Table 5]

[0083] In one embodiment, information of each bacterial genome related to the translated protein sequence of each gene and its location on the bacterial genome is obtained from the repository National Center of Biotechnology Information (NCBI). The database of bacteria (strain name) whose genomes were used for the analysis is called the Bacterial Genome Database (BGD). Protein sequences are utilized in FASTA format, and feature files from the NCBI repository are utilized to confirm gene locations, however, other formats or sources of gene locations and protein sequences of microbial genomes are within the scope of the present invention. These bacterial genomes are functionally annotated using multiple methods to identify protein domains within each gene on the genome, including but not limited to gene homology (such as BLAST), hidden Markov model-based identification (such as protein family or PFAM database), position specific scoring matrix (PSSM), etc. In one embodiment, a database PfamDB (or protein domain family database) containing HMMs corresponding to all protein domains can be obtained as taught in the PFAM database, and the genomes can be searched using publicly available software such as HMMER. The use of other protein domain identification methods, functional protein annotation methods, databases, or structure-based protein identification methods is also within the scope of this disclosure. The list of genes for each bacterial genome listed in the BGD, ordered in terms of genomic location (starting from the origin of replication) as listed in the NCBI feature file, is called the Bacterial Genome Map (BGM). The BGM also contains information about the functional annotation of each of these genes in the form of the constituent protein domains identified for each gene.

[0084] In step 306 of the method 300, a metabolic pathway domain map (MPDM) is generated. A metabolic pathway-domain map (MPDM) is generated for each metabolite from the plurality of metabolites based on the Mmap and the BGM, where the MPDM is composed of an exhaustive list of protein domains associated with each of the plurality of metabolic pathways.

[0085] In one embodiment, the MPDM is composed of each pathway PiM corresponding to each metabolite Mi of the ML and the protein domains corresponding to each of these pathways. An example prototypical scenario of the MPDM is shown in Table 6: [Table 6]

[0086] In one embodiment, the Metabolic Pathway-Domain Map (MPDM) is composed of all metabolite (in Mmap) degradative and biosynthetic pathways and the domains corresponding to each of these pathways.

[0087] In one embodiment, each pathway is considered as a key found in the hash MPDM, and the value corresponding to the key is a list of corresponding domains in the pathway. Genes belonging to a microbial pathway are often located close to each other on the corresponding genome, and are called gene clusters. The distance on the genome in terms of the number of flanking genes that a set of domains forming a pathway should lie to form a functional gene cluster varies and is often defined using manual and literature-based curation. The distance in this embodiment is defined as the number of genes (called window size) based on the genomic position within which the domain should be to indicate a gene cluster and thus the presence of a pathway. Each protein domain (pfams) corresponding to the value of the hash MPDM of each pathway used as a key is searched in BGM to check whether the protein domains that constitute the metabolite biosynthesis / degradation pathway are present as gene clusters on each genome registered in BGM.

[0088] In step 308 of the method (300), the presence of multiple metabolic pathways is identified in the list of bacterial strains and genomes using MPDM and BGM based on a predefined first threshold criterion.

[0089] In one embodiment, a predefined first threshold criterion is defined based on the occurrence of a list of protein domains within a predefined window. Any set of domains is considered to form a gene cluster if it is located within a defined window size on the genome. Window size refers to the number of genes upstream and downstream of a domain that is known to be part of a pathway in order to accurately annotate a gene cluster. In one embodiment, a predefined first threshold criterion of a window is defined and utilized as a window size of 20 genes upstream and downstream of a query protein domain (referring to any one protein domain in a metabolic pathway) on the genome. The domain assignment based on gene name and pfam database is recorded if there are other protein domains in the metabolic pathway within the window (20 in this case). The window size varies depending on various factors such as pathway candidates and domains involved. Furthermore, a pathway is considered to exist if the number of domains in the genome that contribute to this pathway and are present within a window size (e.g., +20 genes and -20 genes of the query protein domain if the window size is 20 genes) exceeds a threshold (which varies from pathway to pathway and is obtained using literature mining and manual curation), which is the ratio of the total number of domains assigned to the MPDM corresponding to this pathway that corresponds to the minimum number of domains required to confirm the existence of the pathway.

[0090] In step 310 of the method (300), a GMP is generated using the BGM, the MPDM, the metabolic pathways, and the metabolites. The GMP is a matrix with a set of bacterial genomes (bacterial strains) as rows and a list of metabolic pathways corresponding to the formation of each metabolite as columns.

[0091] In one embodiment, after identifying a list of biosynthetic-degradative pathways for a list of genomes from the BGM based on a predefined first threshold criterion, a multidimensional matrix containing the genome name and pathway information for each metabolite is created, which is called the Genome-Metabolite-Pathway Map (GMP).

[0092] The GMP map is given a value of either 0 or 1 for each metabolite and pathway based on a first predefined criterion. The first predefined criterion is for each pathway in the bacterial genome. For bacterial genomes where the number of domains corresponding to a pathway in the MPDM is not present on the genome or the number found within a defined window size (20 in this case) does not exceed a threshold, a value of 0 is assigned for that genome and the pathway in the GMP. In one embodiment, the window size can be 20 genes present contiguously on the microbial genome. If the number of pathway protein domains found on the genome exceeds a threshold (defined for pathway candidates using literature mining and manual curation) and is present within a defined window size (20 genes in this case) on the microbial genome, the corresponding genome and pathway in the matrix GMP is assigned a value of 1. The window size can vary depending on the system and the candidate pathway. In one embodiment, the presence of a metabolic pathway in the microbial genome in the GMP is indicated by a predefined value of 1, and the absence of the pathway in the genome is indicated by a predefined value of 0. Alternative embodiments where the predefined values ​​are values ​​other than 1 and 0 are also within the scope of the present invention.

[0093] In step 312 of the method (300), an FGmap is generated using GMP. The FGmap is a matrix with bacterial genera as rows and metabolic pathways as columns.

[0094] The FGmap is generated based on the GMP. The FGmap is a matrix with a set of genera as rows and a list of biosynthetic and degradative pathways for each metabolite as columns. The generation process of the FGmap involves processing the GMP at the genus level based on a predefined second threshold criterion and the BGM. The FGmap is represented as a matrix with a set of genera as rows and a list of biosynthetic and degradative pathways for the production of each metabolite as columns.

[0095] In one embodiment, if the input taxonomic abundance is obtained at the genus level, the GMP can be further processed to identify the pathway composition at the genus level. The presence of the pathway in each strain belonging to the genus is analyzed. In the BGD, the pathway is determined to be present in the genus Ge if the percentage of the number of strains of the genus Ge that have the pathway among the total number of sequenced strains belonging to the genus Ge exceeds a second pre-defined threshold criterion. The second pre-defined threshold criterion value is also determined using manual intervention since the number of strains sequenced for a genus may vary and the threshold needs to be determined accordingly. In one embodiment, the second pre-defined threshold criterion can be 0.7, and the pathway can be considered to be present in the genus Ge if 70% of the strains of the genus Ge whose sequences are available in the repository of the genus Ge contain the pathway. Different thresholds can also be determined by manual analysis or literature mining and are within the scope of this disclosure. The binary genus-pathway matrix thus obtained contains the value 0 (which is a predefined value) if no pathway is found in more than a threshold number of strains belonging to the genus, and contains the value 1 (which is a predefined value) if the number of strains in which the pathway is found is more than or equal to the threshold. This matrix is ​​called Fgmap. In another embodiment, predefined values ​​other than 0 and 1 are also within the scope of the present invention. GMP and FGmap together constitute the Microbial Knowledge Base Module 204, with another column referring to whether the metabolite is known in the literature as beneficial or harmful to gut health, as described in List ML. In one embodiment, the total number of strains in the genus Ge can be obtained by a genome repository in which the genome sequences of the bacterial strains are deposited. In one embodiment, the repository may include the NCBI or HMP genome repository.

[0096] Referring to FIG. 2B, in step 204B of the method (200), a TAXA_NET graph is generated. The TAXA_NET graph is based on the plurality of taxa and the FGmap, and the TAXA_NET graph is an undirected graph. Each node of the TAXA_NET graph corresponds to a taxon from the plurality of taxa, and the edges indicate a positive relationship between pairs of taxa, where each taxon from the plurality of nodes is related to a function of the plurality of taxa from the FGmap, and the positive relationship is composed of one of mutualism, mutualism, facultativeness, and cooperation.

[0097] In one embodiment, experimentally reported microbial abundance data collated from multiple cohorts in healthy human guts are used to construct a taxon-associated reference profile, TAXA_NET. TAXA_NET is enriched with functional information and evidence mining, where the functional information can be obtained from GMP and FGmap. Each taxon in TAXA_NET is associated with a function based on genus-function or genome function mapping described in these matrices. Furthermore, functional information is added to each taxon based on automated literature mining and manual curation. TAXA_NET is an undirected network with taxon names as nodes and edges connected when positive relationships (including but not limited to mutualism, commensalism, symbiosis, and cooperation) are identified between pairs of microbial taxa.

[0098] In step 204C of the method 200, a TAXA-food ingredient network is generated. The TAXA-food component network is generated based on the multiple food components of each taxon in TAXA_NET. The TAXA-food component network is an undirected network of each taxon and the food components used as metabolic sources by that taxon.

[0099] In one embodiment, an undirected network of taxon associations with food components used as metabolic sources (e.g., carbon and nitrogen sources) by microbial taxa is constructed and named TAXA_CONSTITUENT_NET.

[0100] In step 204D of the method (200), a food component network is generated. The food component network is generated based on the food items of each food component in the TAXA-food component network. The food component network is an undirected network of each food component contained in the food.

[0101] In one embodiment, an undirected network of associations between food components (eg, carbon and nitrogen sources) and foods is constructed and named FOOD_CONSTITUENT_NET.

[0102] In step 204E of method 200, a ProbMap is generated by generating a transpose of the GMP, the ProbMap being a matrix with rows containing a plurality of metabolites and each metabolite from a plurality of metabolic pathways, and columns containing a plurality of bacterial strains, probiotics, prebiotics, and dietary components capable of biosynthesizing and / or degrading the corresponding plurality of metabolites.

[0103] In one embodiment, the Probmap is generated based on providing metabolite biosynthesis / degradation pathways as rows, genomes as columns, and additional columns for each metabolite biosynthesis / degradation pathway providing details of dietary supplements, dietary information, or prebiotics that enhance or reduce metabolite Mi as reported in the literature. The additional columns also provide details of probiotics including bacterial strains that can supplement the corresponding pathway if it is a beneficial pathway or inhibit it if it is a harmful pathway. Details of these probiotic strains can be obtained by the strains listed in the ProbMap columns that have a value of 1 for the corresponding beneficial or harmful pathway.

[0104] In step 206 of the method (200), multiple intestinal samples are obtained from the individual at two timestamps. The two timestamps include timestamp 1 and timestamp 2.

[0105] In one embodiment, multiple intestinal samples are obtained from at least one of the individual's stool, intestinal swab, intestinal tissue, and intestinal fluid at timestamp 1 and timestamp 2.

[0106] In step 208 of method 200, a taxon count matrix is ​​generated for the multiple gut samples. The taxon count matrix includes taxon set 1 and taxon set 2, where taxon set 1 and taxon set 2 are generated using timestamp 1 and timestamp 2.

[0107] In step 210 of method 200, the taxon abundance matrix and the FGmap are used to generate pathway abundances for each of the multiple gut samples obtained at timestamp 1 and timestamp 2. The pathway abundances are generated using sample processing techniques, and the pathway abundances for each timestamp indicate the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp.

[0108] The steps of the sample processing technique are described below in conjunction with the flow diagram in Figure 4:

[0109] In step 402 of the method 400, nucleic acids are extracted from the obtained intestinal sample. The extraction of nucleic acids from the obtained intestinal sample is based on nucleic acid extraction techniques.

[0110] In one embodiment, the nucleic acid extraction technique involves extracting the nucleic acid content from the obtained sample using modern laboratory accepted methods. In one embodiment, mini DNA extraction kits from companies such as Qiagen can be used to extract DNA.

[0111] In step 404 of the method (400), a plurality of nucleotide sequences are generated from the extracted nucleic acid by a sequencer via the nucleotide sequence generation unit 212. This is performed using a sequencer.

[0112] In an embodiment, a sequencer is used for sequencing, and the sequencing step is performed using high-throughput sequencing technology. As a result of the sequencing, multiple nucleotide sequences are generated. Nucleic acid content is extracted from the obtained sample using methods acceptable in several laboratories. In one embodiment, mini DNA extraction kits provided by companies such as Qiagen can be utilized for DNA extraction. Other extraction kits such as Norgen, Purelink, OMNIgene / Epicenter can also be utilized. The extracted nucleic acid can be subjected to genus level genotypic assays to obtain the microbiome composition of the obtained sample. The microbiome composition of the gut sample can be obtained by nucleic acid or protein analysis. Nucleic acid analysis may include DNA, RNA, mRNA, rRNA, and can be performed using several methods such as polymerase chain reaction (PCR), qPCR, pyrosequencing, denaturing gradient gel electrophoresis (DGGE), restriction fragment length polymorphism, microarray, next generation sequencing. Any other method capable of detecting nucleic acid sequences or DNA hybridization is within the scope of the present invention. Protein analysis includes gel electrophoresis, mass spectrometry, AQUA, iTRAQ, etc. Other methods for determining the presence of proteins that can be used as indicators of the presence of specific bacteria are within the scope of the present invention. Other laboratory-accepted methods for nucleic acid and protein analysis can also be utilized to reveal the composition of an individual's gut microbiota.

[0113] In step 406 of the method (400), a bacterial taxonomic abundance matrix is ​​obtained via a number of programs and software implemented in one or more hardware processors 104.

[0114] In one embodiment, genes considered as markers of bacterial phylogeny, such as 16S rRNA, can be amplified using classification methods such as polymerase chain reaction (PCR) or qPCR. In one embodiment, the sequenced 16S rRNA can be classified into taxa and genus or species level using a Naive Bayes classifier, such as implemented in the Ribosomal Data Project, or sequence-based matching with different 16S rRNA databases, such as implemented in the Ribosomal Database Project or the Green genes database. In yet another embodiment, an in-house taxonomic database (such as a 16S rRNA database) can be created for the strains of sequenced bacterial genomes. These databases can be used to classify the 16S rRNA sequenced strains obtained from gut samples into genus or species level taxa. Any other method of identifying the taxonomic composition of a microbiome sample is within the scope of this disclosure.

[0115] A taxonomic database stores markers for multiple taxa, including, but not limited to, one or more of the following: genetic sequences (e.g., conserved sequences for a taxon; sequences that are markers for a taxon; invariant sequences for a microbial population; features such as microbiome diversity; microbiome functional repertoires including metabolic pathways, etc.; metabolite markers; natural product or secondary metabolite markers; diagnostic, predictive, molecular biomarkers; or other suitable markers associated with a microorganism (e.g., a taxon)).

[0116] The gene sequences stored in the taxonomic database include one or more variable or conserved regions of rRNA gene sequences corresponding to multiple taxa, including one or more of the following: 16S, 18S, 30S, 40S, 50S, 60S, 5S, 23S, 5.8S, 28S, 70S, 80S, and / or any other suitable rRNA genes.

[0117] In another embodiment, the taxonomic database can store reference values ​​for relative abundance ranges of different microorganisms. These ranges include abundances associated with healthy conditions and abundances associated with unhealthy conditions for one or more conditions. The reference values ​​for the abundance ranges can be obtained based on a set of values ​​obtained from gut biological samples from a population of individuals.

[0118] The reference markers in the intestinal sample can be based on the amplification of a set of primers corresponding to these markers, selected based on the characteristics of the markers shared by various taxa with different variable regions for each taxa. The amplification of the primers leads to the formation of reads that can be sequenced using a sequencer as described in this disclosure. In one embodiment, the resulting reads can be compared to a set of reference markers in a database using sequence similarity to identify the taxa to which the read belongs.

[0119] Further, a genus abundance matrix is ​​created, which consists of genera as rows and the corresponding abundances for each input sample. In one embodiment, the input samples can be obtained from a single or multiple time points from an individual. In another embodiment, the input samples can be obtained from individuals belonging to two different groups or populations. In one embodiment, the population or group can be individuals participating in a case-control study. Any other study involving subjects belonging to two or more different categories is within the scope of the present invention. Similarly, a species abundance matrix or a strain abundance matrix is ​​created according to the taxonomic level at which the 16S rRNA sequences were classified. The matrices are named M1 and M2, corresponding to samples obtained at two time points for one individual or from two different populations. Furthermore, genera and species are the order of biological classification of organisms, with species being below genus in the taxonomic classification hierarchy. Thus, the abundance table at each taxonomic level is created according to the taxonomic resolution obtained in the dataset using the classification algorithm.

[0120] In step 408 of the method (400), pathway abundance matrices of ML at two timestamps are obtained. Upon obtaining the microbiome composition, the bacterial taxonomic abundance matrices of each sample M1 and M2 are fed to a processor having software instructions for converting them into pathway abundance matrices for each sample. In one embodiment, for samples obtained for individuals or populations at two timestamps, one matrix represents two phenotypically distinct populations or groups of subjects. In one embodiment, the distinct populations or groups represent a healthy subpopulation, and a biodiversity disorder subpopulation, on the one hand. In another embodiment, the distinct populations or groups of subjects belong to two stages of disease under treatment, with matched characteristics between the populations in terms of geography, age, diet, lifestyle, etc. In one embodiment, the populations or groups may be individuals participating in a case-control study. Other studies involving subjects belonging to two or more different categories are within the scope of the present invention. The genus abundance matrices M1 and M2 of said samples can be obtained using the methods discussed for obtaining taxonomic abundance matrices. The pathway abundance matrices of the two stages can be calculated as follows: Path abundance matrix M1P = FGmap * M1 Path abundance matrix M2P = FGmap * M2.

[0121] In another embodiment, genes that can be used as markers for bacterial phylogeny (e.g., 16S rRNA in bacteria) can be utilized to determine pathway abundance. In one embodiment, the markers can include bacterial 16S rRNA and encoded products. Utilization of any other bacterial marker genes, such as 23S rRNA, rpoB, cpn60, gyrB, dnaK, dsrAB, amoA, amoB, mip, horA, hitAM, recA, ica, frc.oxc, or / and evaluation of fungal internal transcribed spacer 1 (ITS1), etc., is within the scope of the present invention. 16S rRNA sequences corresponding to the individual's gut microbiota can be amplified after DNA / RNA extraction and sequencing procedures. The sequences thus obtained can be matched against a database of 16S rRNA sequences for each strain of BGD, stored in memory as SEQB. This matching can be performed by a processor with software instructions to identify the nearest neighbor genes. The input 16S rRNA sequences corresponding to the individuals of the population can be matched with the sequences of SEQB using sequence alignment software such as BLAST, FASTA, etc. The pathway configuration of the nearest neighbors thus obtained can be utilized as the pathway of the input 16S rRNA sequence. An abundance matrix corresponding to each match can be obtained with the rows consisting of the strain names and the columns of the sample names, and the number of times the 16S rRNA sequence corresponding to that strain was obtained in each sample is recorded. A similar methodology can be used for other said bacterial marker genes within the scope of this disclosure. In one embodiment, for a population with samples or one matrix obtained for an individual at two timestamps, two phenotypically distinct populations or groups of subjects are represented. In one embodiment, the distinct populations or groups represent one healthy subpopulation and the other a subpopulation with biodiversity abnormalities. In another embodiment, the separate populations or groups of subjects belong to two stages of a disease under treatment, with inter-population characteristics matched in terms of geography, age, diet, lifestyle, etc. In one embodiment, the populations or groups may be individuals participating in a case-control study.Other studies including subjects belonging to two or more different categories are within the scope of the present invention.Strain abundance matrices M1S and M2S of a sample can be obtained when the reads are classified to the strain taxonomic level. Pathway abundance matrix M1P = M1S * GMP Pathway abundance matrix M2P = M2S * GMP

[0122] In another embodiment, the generations (Gi) can be identified as significantly increased or decreased using methods such as statistical tests, such as parametric tests, non-parametric tests, machine learning-based algorithms including decision trees, neural networks, random forests, etc. for M1 and M2. Abundance matrices corresponding to these different abundance genera Gi can be obtained from M1 and M2, with selected genera as rows and samples as columns, and these matrices can be called SM1 and SM2. These genera can be queried against the knowledge base FGmap using a processor with software instructions that can provide information about the metabolites that each of these genera can produce. Path abundance of SM1, called SM1G = SM1 * FGmap Path abundance of SM2, called SM2G = SM2 * FGmap

[0123] In one embodiment, after obtaining the pathway abundances for each sample, the pathway abundances (M1P and M2P) are compared. These comparisons can be made by the processor using software instructions to perform statistical tests such as parametric tests, non-parametric tests, machine learning-based algorithms including decision trees, neural networks, random forests, etc. The software program for analyzing these changes may also include evaluating the fold change within each bacterium between the two time points or populations. In another embodiment, only one sample is taken from the individual and the abundance of the genera in the sample can be compared to a predetermined reference value or range of values ​​for the corresponding genera in a healthy population. This value can also be compared to a reference value or range of values ​​for the abundance of a particular genera in a gut microbiota disorder. Genera with abundance values ​​that are significantly different from the reference value can be used for further analysis. In one embodiment, the processor is configured to analyze and identify metabolites that may be significantly increased or decreased between these samples (two data sets of the same individual or population at two time points) based on the comparison of the pathway matrices and the utilization of MPDM in which these pathways are mapped to corresponding metabolites. The processor is also configured to analyze whether these metabolite changes are statistically significant using the statistical techniques described above. Thus, the processor is configured to analyze the taxonomic composition of the microbiome samples and identify metabolites that may be significantly increased or decreased between these samples. In one embodiment, the input samples can be obtained from a single time point or multiple time points from an individual. In another embodiment, the input samples can be obtained from individuals belonging to two different groups or populations. In one embodiment, the separate populations or groups represent a healthy subpopulation and a biodiversity disorder subpopulation, respectively. In another embodiment, the separate populations or groups of subjects belong to two stages of disease under treatment, with matched characteristics between the populations in terms of geography, age, diet, lifestyle, etc. In one embodiment, the populations or groups can be individuals participating in a case-control study. Any other study involving subjects belonging to two or more different categories is within the scope of the present invention.Additionally, analysis of the knowledge base, similar to ListML using software programs in a processor, helps identify metabolites that are beneficial or detrimental to human health. In one embodiment, metabolites such as short chain fatty acids, including butyrate, are well known in the literature to be beneficial to human health. Similarly, metabolites such as biogenic amines, such as ammonia and trimethylamine, are associated with various inflammatory and disease conditions in humans. Comparison of pathway abundance matrices using statistical methods yields pathways and metabolites that are significantly different between the abundance matrices of the two pathways. These pathways and metabolites are referred to as "putative metabolites" and "putative pathways," respectively.

[0124] In another embodiment, metabolites can be identified in gut samples. Any laboratory-accepted method for identifying metabolites is within the scope of the present invention. In one embodiment, the collected gut sample can be dried and suspended in water and further processed by different chromatography and mass spectrometry techniques, including ultra-performance liquid chromatography or high performance liquid chromatography followed by high resolution / tandem mass spectrometry MS / MS. In another embodiment, biosensors, including those implemented in devices such as nanosensors and e-nose, can be used to assess metabolites in feces. Other methods of analyzing and quantifying metabolites in gut samples are within the scope of the present invention. Some of the methods for identifying and quantifying metabolites can also include colorimetric assays, chemical assays, enzymatic assays, etc. of metabolites. Metabolite content can be determined for a single sample taken from an individual, samples taken from an individual at two time points, and samples taken from two phenotypically distinct populations. Metabolite content of a single sample taken from an individual can be compared to a reference value for healthy individuals to identify metabolites that are elevated or decreased in concentration in an individual. Metabolites in samples can also be compared for samples corresponding to two points in the same individual or samples corresponding to two types of population data. The significance of the metabolite differences between one sample and the reference value, two time points or two populations is calculated using processor-implemented software with statistical methods such as parametric tests, non-parametric tests, and machine learning-based algorithms including decision trees, neural networks, random forests, etc. The software programs for analyzing these changes also include assessing the fold change within each bacterium between the two time points or populations.

[0125] An individualized therapy provides one or more therapies to a user (e.g., a human subject; a healthcare professional providing the therapy; etc.) for the treatment / management of one or more biological conditions (e.g., reducing risk; improving the state of a condition; improving symptoms and / or other suitable aspects of a condition; changing the user's microbiome profile toward a state that is more susceptible to a therapy for one or more biological conditions; changing the microbiome profile toward a state that is more susceptible to a therapy for a disease; etc. These include orally administered pharmaceuticals such as probiotic supplements (e.g., type and amount of microorganisms included, including type, dosage, treatment time), food supplements, antibiotics (including type, amount, treatment schedule, etc.), medical devices (e.g., medicine dispensers, devices that administer antibiotics), user devices (e.g., biosensors), and / or other suitable components. Individualized therapies include applications that alter / modify the composition and / or function of the microbiome, thereby alleviating symptoms or improving health. In certain examples, the therapy can be provided as a probiotic-based therapy formulated from one or more microorganisms and / or their metabolic products (e.g., a microbial population described herein, etc.).

[0126] 2D, in step 212 of method (200), a perturbed pathway is identified. The perturbed pathway is indicative of a deterioration in the intestinal health of the individual, and the perturbed pathway is identified based on a comparison of pathway abundances of multiple intestinal samples taken at timestamp 1 and timestamp 2 using statistical methods, and the perturbed pathway is identified based on one of the following cases: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, the decrease indicating a decrease in a set of beneficial microorganisms; or (b) Case B: An increase in pathway abundance of a population of harmful pathways, which increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of a set of beneficial pathways is decreased and the pathway abundance of a set of harmful pathways is increased, indicating an increased set of harmful microbes and a decreased set of beneficial microbes.

[0127] In an embodiment, a "perturbed pathway" refers to an increase in the biosynthesis of a harmful pathway or a harmful metabolite or the degradation of a beneficial metabolite, or a decrease in a beneficial pathway or a beneficial metabolite or the degradation of a harmful metabolite, and the perturbed pathway is identified based on a statistical method, including one of a parametric method, a non-parametric method, or a machine learning based algorithm.

[0128] In step 214 of method 200, an initial set of personalized therapeutic and dietary recommendations is determined. The initial set of personalized therapeutic and dietary recommendations includes at least one of personalized therapeutic and personalized dietary recommendations using the ProbMap, and the initial set of personalized therapeutic and dietary recommendations are rows that correspond to the identified perturbed pathways.

[0129] The individualized treatment will be determined as follows: (a) multiple bacterial strains with perturbed pathways, or (b) a plurality of bacterial strains having pathways for degradation or / and biosynthesis of high probability metabolites, the high probability metabolites being metabolites corresponding to pathways perturbed in the ProbMap;

[0130] In an embodiment, the ProbMap is used to determine an initial set of personalized therapeutic and dietary recommendations. In an embodiment, a significant change in a metabolite or pathway may refer to a statistically significant change observed in a comparison of pathway matrices for samples obtained from the same individual at two time points. In another embodiment, a significant change may refer to a statistically significant change in the level of a particular metabolite in gut samples obtained from an individual at two time points. Statistical significance refers to a rule of statistical hypothesis testing known in the art that a feature is highly unlikely to occur given the null hypothesis. A significant change may also be referred to as a feature identified as important for discriminating between two conditions using machine learning methods such as supervised or unsupervised, neural networks, regression methods, or any other method known in the art is used to identify features important for deciphering differential function between samples obtained from a set of individuals belonging to different classes.

[0131] In one embodiment, the class is composed of healthy and diseased individuals. In another embodiment, the class can include samples of individuals taken before and after a treatment regimen at different time points. Furthermore, the fold change in a predefined value can also indicate significant changes in a specific function between two sample time points or between samples obtained from phenotypically different populations.

[0132] Depending on the metabolites and pathways that showed significant changes by comparing the pathway abundance matrices, probiotic interventions and treatment regimens can be designed accordingly. If a decrease in a beneficial metabolite or its biosynthetic pathway is observed, probiotics are expected to be composed of bacteria that can biosynthesize this metabolite and survive in the intestinal environment. Conversely, if an increase in a harmful metabolite or its biosynthetic pathway is observed, or a decrease in its degradation pathway is observed, microorganisms that can degrade that metabolite may form a more efficient probiotic. Information on strains that can biosynthesize and degrade specific metabolites has already been cataloged in matrix GMP and matrix FGmap and stored in memory as a back-end database. In addition, information on dietary supplements, prebiotics, dietary components, etc. that may increase the production or degradation of specific metabolites is included in these matrices, and ProbMap has been created using information obtained from mining literature. For example, increasing resistant starch in the diet can increase butyrate producers, which are resident bacteria in the microbiome. Similarly, reducing animal protein diets can reduce harmful ammonia and carnitine in the intestine. Information on the dietary intake or nutritional supplements required to replenish beneficial metabolites decreased in an individual and to reduce harmful metabolites significantly overabundant in an individual was obtained using literature mining and manual curation. This information for each metabolite in the FGmap has been added as a column in the ProbMap. Thus, the Probmap is constructed in a transposed form of the information contained in the genome-metabolite-pathway GMP matrix, where the metabolite biosynthesis or degradation pathways of metabolite Mi in the Mmap form the rows and the genome is treated as the column. In addition to this, columns detailing nutrient supplements, dietary information or prebiotics that increase or decrease metabolite Mi as reported in the literature were added to each row corresponding to the biosynthesis or degradation of the metabolite, respectively.Thus, based on metabolites or their biosynthetic / degradative pathways that are significantly increased / decreased within an individual from data collected at one or more time points or across two populations, the ProbMap database can be queried to design personalized probiotic and dietary treatments based on their function. Example prototypic scenarios for the ProbMap database are shown in Table 7: [Table 7]

[0133] The processor has software instructions for identifying strains of bacteria that can be formulated to create a probiotic cocktail that can enhance beneficial metabolites and degrade harmful metabolites depending on the metabolites / pathways analyzed and identified in the sample. The likely pathways or perturbed pathways or likely metabolites obtained in the present disclosure can be queried with the ProbMap knowledge base. In one embodiment, if the likely pathways or perturbed pathways or metabolites include a harmful metabolite or harmful pathway, the ProbMap is queried to find the row of the degradative pathway of the likely metabolite and identify strains with a value of 1 for this pathway. In another embodiment, if the putative pathway or metabolite includes a reduction in a beneficial metabolite or beneficial pathway, the ProbMap is queried to find the row of the pathway of biosynthesis of the putative metabolite and identify strains with a value of 1 for this pathway. Similarly, the information in the additional columns corresponding to the likely pathways and conditions can be used to design a personalized dietary treatment.

[0134] In another embodiment, if an individual lacks microbes that produce a beneficial metabolite, feces from another individual with a microbiome capable of producing this metabolite can be administered using fecal microbial transplantation. Similarly, fecal microbial transplantation can be administered to an individual so that it can be used to replace organisms that cause the biosynthesis of harmful metabolites. A donor individual is an individual whose gut microbiome exhibits the necessary composition to perform the desired function in this scenario. A recipient is an individual whose gut sample and microbiome have been tested and found to have a dysbiosis of the gut microbiome. In such a case, the microbiome of the donor individual whose feces is to be transplanted to the recipient should have either bacteria capable of degrading the harmful metabolites or bacteria that do not biosynthesize harmful metabolites and can replace the harmful bacteria already present in the recipient individual. In one embodiment, a fecal sample obtained from a donor individual can be treated with a diluent and filtered using a filter medium, where the mixed sample can be filtered through successively smaller sized sieves. The filtrate thus obtained can be centrifuged and frozen or lyophilized. This preparation made with the gut microbiota of the healthy individual can be administered orally or by nasogastric tube or colonoscopy to the recipient. In one embodiment, the gut microbiome of the recipient individual can be flushed / removed using methods such as, but not limited to, administration of antibiotics. This helps the donor's microbiome to colonize the recipient's gut. In one embodiment, a combination of probiotics and nutritional supplements that promote the growth of beneficial bacteria (prebiotics) can be administered in the form of synbiotics or metabiotics or parabiotics to restore beneficial gut bacterial strains.

[0135] Thus, analyzing an individual's microbiome and identifying metabolites that may primarily affect health status can be used to personalize probiotic formulations and regimens using steps performed by the processor based on a pre-created knowledge base stored in the memory. The processor is further configured to analyze the patient's genetic data, blood reports, medical history, as well as factors such as geography, lifestyle, etc., to further select strains capable of biosynthesizing or degrading said metabolites and that are compatible with various factors and already existing microbiomes in the patient. Various health parameters and physiological markers of an individual may also be indicative of metabolites that can be removed or replenished in the individual using treatments or dietary interventions.

[0136] Probiotic supplements can be formulated and administered in a variety of forms, including food, dairy products, and tablets, depending on the patient's requirements. Furthermore, the method of administration and dosage can be individualized according to the patient's needs.

[0137] Follow-up studies may be necessary to evaluate the effectiveness of probiotics. Additional tests to investigate the gut microbiota and its corresponding metabolites can be performed and the formulation adjusted accordingly. An entirely new formula or dietary pattern may be prescribed by the healthcare provider using the described method and the knowledge base created after analyzing the results of the follow-up tests. The knowledge base contains information on a large number of bacterial strains and their association with metabolic ability to degrade or biosynthesize various metabolites (beneficial or harmful). This wide variety of strains can help customize probiotic formulations for each individual or design multiple consortia for the same individual. Factors taken into account include, but are not limited to, physiological and genotypic factors, geographic factors, lifestyle, dietary patterns, laboratory reports and findings, etc.

[0138] As used herein, a "health status reference value or range of values" refers to a reference value or range of values ​​for taxon abundance established by analysis of the composition of the microbiota in a population determined to be in good health, obtained from publicly available microbiome profiles of healthy individuals or groups of individuals or populations. Health status can be diagnosed by methods known in the art, including but not limited to clinical parameters, laboratory investigations, investigations by certified clinicians or health practitioners, etc. Reference range values ​​may also be established by governmental, academic, private, or non-profit organizations that manage libraries of microbiome profiles, or by organizations that catalog information on reference values ​​for taxa and metabolites obtained from analysis of microbiome profiles.

[0139] As used herein, "reference value or range of reference values ​​for gut dysbiosis" refers to a reference value or range of reference values ​​for taxon abundance established by analysis of the composition of the microbiota in a population determined to have gut dysbiosis or to be affected by a microbiome-related disease state. The disease state can be diagnosed by methods known in the art for diagnosing disease states, including but not limited to clinical parameters, laboratory investigations, investigations by certified clinicians or health practitioners, and the like. This information can also be obtained from publicly available microbiome profiles of individuals or populations with known manifestations of dysbiosis or microbiome-related diseases / disorders in the gut. Reference range values ​​for gut dysbiosis or disease / disorder states can be established by government agencies, academic institutions, private organizations, or non-profit organizations that manage libraries of such microbiome profiles or catalog information on these reference values ​​for taxa or metabolites obtained from analysis of the microbiome profiles.

[0140] Thus, a reference value for a healthy individual would indicate at least one of the following: a relative abundance of a taxon in a microbiome profile that is recognized as representative of a subject in good health, and in the absence of physiological symptoms of a disease state, or in the absence of clinical / diagnostic parameters known in the art as indicative of a disease. Similarly, a reference value for a diseased individual refers to a relative abundance of a taxon in a microbiome profile that is recognized as representative of a disease / disease risk. Furthermore, higher than the reference value is understood to mean that the relative abundance of the taxon in the collected sample is higher than the reference value, to an extent that may be associated with a disease state. The term lower than the reference value means that the relative abundance of the taxon in the collected sample is lower than the reference value to an extent that may be associated with a disease state.

[0141] In step 216 of the method (200), taxon sets are identified in the taxon abundance matrix obtained at timestamp 2. The taxon sets include TAXA_SET_P and TAXA_SET_A.

[0142] TAXA_SET_P is composed of taxa having a predefined first value for one or more of the informative pathways in either FGmap or GMP. In an embodiment, the predefined first value for one or more of the informative pathways is 1.

[0143] TAXA_SET_A is composed of taxa having a predefined second value for one or more of the harmful pathways in either FGmap or GMP. In an embodiment, the predefined second value for one or more of the harmful pathways is 1.

[0144] In one embodiment, FIG. 6 shows that the taxon set is composed of TAXA_SET_P and TAXA_SET_A.

[0145] In step 218 of the method (200), a set of first neighbors is identified from the TAXA_NET graph for each taxon in TAXA_SET_P at timestamp 2. The set of first neighbors are the direct neighbors of TAXA_SET_P. The direct neighbors consist of TAXA_SET_FN, TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW.

[0146] In one embodiment, Figure 6 shows a set of initial neighbors in a taxonomic group that includes TAXA_SET_P and TAXA_SET_A. The first set of neighbors are the direct neighbors of TAXA_SET_P. The immediate neighbors include TAXA_SET_FN, TAXA_SET_FN_COM, TAXA_SET_INTERSECT, aa TAXA_SET_NEW.

[0147] First neighbors refer to the set of taxon nodes in TAXA_NET that are directly connected by edges to the set of nodes that belong to TAXA_SET_P. The first neighbors of a query microbial node in a microbial association network essentially indicate the list of other microbial nodes that are the closest relatives in the microbial community and have a direct influence on the query node.

[0148] In step 220 of the method (200), a personalized therapeutic intervention for improving the individual's gut health is recommended. The therapeutic intervention may include recommending one of the following: (a) Prebiotic cocktail, (b) probiotic cocktail, (c) Optimized diet .

[0149] A therapeutic intervention is recommended based on the identified first neighborhood using the set of knowledge base and the initial set of personalized therapeutic and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) For case A (reduced pathway abundance of a group of beneficial microorganisms), we recommend a prebiotic cocktail, a probiotic cocktail, and an optimized diet, which can replenish multiple beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of harmful microbial populations), recommending an optimized diet, which promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; (c) For case C (pathway abundance of beneficial microbial populations is decreased and pathway abundance of harmful microbial populations is increased), a prebiotic cocktail, a probiotic cocktail, and an optimized diet are recommended, where the prebiotic cocktail, the probiotic cocktail, and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms, and suppress the growth of harmful microorganisms.

[0150] Individualized therapeutic intervention recommendations include recommending one of a prebiotic cocktail, a probiotic cocktail, and an optimized diet based on key taxa, as described below:

[0151] Recommend personalized therapeutic interventions for reduced abundance of multiple beneficial microbial pathways1: personalized therapeutic recommendations include: (a) Prebiotic cocktail, (b) Probiotic cocktail, and (c) Optimized diet

[0152] Among the microbial taxa available in the sample belonging to T2, the taxa mainly responsible for pyruvate utilization are identified and stored in TAXA_SET_P. In one embodiment, the taxa responsible for pyruvate utilization can be obtained from GMP or FGmap, whereby each taxa in the microbial taxa abundance input of T2 is mapped to the GMP and FGMap and checked whether the value corresponding to this taxon in the pyruvate utilization pathway is 1. The first neighbors of TAXA_SET_P in TAXA_NET are identified and stored in a new list TAXA_SET_FN. First neighbors refer to the set of nodes in TAXA_NET that are directly connected to the set of nodes belonging to TAXA_SET_P. The first neighbors of a query microbial node in the microbial association network essentially indicate a list of other microbial nodes that are the closest related in the microbial community and have a direct influence on the query node. The functional potential of the taxa in TAXA_SET_FN is predicted, and in one embodiment, facultative taxa are identified based on their ability to produce butyrate using the pyruvate utilization pathway. In other embodiments, biosynthetic pathways of useful metabolites described in GMPs, FGmaps or literature can also be used. The functional information can be obtained from the GMPs and FGmaps described in the present invention, which allows each taxon in TAXA_NET to be associated with a function based on genus-function or genome function mapping described in these matrices. Further functional information can be added to each taxon based on automated literature mining or by manual curation. Save a subset of these taxa from TAXA_SET_FN as TAXA_SET_FN_COM. Identify a set of taxa that are common between TAXA_SET_FN_COM and all taxa present in T2. ​​Save this list as TAXA_SET_INTERSECT. Save the remaining taxa included in TAXA_SET_FN_COM that are not present in T2 as TAXA_SET_NEW.

[0153] Possible individualized treatment options are suggested below. (a) Recommendation-1: We suggest the administration of one or more prebiotics to promote the growth of TAXA_SET_INTERSECT and TAXA_SET_P. (b) Recommendation-2: Propose one or more microbial strains belonging to TAXA_SET_NEW as candidates for the probiotic cocktail. Based on the fact that the population synthetic potential of microorganisms is responsible for shaping the interaction profile, strains that are more likely to complete the metabolic pathways required for the community, as revealed by their predicted functional potential, can be used as a measure to optimize the cocktail microbial components. Given a set of microbial strains belonging to TAXA_SET_NEW as candidates, a subset of the same strains can be obtained that can help to increase the population synthetic potential. For this purpose, methods such as Netcooperate (https: / / doi.org / 10.1186 / s12859-015-0588-y) can be used to calculate the metabolic complementarity of pairs of microorganisms. This can be done for all possible unique pairwise combinations between taxa identified in TAXA_SET_NEW (as the first part of the pair) and TAXA_SET_INTERSECT (as the second part of the pair). In the next step, taxa identified in matching pairs in TAXA_SET_NEW are included as the final probiotic cocktail TAXA_SET_NEW_OPTIMIZED. The identification of TAXA_SET_NEW_OPTIMIZED is optional. If TAXA_SET_NEW_OPTIMIZED is identified, TAXA_SET_NEW will be replaced by TAXA_SET_NEW_OPTIMIZED and all references to TAXA_SET_NEW will be the same as TAXA_SET_NEW_OPTIMIZED. (c) Recommendation-3: In addition to recommendation 2, we suggest the administration of one or more prebiotics that promote the growth of TAXA_SET_INTERSECT, TAXA_SET_P, and TAXA_SET_NEW. (d) Recommendation-4: Obtain a list of food ingredients used by the sets of taxa in TAXA_SET_INTERSECT and TAXA_SET_P using TAXA_CONSTITUENT_NET and save it in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a collection of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and save it as OPTIMIZED_FOOD_SET. Use OPTIMIZED_FOOD_SET to recommend personalized diets to subjects identified with deteriorating gut health at time point T2. (e) Recommendation 5: Combining Recommendation 2 and Recommendation 4, we suggest the administration of probiotics as well as foods necessary to maintain the stability of the gut microbial community structure. (f) Recommendation-6: Following recommendation-2, suggest diets to maintain existing communities and add new probiotics. Use TAXA_CONSTITUENT_NET to obtain the list of food ingredients used by taxa in TAXA_SET_INTERSECT, TAXA_SET_NEW and TAXA_SET_P and save them in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and save it as OPTIMIZED_FOOD_SET. Use OPTIMIZED_FOOD_SET to recommend personalized diets for subjects identified with deteriorating gut health at time point T2.

[0154] Individualized therapeutic intervention 2 - for increased pathway abundance of multiple harmful microbes - is recommended: Individualized therapeutic recommendations include optimized diet.

[0155] Among the microbial taxa available in the samples belonging to T2, identify those mainly involved in ammonia production and store them in TAXA_SET_A. Identify the set of commensal bacteria in the samples belonging to T2 and store it in the list T2_COM.

[0156] We can recommend a personalized treatment option for you: (a) Recommendation-1: Use TAXA_CONSTITUENT_NET to obtain a list of food ingredients used by taxa in TAXA_SET_A and store it in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET) that contain ingredients identified in CONSTITUENT_SET. Suggest that subjects with deteriorating gut health at time T2 reduce or stop consuming foods from FOOD_SET. (b) Recommendation-2: In addition to Recommendation-1, use TAXA_CONSTITUENT_NET to obtain a list of food ingredients used by taxa in T2_COM and save the list in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and save it as OPTIMIZED_FOOD_SET. Use OPTIMIZED_FOOD_SET to recommend personalized diets to subjects identified as having poor gut health at time point T2.

[0157] When pathway abundance of multiple beneficial microorganisms is decreased and pathway abundance of multiple harmful microorganisms is increased.

[0158] Individualized treatment recommendations include an optimized diet: (a) Prebiotic cocktail, (b) probiotic cocktail, and (c) optimized diet

[0159] Among the microbial taxa available in the samples belonging to T2, identify those primarily responsible for pyruvate utilization and save them in TAXA_SET_P. In the next step, among the microbial taxa available in the samples belonging to T2, identify those primarily responsible for ammonia production and save them in TAXA_SET_A. Identify the first neighbors of TAXA_SET_P in TAXA_NET and save them in a new list TAXA_SET_FN. Predict the functional potential of the taxa in TAXA_SET_FN, in one embodiment identifying facultative taxa based on butyrate production potential. Save a subset of these taxa from TAXA_SET_FN as TAXA_SET_FN_COM. Identify the set of taxa that are common between TAXA_SET_FN_COM and the set of all taxa present in T2. ​​Save this list as TAXA_SET_INTERSECT. Save the remaining taxa in TAXA_SET_FN_COM that are not present in T2 as TAXA_SET_NEW. Identify the set of resident bacteria in the sample that belong to T2 and store them in a list T2_COM. In one embodiment, this can be done by evaluating the pyruvate utilization ability of the constituent microorganisms.

[0160] We can recommend a personalized treatment option for you: (a) Recommendation-1: We suggest the administration of one or more prebiotics that promote the growth of TAXA_SET_INTERSECT and TAXA_SET_P. (b) Recommendation-2: One or more microbial strains belonging to TAXA_SET_NEW are proposed as candidates for probiotic cocktail. The final probiotic cocktail can then be determined by identifying strains that are likely to complete the metabolic pathways required by the individual microorganisms and communities. Such cocktail designs can include multiple combinations. The final combination can be determined by co-cultivating the microbial cocktail with the required nutrient sources in a laboratory mimicking the human gut environment. (c) Recommendation-3: In addition to recommendation 2, we suggest the administration of one or more prebiotics that promote the growth of TAXA_SET_INTERSECT, TAXA_SET_P, and TAXA_SET_NEW. (d) Recommendation-4: Obtain a list of food ingredients used by the sets of taxa in TAXA_SET_INTERSECT and TAXA_SET_P using TAXA_CONSTITUENT_NET and save it in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a collection of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and save it as OPTIMIZED_FOOD_SET. Use OPTIMIZED_FOOD_SET to recommend personalized diets to subjects identified with deteriorating gut health at time point T2. (e) Recommendation 5: Combining Recommendation 2 and Recommendation 4, we suggest the administration of probiotics as well as foods necessary to maintain the stability of the gut microbial community structure. (f) Recommendation-6: Following recommendation-2, suggest diets to maintain existing communities and add new probiotics. Use TAXA_CONSTITUENT_NET to obtain the list of food ingredients used by taxa in TAXA_SET_INTERSECT, TAXA_SET_NEW and TAXA_SET_P and save them in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and save it as OPTIMIZED_FOOD_SET. Use OPTIMIZED_FOOD_SET to recommend personalized diets for subjects identified with deteriorating gut health at time point T2. (g) Recommendation-7: Following recommendation-2, suggest diets to maintain existing communities and add new probiotics. Obtain a list of food ingredients used by taxa in TAXA_SET_INTERSECT, TAXA_SET_NEW, and TAXA_SET_P using TAXA_CONSTITUENT_NET and store in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET) that hold the ingredients identified in CONSTITUENT_SET. Optimize the food FOOD_SET based on personalized preferences and dietary requirements and store as OPTIMIZED_FOOD_SET. Similarly, use TAXA_CONSTITUENT_NET to obtain a list of food ingredients used by taxa in TAXA_SET_A and store that list in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET_A) that contain the ingredients identified in CONSTITUENT_SET. For subjects identified as having a worsening gut health at time T2, suggest reducing or stopping the intake of foods from FOOD_SET_A. (h) Recommendation-8: Following recommendation-4, use TAXA_CONSTITUENT_NET to obtain a list of food ingredients used by taxa in TAXA_SET_A and store the list in CONSTITUENT_SET. Use FOOD_CONSTITUENT_NET to find a set of foods (FOOD_SET_A) that contain ingredients identified in CONSTITUENT_SET. Suggest that subjects with deteriorating gut health at time T2 reduce or stop consuming foods from FOOD_SET_A.

[0161] The optimized diet is recommended based on an optimization technique, which includes optimizing the gut food score to obtain the optimized diet, the optimization being subject to multiple constraint parameters including calorie parameters, carbohydrate parameters, protein parameters, and fat parameters. In one embodiment, the optimization of the different food items is performed such that food items with a high gut food score are recommended and items with a low gut food score are avoided. The gut food score (GFS) is a quantified individualized score that indicates whether a food item specifically increases the abundance of resident or beneficial bacterial taxa and does not cause significant changes in the abundance of harmful or pathogenic or potentially pathogenic bacterial taxa in an individual, given the changes in microbiome composition between two samples. The gut food score of a particular food is calculated using the properties of its constituents.

[0162] Compounds contained in foods are utilized as an energy source by bacterial taxa, which then proliferate and, as a result, their abundance increases. If the increased taxa are resident, the gut environment improves, whereas if the increased taxa are pathogenic, the gut environment worsens. Thus, consuming certain foods that contain compounds that are specifically utilized by resident bacterial taxa is beneficial for gut health. Conversely, refraining from foods that may be utilized by pathogenic bacteria is also beneficial for health. Thus, if a certain pathogenic bacteria increases, foods containing compounds utilized by that pathogenic bacteria should be particularly avoided. Conversely, if the abundance of a certain resident bacteria decreases, foods containing compounds that can be utilized by resident bacteria should be particularly eaten to restore gut health. To quantify or score foods based on the utilization profile of their constituent compounds by resident and pathogenic bacteria, and on the increase of pathogenic taxa and the decrease of resident bacterial taxa, we calculate the Gut Food Score (GFS). The GFS score is individualized and every person / patient will have a different score based on the changes in their microbiome profile in healthy and disease states. Foods with a high GFS score should be consumed and foods with a low (-ve) score should be avoided.

[0163] The first requirement of the disclosed steps is a list of compounds found in various foods. Let such a list of compounds be represented as follows: TIFF0007634701000009.tif590 where p is the total number of such compounds.

[0164] In one embodiment, the compound list can be obtained from different published food ingredient databases or tables.

[0165] The second condition is the food list: TIFF0007634701000010.tif451 where m is the total number of such foods.

[0166] Such food lists can be obtained from various publicly available food ingredient databases and tables. The ingredients and their contents in these foods must be known. In one embodiment, the ingredients in the foods can be obtained from publicly available databases. In another embodiment, the ingredients in the foods can be obtained by literature mining techniques and manual curation.

[0167] The third requirement is the food ingredient matrix (C). TIFF0007634701000011.tif415, Where: p = total number of compounds obtained in the food from the list foods, and m = total number of foods. In one embodiment, a food component matrix (similar to the matrix format of FOOD_CONSTITUENT_NET) is created from the literature or from multiple publicly available food component databases or tables. Matrix C can be obtained from various publicly available food composition databases and literature and is defined in Table 8 below: [Table 8]

[0168] The fourth requirement is multiple vectors using data available in public databases and scientific literature: TIFF0007634701000013.tif465 is a vector containing the carbohydrate content of foods. TIFF0007634701000014.tif49= is the carbohydrate content per unit of. TIFF0007634701000015.tif49- This is taken from existing literature. TIFF0007634701000016.tif471 is a vector containing the protein content of foods, looking like this: TIFF0007634701000017.tif412=Units TIFF0007634701000018.tif49.This can be obtained from existing literature. TIFF0007634701000019.tif453 is a vector containing the fat content of foods, looking like this: TIFF0007634701000020.tif46= Fat content per unit TIFF0007634701000021.tif49.This can be obtained from the published literature. This is a vector containing TIFF0007634701000022.tif485. This is a vector containing TIFF0007634701000023.tif411. TIFF0007634701000024.tif412= Content per unit of TIFF0007634701000025.tif411. TIFF0007634701000026.tif49.This can be obtained from the published literature.

[0169] The fifth requirement is to have a list of taxa. The list of taxa should be: TIFF0007634701000027.tif549Here q is the total number of taxa.

[0170] In one embodiment, the list of taxa can be only those that have either beneficial or harmful metabolic pathways, as defined earlier in this disclosure.

[0171] The fifth requirement is two functions that can examine the genetic functional potential of different bacterial taxa and determine whether they are facultative (also called beneficial) or pathogenic (also called harmful). TIFF0007634701000028.tif766 TIFF0007634701000029.tif762

[0001] In one embodiment, the function can identify resident bacteria as taxa that have functional capabilities or genes / enzymes / proteins that correspond to beneficial pathways as described in this disclosure, and identify pathogenic bacteria as taxa that have functional capabilities or genes / enzymes / proteins that correspond to harmful pathways as described in this disclosure. In such an embodiment, resident bacteria(x) is a function that essentially tests for genes present in taxon x and returns "true" if the genes code for proteins that correspond to beneficial pathways as described in this disclosure. Meanwhile, pathogens(x) is a function that essentially tests for genes present in taxon x and returns "true" if the genes code for proteins that correspond to harmful pathways as described in this disclosure. PU is defined as a set that includes taxa that belong to the facultative taxon list. TIFF0007634701000030.tif471 An AP is defined as a collection that includes taxa that belong to the list of pathogenic taxa. TIFF0007634701000031.tif470

[0172] In one embodiment, all first neighbor bacterial taxa that are positively associated (have direct edges) with the elements / bacterial taxa of set PU in TAXA_NET can also be added to set PU. Additionally, all bacterial taxa that are positively correlated with the elements of set AP in TAXA_NET can also be added to set AP. In one embodiment, PU can be TAXA_SET_P and AP can be TAXA_SET_A as disclosed above.

[0173] The sixth requirement is a Taxa-Compound Utilization matrix (which is essentially identical to the matrix format of TAXA_CONSTITUENT_NET), which is a matrix containing compounds that can be utilized as energy sources by different bacterial taxa. TIFF0007634701000032.tif797 Where: p = total number of compounds q = total number of taxa.

[0174] In one embodiment, such a matrix can be obtained using functional potential estimation based on the various genes present in the bacterial taxon, where functional potential refers to the set of metabolic pathways encoded within the taxon that are useful for utilizing a set of food components, hi another embodiment, the table can be obtained from publicly available repositories such as Virtual Metabolic Human.

[0175] The process of recommending an optimized diet is described below with the flow diagram in Figure 5:

[0176] In step 502 of method (500), normalized changes are calculated for all taxa in the union of taxon set 1 and taxon set 2 between timestamp 1 (or time point 1) and timestamp 2 (or time point 2).

[0177] In an embodiment, a deteriorating gut environment refers to either a decrease in a combination of beneficial metabolic pathways and their constituent metabolites in the gut microbiota, or an increase in harmful metabolic pathways and their constituent metabolites. This is considered to refer to an abnormality in an individual's biological state between samples at timestamps T1 and T2. The first time point (T1) is a pre-perturbation state, and the second time point (T2) is a post-perturbation state, where perturbation has caused dysbiosis of the gut microbial community structure. At time point T2, a subject with a deteriorating gut environment relative to T1 is identified. From the input microbial taxon abundance file of the subject's microbiome corresponding to T2 relative to T1, the reason for dysbiosis in terms of functional potential is identified.

[0178] In one embodiment, the normalized change, represented as a vector d, is calculated for all taxa, denoted by taxa. TIFF0007634701000033.tif410 (q is the total number of related taxa) is For all taxa shown in TIFF0007634701000034.tif48, TIFF0007634701000035.tif45 shows the T2 and T1 in the gut microbiota samples as shown below. Between TIFF0007634701000036.tif49 Calculated to capture the change in abundance of TIFF0007634701000037.tif48: TIFF0007634701000038.tif850 Where: TIFF0007634701000039.tif45 is sample T2 Abundance of TIFF0007634701000040.tif48. TIFF0007634701000041.tif45 is sample T1 Abundance of TIFF0007634701000042.tif49. TIFF0007634701000043.tif45 shows the results of the analysis of different gut microbiota samples from healthy individuals. The standard deviation of abundance of TIFF0007634701000044.tif48.

[0179] In one embodiment, human gut microbiome samples can be obtained from different publicly available datasets containing microbial loads of multiple healthy humans.

[0180] In step 504 of method (500), a taxon importance score is calculated for every taxon in the combination of taxon set 1 and taxon set 2. The taxon importance score is calculated using the normalized variance, a number of beneficial microorganisms, and a number of harmful microorganisms from the knowledge base set.

[0181] Taxon importance vector All taxa (taxa i ) About: TIFF0007634701000046.tif5105 Where: b i is a taxonomic group i ) is the importance score of d i is a taxonomic group i ) is the normalized change score of I is the indicator function, i.e., TIFF0007634701000047.tif733 That is, for any set Z and any variable y which may or may not be in Z, PU: Set of resident bacterial taxa AP: collection of pathogenic bacterial taxa

[0182] Due to the definition of vector b, if a pathogenic bacterial taxon has increased significantly in T2 (relative to its abundance and standard deviation in healthy conditions), it will receive a high score, and if it has increased less significantly (relative to its abundance and standard deviation in healthy conditions), it will receive a low score. On the other hand, if a commensal bacterial taxon has decreased significantly in T2 (relative to its abundance and standard deviation in healthy conditions), its importance will be high, otherwise it will be 1. If a taxon belongs to both commensal and pathogenic bacteria or neither, its score will be 1.

[0183] In step 506 of method (500), a personalized gut compound score is calculated using the importance scores of all taxa in the combination of taxon set 1 and taxon set 2 and the predefined taxon-compound utilization matrix (T). The personalized gut compound score quantifies the ability of a compound to increase multiple beneficial microorganisms in the gut.

[0184] In an embodiment, the GCS score is calculated for all p compounds present in the list of compounds as follows: TIFF0007634701000048.tif455 is a vector containing the intestinal compound scores of the different compounds. TIFF0007634701000049.tif438The intestinal composite score is defined as: TIFF0007634701000050.tif1185 Where: I is the indicator function.

[0185] The gut compound score for a particular compound indicates the importance of the various compounds in the optimization process. The first element of the equation quantifies the ability of the compound to increase resident gut microbial taxa. If the compound is utilized by resident bacterial taxa that were significantly reduced in T2, it will receive a higher score in the first component of the equation. If the compound is utilized by other resident bacteria that were not significantly reduced in T2, the compound will receive a lower score, bounded by 1.

[0186] Conversely, the second component of the equation quantifies the compound's ability to reduce pathogenic bacterial taxa. If the compound is utilized by the pathogenic bacterial taxon that increases in abundance at T2, it will receive a high score in the second component of the equation. If the compound is utilized by other pathogenic bacteria, the compound will receive a low score, with a floor of 1. If the compound is not utilized by pathogenic bacteria, the compound will receive a score of 0.

[0187] To summarize the above two points, the Gut Compound Score of a particular compound quantifies whether the compound is utilized as an energy source by resident bacterial taxa (if utilized by resident bacterial taxa decreased in T2, the score is high) and not utilized by pathogenic bacterial taxa (if utilized by pathogenic bacterial taxa increased in T2, the score is low). In other words, compounds with a high (positive) GCS selectively increase the abundance of resident bacteria but not pathogenic bacteria. Conversely, compounds with a low (negative) GCS score selectively increase the abundance of pathogenic bacteria but not resident bacteria. The GCS is a vector that contains the Gut Compound Scores of all compounds in the list.

[0188] In step 508 of the method (500), the gut composite score and the food component matrix are used to calculate a gut food score (GFS) for every food item in the set of food items. The GFS quantifies the ability of a food item to increase multiple beneficial microorganisms.

[0189] In one embodiment, the GFS for a particular food is the weighted sum of the gut compound scores of the food's constituent compounds weighted by their concentration, i.e., the amount of compound per unit of food. gfs is a vector containing the gut food scores of all foods in the list of foods. TIFF0007634701000051.tif453 is a vector containing the enteric food scores for m items. TIFF0007634701000052.tif586 TIFF0007634701000053.tif1127

[0190] To calculate the GFS for a particular food, multiply the GCS score of each constituent compound by its content in the food and sum the results. Interpretation of the GFS: Foods with a high GFS indicate that they may be utilized by resident bacterial taxa, resulting in proliferation of resident bacterial taxa, and a higher score indicates that they may be utilized by resident bacteria that are reduced in disease states, potentially improving gut health. This personalized Gut Food Score can help you find your optimal diet.

[0191] In step 510 of the method (500), the diet is optimized such that the gutfood score of all optimized food items is maximized, subject to multiple constraint parameters including a calorie parameter, a carbohydrate parameter, a protein parameter, and a fat parameter.

[0192] In one embodiment, a diet optimized to meet daily caloric and macronutrient requirements as well as be specifically utilized by resident genera, but not pathogenic genera, is calculated based on the gut food score-based optimization problem: TIFF0007634701000054.tif1045 Subject to the following constraints: TIFF0007634701000055.tif1069 TIFF0007634701000056.tif1060 TIFF0007634701000057.tif1067 TIFF0007634701000058.tif1050 TIFF0007634701000059.tif419 Where: The user has the option to choose from a list of m foods. TIFF0007634701000060.tif466 is a list of all 'm' foods, TIFF0007634701000061.tif465 is a vector containing the carbohydrate content of foods. TIFF0007634701000062.tif49= is the carbohydrate content per unit of. TIFF0007634701000063.tif49- This is taken from existing literature. TIFF0007634701000064.tif471 is a vector containing the protein content of foods, looking like this: TIFF0007634701000065.tif412=Units TIFF0007634701000066.tif49.This can be obtained from existing literature. TIFF0007634701000067.tif453 is a vector containing the fat content of foods, looking like this: TIFF0007634701000068.tif46= Fat content per unit TIFF0007634701000069.tif49.This can be obtained from the published literature. This is a vector containing TIFF0007634701000070.tif485. This is a vector containing TIFF0007634701000071.tif411. TIFF0007634701000072.tif412= Content per unit of TIFF0007634701000073.tif411. TIFF0007634701000074.tif49.This can be obtained from the published literature. TIFF0007634701000075.tif439 is a vector like this: TIFF0007634701000076.tif442 and TIFF0007634701000077.tif738

[0193] A user may select some foods from the list foods based on preferences. In one embodiment, these preferences may be based on dietary preferences (including taste, allergies to certain foods, religious preferences, etc.), availability of foods in a geographic location, etc. This does not necessarily exclude other factors that may affect preferences for certain foods. This user food selection may be assisted by a computing system, such as a website, a mobile application, etc. TIFF0007634701000078.tif442 is a vector containing the amount of food, and x i teeth The amount of TIFF0007634701000079.tif49. TIFF0007634701000080.tif445 is a vector containing the optimized production of food by solving a constrained optimization problem. TIFF0007634701000081.tif454 is a vector containing the individualized gut food scores given to m foods. TIFF0007634701000082.tif47 is the intestinal food score of a specific person Quantify TIFF0007634701000083.tif49. TIFF0007634701000084.tif4109 are the minimum and maximum values ​​for the user's carbohydrate, protein, fat and total calorie intake, which can be provided by the user (assuming they have a fair idea of ​​what constitutes a good diet) or selected from several pre-set standard values ​​based on the end user's characteristics, including but not limited to age, height and weight. The selection of these values ​​can also be made empirically by an expert on behalf of the end user. This selection can also be assisted, in part or in whole, by a computational system, e.g. a website, mobile application, etc.

[0194] In one embodiment, the constrained optimization problem can be solved manually or using computational tools using any optimization technique, including but not limited to linear programming. Recommended food portions TIFF0007634701000085.tif466, where TIFF0007634701000086.tif48 represents the element-wise product between vectors.

[0195] The vector r is the final food recommendation, and r i Food i For all foods not selected by the user it will have amount 0, for foods selected by the user it will have value 0 or +ve depending on the result of the optimization step.

[0196] In another embodiment, one or more beneficial metabolic product production pathways or / and one or more harmful metabolic product degradation / elimination pathways can be engineered or bioengineered into the genetic makeup of gut microbes. These engineered microbes can be administered as probiotics, alone or in combination with prebiotics / metabiotics / paraprobiotics, to individuals with gut disease.

[0197] In one embodiment, the disclosure provides modified or engineered microorganisms, where the organism heterologously expresses one or more of the pathways required to produce a metabolic product. A "modified" microorganism refers to a microorganism capable of surviving in the gut as a resident or symbiont, whose naturally occurring form has been altered, such as by being engineered to heterologously express a metabolic pathway. A "heterologous" protein or pathway refers to a gene or set of genes that form a pathway that is not normally encoded in the genome of the microorganism. Thus, heterologous production of a pathway-forming protein involves introducing into a microorganism a DNA sequence that encodes a gene / set of genes corresponding to a particular pathway, together with a promoter and terminator sequence operable in the microorganism. The pathway-forming protein can be expressed using an appropriate expression vector or other construct introduced into the microorganism. In an embodiment, a heterologous pathway is introduced into the modified organism encoded by a plasmid or a DNA segment introduced into the bacterial chromosome.

[0198] In an embodiment, the present disclosure includes modified bacteria, including one or more of the resident or commensal bacteria known in the art.In one embodiment, the modified bacteria may include species of genera such as Lactobacillus and Bifidobacterium that can reside in the intestine.Many members of these genera are active in foods intended for human and animal consumption, including dairy products such as yogurt, milk, milk-based cream, ice cream products, cheese, or combinations thereof.In certain embodiments, the modified bacteria may be one or more of Lactobacillus plantarum, Lactobacillus casei, Lactobacillus acidophilus, Lactobacillus salivarius, or L. Reuteri, and probiotic strains of Bifidobacterium, such as B. longum.In another embodiment, the modified organism may include facultative anaerobes that can reside in the intestine. In one embodiment, commercially available probiotic strains including Clostridium butyricum and Streptococcus thermophilus can also be used as modified organisms. Other indigenous / commensal organisms in the gut are also within the realm of engineering to express desired pathways.

[0199] In another aspect, the present disclosure can synthesize food products that contain modified bacteria that express heterologous pathways. In another aspect, food products can be composed of the personalized probiotic strains identified in the present invention that have the pathways necessary for improving the gut health of individuals. In an embodiment, the food product is a dairy product, including, but not necessarily limited to, yogurt, milk, milk-based cream, and cheese or chocolate. The manufacture of food products that contain live cultures of microorganisms, such as yogurt, is well known in the art. These can be adapted to contain the modified microorganisms currently provided or the microbial consortium identified in the present invention. The packaging of the food product can provide information to the consumer that identifies the presence of the modified strain or other strains in the food product.

[0200] In another embodiment, the present disclosure includes supplement products such as dietary supplements, nutraceuticals, food ingredients, etc., including but not limited to probiotic formulations or functional foods that include one or more of the live bacteria modified or naturally occurring as described in this disclosure. The supplement products may be available as liquids, capsules, tablets, softgels, powders, etc.

[0201] In another aspect, the present disclosure provides a pharmaceutical composition comprising said modified microorganism or a probiotic formulation composition identified in the present disclosure. The pharmaceutical composition may include any suitable diluent, carrier, excipient, buffer, etc., intended for use with the microorganism present in the formulation. Administration of the pharmaceutical formulation may be by one or more suitable routes, including but not limited to parenteral, intraperitoneal, intrapulmonary, oral, intraperitoneal, etc. Parenteral infusion may include one or more of intramuscular, intravenous, intraarterial, intraperitoneal, and subcutaneous administration.

[0202] The personalized therapeutic intervention further includes administering genetically engineered bacteria, the genetically engineered bacteria including (a) multiple microbial pathways for the production of one or more beneficial metabolites, or / and (b) multiple microbial pathways for the degradation / elimination of one or more harmful metabolites, engineered into the genetic makeup of the gut microbes, the genetically engineered bacteria being administered in one of the following shared methods: (a) genetically engineered probiotics, used alone or in combination with prebiotics, metabiotics, or parabiotics; (b) A genetically engineered organism that heterologously expresses one or more of the pathways necessary to produce a metabolite, wherein the genetically engineered organism may be one or more of the indigenous gut bacteria, wherein the indigenous gut bacteria may include, but are not limited to, beneficial microorganisms, commercially available probiotic strains, or facultative anaerobes that may reside in the gut; wherein the genetically engineered organism is also referred to as a modified bacterium, wherein the modified bacterium may be administered as a food, a food supplement, or a pharmaceutical composition or probiotic formulation.

[0203] The subject matter is described herein so that anyone skilled in the art can make and use the embodiments. The scope of the subject matter embodiments is defined by the claims, and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be included within the scope of the claims if they have similar elements that do not differ from the wording of the claims, or if they contain equivalent elements that do not substantially differ from the wording of the claims.

[0204] The embodiments of the present disclosure herein provide a solution to address the problem of designing a personalized therapeutic intervention for an individual based on the function of the microbiome. Each individual has a unique composition of the microbiome in the gut, and one probiotic or dietary therapy may not have the same effectiveness in different individuals. The present disclosure recommends a personalized therapeutic intervention for improving the gut health of an individual by designing personalized therapeutics and dietary therapy based on the function of the microbiome. The personalized therapeutic intervention is recommended based on several steps including generating a set of knowledge bases, identifying changes in the gut health of the individual by monitoring gut samples, and recommending the personalized therapeutic intervention. The personalized therapeutic intervention includes at least one of prebiotics, probiotics, and an optimized diet, the optimized diet is estimated based on the optimization of the gut food score, and the gut food score is calculated based on the changes in the gut health of the individual.

[0205] It should be understood that the scope of protection extends to computer readable means having messages therein as well as such programs. Such computer readable storage means includes program code means for performing one or more steps of the method when the program is executed on a server or a mobile device or any suitable programmable device. The hardware device may be any type of programmable device, including any type of computer, such as a server or a personal computer, or any combination thereof. The device may also include hardware means, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or a combination of hardware and software means, such as an ASIC and an FPGA, or at least one microprocessor and at least one memory having software processing components disposed therein. Thus, the means may include both hardware means and software means. Method embodiments described herein may be implemented in hardware and software. The apparatus may also include software means. Alternatively, the embodiments may be implemented using different hardware apparatus, such as multiple CPUs.

[0206] The embodiments herein may be composed of hardware and software elements. Software-implemented embodiments include, but are not limited to, firmware, resident software, microcode, etc. The functions performed by the various components described herein may be implemented with other components or combinations of other components. In this specification, a computer-usable medium or computer-readable medium may be any device that can configure, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0207] The illustrated steps are set forth to illustrate the illustrated exemplary embodiments, and it should be expected that continuing technological developments will result in changes in the manner in which certain functions are performed. These examples are presented herein for purposes of explanation, and not limitation. Moreover, the boundaries of the functional building blocks are arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc. of those described herein) will be apparent to those skilled in the relevant art based on the teachings contained herein. Such alternatives are within the scope of the disclosed embodiments. Additionally, the words "comprising," "having," "containing," and "including," as well as other similar forms, are intended to be equivalent in meaning and open-ended in that the items or items following any one of these words are not meant to be an exhaustive list of such items or items, or to be limited to only the listed items or items. It should also be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0208] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory in which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to execute steps or stages consistent with the embodiments described herein. The term "computer-readable medium" should be understood to include tangible objects and exclude carrier waves and transient signals, i.e., non-transient. For example, random access memory (RAM), read-only memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage mediums are included.

[0209] It is intended that the disclosure and examples be considered as exemplary only, with the true scope of the disclosed embodiments being indicated by the following claims. In relation to the present invention, the following is further disclosed: [1] Methods for designing personalized therapeutics and dietary treatments based on microbiome function (200), including: Receiving (202) a set of input data via one or more hardware processors, the set of input data including data relating to: Multiple metabolites, multiple metabolic pathways, multiple taxa, nutritional information, probiotics, prebiotics, supplements for the replacement or degradation of multiple metabolites, multiple food components, multiple food items, microbial abundance matrices of healthy cohorts, multiple dietary optimization prerequisite parameters, wherein the plurality of metabolites includes a set of beneficial metabolites and a set of harmful metabolites, the set of beneficial metabolites being beneficial to the intestinal health of the individual, and the set of harmful metabolites being harmful to the intestinal health of the individual; wherein the multiple metabolic pathways include either (a) beneficial pathways or (b) harmful pathways; wherein the beneficial pathway comprises one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites; and wherein the harmful pathway comprises one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of harmful metabolites; wherein a plurality of beneficial microorganisms possess beneficial pathways and a plurality of harmful microorganisms possess harmful pathways; wherein the plurality of taxa comprises a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, wherein the plurality of microorganisms comprises a set of a plurality of genes having distinct genomic locations, a nucleotide sequence and a protein sequence of the bacterial strain; and wherein the plurality of dietary optimization prerequisite parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix, the food component matrix tabulating the contents of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a food component matrix, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters relating to carbohydrate, protein, fat and calories, and a user-selected food; generating a set of knowledge bases using the set of input data (204) via one or more hardware processors, the set of knowledge bases including a genus-functional map (FGmap), a genome-metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap, the generation of the set of knowledge bases including the steps of: generating an FGmap and a GMP map, where the FGmap is a matrix with a plurality of bacterial genera belonging to one of a plurality of taxa and a plurality of metabolic pathways as columns, where a first predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway, and the GMP is a matrix with a plurality of bacterial strains as rows and a plurality of metabolic pathways as columns, where a second predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway (204A); generating a TAXA_NET graph based on the plurality of taxa and the FGmap (wherein the TAXA_NET graph is an undirected graph), where each node of the TAXA_NET graph corresponds to a taxon from the plurality of taxa and edges indicate positive relationships between pairs of taxa, where each taxon from the plurality of nodes is associated with a function of the plurality of taxa from the FGmap, the positive relationships including one of mutualism, mutualism, facultativeism, and cooperation (204B); generate a TAXA-food component network based on multiple food components for each taxon in TAXA_NET, where the TAXA-food component network is an undirected network of each taxon associated with food components used by the taxon as metabolic or nutrient sources (204C); Generate a food component network based on the food items of each food component in the TAXA-food component network, where the food component network is an undirected network of each food component present in the food item (204D); and generating a transpose of the GMP to obtain a ProbMap, where the ProbMap is a matrix with rows containing multiple metabolites and each metabolite from multiple metabolic pathways, and columns containing multiple bacterial strains, probiotics, prebiotics, and dietary components capable of biosynthesizing and / or degrading the corresponding multiple metabolites (204E); receiving, via one or more hardware processors, a plurality of intestinal samples from the individual at two timestamps, the two timestamps including timestamp 1 and timestamp 2 at which the two samples were collected (206); generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including taxon set 1 and taxon set 2, the taxon set 1 and the taxon set 2 being generated using the timestamp 1 and the timestamp 2 (208); generating, via one or more hardware processors, pathway abundances for each of a plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of a plurality of metabolic pathways for GMP in the gut microbiome of the individual at the corresponding timestamp (210); Identifying 212 a perturbed pathway, the perturbed pathway indicative of impaired gut health of the individual, the perturbed pathway being identified based on a comparison of pathway abundances of the multiple gut samples obtained at timestamp 1 and timestamp 2 using statistical methods, the perturbed pathway being identified based on any of the following: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease indicates a decrease in a set of beneficial microorganisms, or (b) Case B: An increase in pathway abundance of a population of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of the beneficial pathway group was decreased and the pathway abundance of the harmful pathway group was increased, suggesting an increase in harmful microbial groups and a decrease in beneficial microbial groups; determining an initial set of personalized therapeutics and dietary therapies using a ProbMap, the initial set of personalized therapeutics and dietary therapies including at least one of the following: a dietary regimen, the initial set of personalized therapeutics and dietary therapies being rows corresponding to the identified perturbed pathways, where the personalized therapeutics are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having a pathway for degradation or / and biosynthesis of a high probability metabolite, the high probability metabolite being a metabolite corresponding to the perturbed pathway in the ProbMap (214); identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, where the taxon set is comprised of TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes predefined first values ​​for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes predefined second values ​​for one or more of the harmful pathways in one of the FGmap and the GMP (216); identifying a set of first neighbors from the TAXA_NET graph for each tax element in TAXA_SET_P at timestamp 2, the set of first neighbors being direct neighbors of TAXA_SET_P including TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW_ (218); and recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving gut health of the individual (220), the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and an initial set of personalized therapeutic and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) For case A (reduced pathway abundance of a group of beneficial microorganisms), we recommend a prebiotic cocktail, a probiotic cocktail, and an optimized diet, which can replenish multiple beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of harmful microbial populations), recommending an optimized diet, which promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; (c) For case C (reduced pathway abundance of beneficial microbial populations and increased pathway abundance of harmful microbial populations), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, the probiotic cocktail and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms and suppress the growth of harmful microorganisms. [2] The method of claim 1, wherein the plurality of beneficial metabolic pathways may include one or more of the following: pyruvate to butyrate production via a pyruvate pathway, an ammonia oxidation pathway, and a pathway for trimethylamine oxide (TMA) decomposition, and the plurality of harmful metabolic pathways may include, but are not limited to, one or more of ammonia release pathways, biogenic amine production, and trimethylamine production. [3] The generation of the GMP and FGmap (300) is a processor-implemented method as described in [1], comprising: generating a matrix metabolite map (Mmap) based on the list of the plurality of metabolites, the plurality of metabolic pathways, and organisms associated with each of the plurality of metabolic pathways, where a Mmap is generated for each of the plurality of metabolites (302); generating a bacterial genome map (BGM) using a bacterial genome database (BGD), the BGD comprising a comprehensive list of the plurality of bacterial strains, the BGM comprising information relating to the plurality of bacterial strains including a plurality of names of the plurality of bacterial strains, a plurality of identification numbers of the plurality of bacterial strains, a plurality of gene locations of the plurality of bacterial strains, and a plurality of protein domains of the plurality of bacterial strains (304); Based on Mmap and BGM, we generated a metabolite pathway domain map (MPDM) for each metabolite from multiple metabolites, where the MPDM contains an exhaustive list of protein domains associated with each of multiple metabolic pathways ( 306 ); Using MPDM and BGM to identify the presence of multiple metabolic pathways in a list of bacterial strains and genomes based on a predefined first threshold criterion ( 308 ); generating a GMP using the BGM, the MPDM, the multiple metabolic pathways, and the multiple metabolites, the GMP being a matrix having a set of bacterial genomes (bacterial strains) as rows and a list of multiple metabolic pathways corresponding to the formation of each metabolite as columns (310); and GMP was used to generate the FGmap, where the FGmap is a matrix with multiple bacterial genera as rows and multiple metabolic pathways as columns ( 312 ). [4] The processor-implemented method of [1], wherein the sample processing technique (400) for generating a pathway abundance matrix comprises the steps of: extracting a plurality of nucleic acids from the plurality of intestinal samples based on a nucleic acid extraction technique; (402) generating a plurality of nucleotide sequences from the plurality of extracted nucleic acids using a sequencer; (404) (406) using the plurality of nucleotide sequences to obtain a set of bacterial taxonomic abundance matrices at each taxonomic hierarchical level, the bacterial taxonomic abundance matrices being obtained by classifying the plurality of nucleotide sequences based on a predefined taxonomic level using a classification algorithm; and Generating pathway abundances using the set of bacterial taxonomic abundance matrices, GMP, and FGmap (408). [5] The processor-implemented method of any one of claims 1 to 5, wherein the step of recommending an optimized diet (500) includes: Between timestamp 1 and timestamp 2, normalized for each taxon in the union of taxon set 1 and taxon set 2 (d k ) (502); Importance scores of all taxa in the federations of taxon set 1 and taxon set 2 using normalized changes, multiple beneficial microbes, multiple harmful microbes from the knowledge base set (b k ) (504); The importance scores of all taxa in the combination of taxon set 1 and taxon set 2 (b k ) and a predefined taxon-compound utilization matrix were used to generate an individualized gut compound score (GCS j ), where the personalized gut compound score quantifies a compound's ability to increase multiple beneficial microbes in the gut ( 506 ); Gut bacteria score (gfs j ) is calculated for all food items in the set of food items using the gut compound score and the food component matrix, where the gut food score quantifies the ability of a food item to increase multiple beneficial microorganisms (508); and Optimizing an enteral food score using an optimization technique to obtain an optimized diet, the enteral food score being optimized subject to a plurality of constraint parameters (510) including a calorie parameter, a carbohydrate parameter, a protein parameter, and a fat parameter. [6] Certain foods (food i ) the gut food score is expressed as: JPEG0007634701000087.jpg2579 Where: TIFF0007634701000088.tif513 is the food composition matrix, where C j,i Food i Compound per unit j is the content. p = total number of compounds obtained in the food from the list foods, and m = total number of foods; JPEG0007634701000089.jpg25140 where T is the taxon-compound utilization matrix; where JPEG0007634701000090.jpg24154 q = total number of bacterial taxa; I is the indicator function, i.e., JPEG0007634701000091.jpg1984 PU: resident bacterial taxa; AP: pathogenic bacterial taxa; JPEG0007634701000092.jpg15128 where JPEG0007634701000093.jpg1768 Where: A 2 k is the taxa in sample T2 k is the abundance of A 1 k is the taxa in sample T1 k is the abundance of σ k Taxa of multiple healthy gut microbiota samples k is the standard deviation of [7] The method of [1], wherein the term "perturbed pathway" means an increase in the biosynthesis of a harmful pathway or a harmful metabolite or the degradation of a beneficial metabolite, or a decrease in a beneficial pathway or a beneficial metabolite or the degradation of a harmful metabolite, and the perturbed pathway is identified based on a statistical method, including one of a parametric method, a non-parametric method, or a machine learning based algorithm. [8] The method according to [1], wherein the personalized therapeutic intervention further comprises administering genetically engineered bacteria, wherein the genetically engineered bacteria comprises engineering (a) multiple microbial pathways for the production of one or more beneficial metabolites, or / and (b) multiple microbial pathways for the degradation / elimination of one or more harmful metabolites, into the genetic makeup of the gut microbes, wherein the genetically engineered bacteria is administered in one of the following shared manners: (a) genetically engineered probiotics, used alone or in combination with prebiotics, metabiotics, or parabiotics; (b) A genetically engineered organism that heterologously expresses one or more of the pathways necessary to produce a metabolite, wherein the genetically engineered organism may be one or more of the indigenous gut bacteria, wherein the indigenous gut bacteria may include, but are not limited to, beneficial microorganisms, commercially available probiotic strains, or facultative anaerobes that may reside in the gut; wherein the genetically engineered organism is also referred to as a modified bacterium, wherein the modified bacterium may be administered as a food, a food supplement, or a pharmaceutical composition or probiotic formulation. [9] A system (100) including: Input / Output Interface (106); one or more memories (102); and one or more hardware processors (104), one or more memories (104) coupled to the one or more hardware processors (104), the one or more hardware processors (104) configured to execute programmed instructions stored in the one or more memories (102): receiving, via one or more hardware processors, a set of input data, the set of input data including data relating to: Multiple metabolites, multiple metabolic pathways, multiple taxa, Information on nutrients, probiotics, prebiotics and supplements to replenish or break down multiple metabolic products; Multiple food components, multiple food items, microbial abundance matrices of healthy cohorts, multiple dietary optimization parameters, wherein the plurality of metabolites includes a set of beneficial metabolites and a set of harmful metabolites, the set of beneficial metabolites being beneficial to the intestinal health of the individual, and the set of harmful metabolites being harmful to the intestinal health of the individual; wherein the multiple metabolic pathways include either (a) beneficial pathways or (b) harmful pathways; wherein the beneficial pathway comprises one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites; and wherein the harmful pathway comprises one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of harmful metabolites; wherein a plurality of beneficial microorganisms possess beneficial pathways and a plurality of harmful microorganisms possess harmful pathways; wherein the plurality of taxa comprises a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, wherein the plurality of microorganisms comprises a plurality of genes having distinct genomic locations, a set of nucleotide sequences and protein sequences of the bacterial strains, and wherein the plurality of dietary optimization prerequisite parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix, the food component matrix tabulating the contents of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a food component matrix, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters relating to carbohydrate, protein, fat and calories, and a user-selected food; generating a set of knowledge bases using the set of input data (204) via one or more hardware processors, the set of knowledge bases including a genus-functional map (FGmap), a genome-metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap, the generation of the set of knowledge bases including the steps of: generating an FGmap and a GMP map, where the FGmap is a matrix with a function of a plurality of bacterial genera belonging to one of a plurality of taxa and a plurality of metabolic pathways as columns, where a first predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway, and the GMP is a matrix with a plurality of bacterial strains as rows and a plurality of metabolic pathways as columns, where a second predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway; generating a TAXA_NET graph based on the plurality of taxa and the FGmap (wherein the TAXA_NET graph is an undirected graph), where each node of the TAXA_NET graph corresponds to a taxon from the plurality of taxa and edges indicate positive relationships between pairs of taxa, where each taxon from the plurality of nodes is associated with a function of the plurality of taxa from the FGmap, the positive relationships including one of mutualism, mutualism, facultativeism, and cooperation; generate a TAXA-food component network based on multiple food components for each taxon in TAXA_NET, where the TAXA-food component network is an undirected network of each taxon associated with food components used by the taxon as metabolic or nutrient sources; Generate a food component network based on the food items of each food component in the TAXA-food component network, where the food component network is an undirected network of each food component present in the food item (204D); and Generate a transpose of GMP to obtain a ProbMap, where the ProbMap is a matrix with rows containing multiple metabolites and each metabolite from multiple metabolic pathways, and columns containing multiple bacterial strains, probiotics, prebiotics, and dietary components capable of biosynthesizing and / or degrading the corresponding multiple metabolites; receiving, via one or more hardware processors, a plurality of intestinal samples from the individual at two timestamps, the two timestamps including timestamp 1 and timestamp 2 at which the two samples were collected; generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including a taxon set 1 and a taxon set 2, the taxon set 1 and the taxon set 2 being generated using the timestamp 1 and the timestamp 2; generating, via one or more hardware processors, pathway abundances for each of a plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of a plurality of metabolic pathways for GMP in the gut microbiome of the individual at the corresponding timestamp; identifying a perturbed pathway, the perturbed pathway indicative of impaired gut health of the individual, the perturbed pathway being identified based on a comparison of pathway abundances of the multiple gut samples obtained at timestamp 1 and timestamp 2 using statistical methods, the perturbed pathway being identified based on any of the following: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease indicates a decrease in a set of beneficial microorganisms, or (b) Case B: An increase in pathway abundance of a population of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of the beneficial pathway group was decreased and the pathway abundance of the harmful pathway group was increased, suggesting an increase in harmful microbial groups and a decrease in beneficial microbial groups; determining an initial set of personalized therapeutics and dietary therapies using a ProbMap, the initial set of personalized therapeutics and dietary therapies including at least one of the following: a dietary regimen, the initial set of personalized therapeutics and dietary therapies being rows corresponding to the identified perturbed pathways, where the personalized therapeutics are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having a pathway for degradation or / and biosynthesis of a high probability metabolite, the high probability metabolite being a metabolite corresponding to the perturbed pathway in the ProbMap; identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, where the taxon set is comprised of TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes predefined first values ​​for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes predefined second values ​​for one or more of the harmful pathways in one of the FGmap and the GMP (216); identifying a set of first neighbors from the TAXA_NET graph for each tax element in TAXA_SET_P at timestamp 2, the set of first neighbors being direct neighbors of TAXA_SET_P including TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW; and recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving gut health of the individual, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and an initial set of personalized therapeutic and dietary recommendations, wherein the personalized therapeutic intervention includes at least one or more of the following: (a) For case A (reduced pathway abundance of a group of beneficial microorganisms), we recommend a prebiotic cocktail, a probiotic cocktail, and an optimized diet, which can replenish multiple beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of harmful microbial populations), recommending an optimized diet, which promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; (c) For case C (reduced pathway abundance of beneficial microbial populations and increased pathway abundance of harmful microbial populations), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, the probiotic cocktail and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms and suppress the growth of harmful microorganisms.

[10] [9], wherein the one or more hardware processors are configured by the instructions to perform generation of a GMP and an FGmap, the GMP and FGmap including: generating a matrix metabolite map (Mmap) based on the list of metabolites, the metabolic pathways, and organisms associated with each of the metabolic pathways, wherein the Mmap is generated for each of the metabolites; generating a bacterial genome map (BGM) using a bacterial genome database (BGD), the BGD comprising an exhaustive list of the bacterial strains, the BGM comprising information related to the bacterial strains including names of the bacterial strains, identification numbers of the bacterial strains, gene locations of the bacterial strains, and protein domains of the bacterial strains; generating a metabolic pathway domain map (MPDM) for each metabolite from the multiple metabolites based on Mmap and BGM (MPDM is composed of an exhaustive list of protein domains associated with each of the multiple metabolic pathways); Identifying the presence of multiple metabolic pathways in a list of bacterial strains and genomes using MPDM and BGM based on a predefined first threshold criterion; generating a GMP using the BGM, MPDM, multiple metabolic pathways, and multiple metabolites, the GMP being a matrix with a set of bacterial genomes (bacterial strains) as rows and a list of multiple metabolic pathways corresponding to the formation of each metabolite as columns; GMP was used to generate the FGmap, which is a matrix with multiple bacterial genera as rows and multiple metabolic pathways as columns.

[11] The system of [9], wherein the one or more hardware processors are configured by the instructions to implement a sample processing technique (400) to generate the path abundance matrix, the sample processing technique including: extracting a plurality of nucleic acids from the plurality of intestinal samples based on a nucleic acid extraction technique; generating a plurality of nucleotide sequences from the plurality of extracted nucleic acids using a sequencer; using the plurality of nucleotide sequences to obtain a set of bacterial taxonomic abundance matrices at each taxonomic hierarchical level, the bacterial taxonomic abundance matrices being obtained by classifying the plurality of nucleotide sequences based on a predefined taxonomic level using a classification algorithm; A set of bacterial taxonomic abundance matrices, GMPs, and FGmaps are used to generate pathway abundances.

[12] [9] , wherein the one or more hardware processors are configured by the instructions to perform a process for recommending the optimized diet, the process including: The normalized change (d k ) to calculate; Importance scores of all taxa in the combined taxon set 1 and taxon set 2 (b k ) to calculate; The importance score of all taxa in the union of taxon set 1 and taxon set 2 (b k ) and a predefined taxon-compound utilization matrix were used to generate an individualized gut compound score (GCS j ), where the personalized gut compound score quantifies the ability of a compound to increase multiple beneficial microbes in the gut; The gut compound scores and food component matrix were used to calculate the gut food score (gfs) for all foods in the set of foods. j ), where the Gut Food Score quantifies the ability of a food item to increase multiple beneficial microorganisms; and Optimizing the gut food score using an optimization technique to obtain an optimized diet, the gut food score being optimized subject to a number of constraint parameters including calorie parameters, carbohydrate parameters, protein parameters and fat parameters.

[13] In the system according to claim 12, the one or more hardware processors are configured to: i ) a system configured with instructions to perform a gut food score calculation: JPEG0007634701000094.jpg2289 Where: TIFF0007634701000095.tif513 is the food composition matrix, where C j,i Food i Compound per unit j The content of p = total number of compounds obtained in the food from the list foods, and m = total number of foods; JPEG0007634701000096.jpg23147 Where: T is the taxon-compound utilization matrix; where JPEG0007634701000097.jpg17161 q = total number of bacterial taxa; I is the indicator function, i.e., JPEG0007634701000098.jpg1887 PU: set of resident bacterial taxa; AP: pathogenic bacterial taxa; JPEG0007634701000099.jpg15137 where JPEG0007634701000100.jpg1972 Where: A 2 k is the taxa in sample T2 k is the abundance of A 1 k is the taxa in sample T1 k is the abundance of σ k Taxa of multiple healthy gut microbiota samples k is the standard deviation of

[14] [9] The system of claim 1, wherein the one or more hardware processors are configured with instructions to perform a personalized therapeutic intervention, further comprising administering a genetically engineered bacterium, the genetically engineered bacterium comprising: (a) a plurality of microbial pathways for the production of one or more beneficial metabolites; or (b) a plurality of microbial pathways for the degradation / elimination of one or more harmful metabolites; and the genetically engineered bacterium is configured with instructions to perform a personalized therapeutic intervention, the genetically engineered bacterium being administered in one of the following ways: (a) genetically engineered probiotics, used alone or in combination with prebiotics, metabiotics, or parabiotics; (b) A genetically engineered organism that heterologously expresses one or more of the pathways necessary to produce a metabolite, wherein the genetically engineered organism may be one or more of the indigenous gut bacteria, wherein the indigenous gut bacteria may include, but are not limited to, beneficial microorganisms, commercially available probiotic strains, or facultative anaerobes that may reside in the gut; wherein the genetically engineered organism is also referred to as a modified bacterium, wherein the modified bacterium may be administered as a food, a food supplement, or a pharmaceutical composition or probiotic formulation.

Claims

1. A processor-implemented method (200) for designing personalized therapeutics and dietary treatments based on microbiome function, comprising: Receiving (202) a set of input data via one or more hardware processors, the set of input data including data relating to: Multiple metabolites, multiple metabolic pathways, multiple taxa, nutritional information, probiotics, prebiotics, supplements for the replacement or degradation of multiple metabolites, multiple food components, multiple food items, microbial abundance matrices of healthy cohorts, multiple dietary optimization prerequisite parameters, wherein the plurality of metabolites includes a set of beneficial metabolites and a set of harmful metabolites, the set of beneficial metabolites being beneficial to the intestinal health of the individual, and the set of harmful metabolites being harmful to the intestinal health of the individual; wherein the multiple metabolic pathways include either (a) beneficial pathways or (b) harmful pathways; wherein the beneficial pathway comprises one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites; and wherein the harmful pathway comprises one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of harmful metabolites; wherein a plurality of beneficial microorganisms possess a beneficial pathway and a plurality of harmful microorganisms possess a harmful pathway; wherein the plurality of taxa comprises a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, wherein the plurality of microorganisms comprises a set of a plurality of genes having distinct genomic locations, a nucleotide sequence and a protein sequence of the bacterial strain; and wherein the plurality of dietary optimization prerequisite parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix, the food component matrix tabulating the contents of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a food component matrix, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters relating to carbohydrate, protein, fat and calories, and a user-selected food; generating (204) a set of knowledge bases using the set of input data via one or more hardware processors, the set of knowledge bases including a genus-function map (FGmap), a genome-metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap, the generation of the set of knowledge bases including the steps of: generating (204A) via one or more hardware processors an FGmap and a GMP map, where the FGmap is a matrix with a function of a plurality of bacterial genera belonging to one of a plurality of taxonomic groups and a plurality of metabolic pathways as columns, where a first predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway, and the GMP is a matrix of a plurality of bacterial strains as rows and a plurality of metabolic pathways as columns, where a second predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway; generating (204B) a TAXA_NET graph based on the plurality of taxa and the FGmap via one or more hardware processors, where the TAXA_NET graph is an undirected graph, where each node of the TAXA_NET graph corresponds to a taxon from the plurality of taxa and the edges indicate positive relationships between pairs of taxa, where each taxon from the plurality of nodes is associated with a function of the plurality of taxa from the FGmap, the positive relationships including one of mutualism, mutualism, facultativeism, and cooperation; generating (204C) via one or more hardware processors a TAXA-food component network based on the plurality of food components for each taxon in the TAXA_NET, where the TAXA-food component network is an undirected network of each taxon associated with food components used by the taxon as a metabolic or nutrient source; generating (204D) a food component network based on the food items of each food component in the TAXA-food component network via one or more hardware processors, where the food component network is an undirected network of each food component present in the food item; and generating, via one or more hardware processors, a transpose of the GMP to obtain a ProbMap (204E), where the ProbMap is a matrix having rows containing a plurality of metabolites and each metabolite from a plurality of metabolic pathways, and columns containing a plurality of bacterial strains, probiotics, prebiotics, dietary components capable of biosynthesis and / or degradation of the corresponding plurality of metabolites; receiving (206) a plurality of intestinal samples from the individual at two time stamps via one or more hardware processors, the two time stamps including time stamp 1 and time stamp 2 at which the two samples were collected; generating (208) a taxon abundance matrix for the plurality of gut samples via the one or more hardware processors, the taxon abundance matrix including taxon set 1 and taxon set 2, the taxon set 1 and the taxon set 2 being generated using the timestamp 1 and the timestamp 2; generating (210) pathway abundances for each of a plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap via one or more hardware processors, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp; Identifying (212) a perturbed pathway via the one or more hardware processors, the perturbed pathway being indicative of a deterioration in the individual's gut health, the perturbed pathway being identified based on a comparison of pathway abundances of the multiple gut samples taken at timestamp 1 and timestamp 2 using statistical methods, the perturbed pathway being identified based on any of the following: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease indicates a decrease in a set of beneficial microorganisms, or (b) Case B: An increase in pathway abundance of a population of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of beneficial pathways was decreased and the pathway abundance of harmful pathways was increased, suggesting an increase in harmful microbial groups and a decrease in beneficial microbial groups; determining (214) an initial set of personalized therapeutics and dietary therapies via the one or more hardware processors, the initial set of personalized therapeutics and dietary therapies including at least one of the following personalized therapies and personalized dietary therapies using a ProbMap, the initial set of personalized therapeutics and dietary therapies being rows corresponding to the identified perturbed pathways, where the personalized therapeutics are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having a pathway for degradation or / and biosynthesis of a high probability metabolite, the high probability metabolite being a metabolite corresponding to the perturbed pathway in the ProbMap; identifying (216) via one or more hardware processors a taxon set in the taxon abundance matrix obtained at timestamp 2, where the taxon set is comprised of TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes predefined first values ​​for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes predefined second values ​​for one or more of the harmful pathways in one of the FGmap and the GMP; identifying (218) from the TAXA_NET graph, via one or more hardware processors, a set of first neighbors for each tax element in TAXA_SET_P at timestamp 2, the set of first neighbors being direct neighbors of TAXA_SET_P, including TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW; and recommending (220) via the one or more hardware processors a personalized therapeutic intervention for improving the individual's gut health, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and an initial set of personalized therapeutic and dietary recommendations, the personalized therapeutic intervention including at least one or more of the following: (a) for case A (reduced pathway abundance of a group of beneficial microorganisms), recommending a prebiotic cocktail, a probiotic cocktail, and an optimized diet, where the prebiotic cocktail, the probiotic cocktail, and the optimized diet replenish a plurality of beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of a population of harmful microorganisms), recommending an optimized diet, the optimized diet promoting the growth of multiple beneficial microorganisms and suppressing the growth of harmful microorganisms; (c) For case C (reduced pathway abundance of beneficial microbial population and increased pathway abundance of harmful microbial population), recommending a prebiotic cocktail, a probiotic cocktail and an optimized diet, where the prebiotic cocktail, the probiotic cocktail and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms and suppress the growth of harmful microorganisms.

2. 2. The processor-implemented method of claim 1, wherein the plurality of beneficial metabolic pathways may include one or more of pyruvate to butyrate production via a pyruvate pathway, an ammonia oxidation pathway, and a pathway for trimethylamine oxide (TMA) degradation, and the plurality of harmful metabolic pathways may include, but are not limited to, one or more of ammonia release pathways, biogenic amine production, and trimethylamine production.

3. The processor-implemented method of claim 1, wherein generating the FGmap and the GMP map comprises the steps of: generating a matrix metabolite map (Mmap) based on the list of the plurality of metabolites, the plurality of metabolic pathways, and organisms associated with each of the plurality of metabolic pathways, wherein a Mmap is generated for each of the plurality of metabolites; generating a bacterial genome map (BGM) using a bacterial genome database (BGD), the BGD comprising an exhaustive list of the plurality of bacterial strains, the BGM comprising information relating to the plurality of bacterial strains including names of the plurality of bacterial strains, identification numbers of the plurality of bacterial strains, gene locations of the plurality of bacterial strains, and protein domains of the plurality of bacterial strains; generating a metabolic pathway domain map (MPDM) for each metabolite from the plurality of metabolic pathways based on the Mmap and the BGM, the MPDM comprising a comprehensive list of protein domains associated with each of the plurality of metabolic pathways; Identifying the presence of multiple metabolic pathways in the list of bacterial strains and genomes using MPDM and BGM based on a predefined first threshold criterion; generating a GMP using the BGM, MPDM, multiple metabolic pathways, and multiple metabolites, the GMP being a matrix with a set of bacterial genomes (bacterial strains) as rows and a list of multiple metabolic pathways corresponding to the formation of each metabolite as columns; and Generating an FGmap using GMP, where the FGmap is a matrix with bacterial genera as rows and metabolic pathways as columns.

4. 2. The processor-implemented method of claim 1, wherein the sample processing technique for generating pathway abundances comprises the steps of: extracting a plurality of nucleic acids from the plurality of intestinal samples based on a nucleic acid extraction technique; generating a plurality of nucleotide sequences from the extracted plurality of nucleic acids using a sequencer; using the plurality of nucleotide sequences to obtain a set of bacterial taxonomic abundance matrices at each taxonomic hierarchical level, the bacterial taxonomic abundance matrices being obtained by classifying the plurality of nucleotide sequences based on predefined taxonomic levels using a classification algorithm; and Generating pathway abundances using the set of bacterial taxonomic abundance matrices, GMP, and FGmap.

5. 2. The processor-implemented method of claim 1, wherein recommending an optimized diet comprises: Between timestamp 1 and timestamp 2, normalized for each taxon in the union of taxon set 1 and taxon set 2 (d k ) calculating Importance scores of all taxa in the federations of taxon set 1 and taxon set 2 using normalized changes, multiple beneficial microbes, multiple harmful microbes from the knowledge base set (b k ) calculating The importance scores of all taxa in the combination of taxon set 1 and taxon set 2 (b k ) and a predefined taxon-compound utilization matrix were used to generate an individualized gut compound score (GCS j ), where the personalized gut compound score quantifies the ability of the compound to increase multiple beneficial microbes in the gut; Gut bacteria score (gfs j ) for all food items in the set of food items using the gut compound score and the food component matrix, where the gut food score quantifies the ability of the food item to increase multiple beneficial microorganisms; and optimizing the gut food score using an optimization technique to obtain an optimized diet, the gut food score being optimized subject to a number of constraint parameters including a calorie parameter, a carbohydrate parameter, a protein parameter, and a fat parameter.

6. Certain foods (food i 6. The processor-implemented method of claim 5, wherein the gut food score of Where: is the food composition matrix, where C j,i Food i Compound per unit j is the content. p = total number of compounds obtained in the food from the list of foods, and m = total number of foods; where T is the taxon-compound utilization matrix; where q = total number of bacterial taxa; I is the indicator function, i.e., PU: resident bacterial taxa; AP: pathogenic bacterial taxa; where Where: A 2 k is the taxa in sample T2 k is the abundance of A 1 k is the taxa in sample T1 k is the abundance of σ k Taxa of multiple healthy gut microbiota samples k is the standard deviation of

7. The processor-implemented method of claim 1, wherein the term "perturbed pathway" means an increase in the biosynthesis of a harmful pathway or harmful metabolite or the degradation of a beneficial metabolite, or a decrease in a beneficial pathway or beneficial metabolite or the degradation of a harmful metabolite, and the perturbed pathway is identified based on a statistical method including one of a parametric method, a non-parametric method, or an algorithm based on machine learning.

8. 2. The processor-implemented method of claim 1, wherein the personalized therapeutic intervention further comprises administering genetically engineered bacteria, the genetically engineered bacteria comprising: (a) multiple microbial pathways for the production of one or more beneficial metabolites; or (b) multiple microbial pathways for the degradation / removal of one or more harmful metabolites; engineered into the genetic makeup of the gut microbes, the genetically engineered bacteria being administered in one of the following shared manners: (a) genetically engineered probiotics, used alone or in combination with prebiotics, metabiotics, or parabiotics; (b) A genetically engineered organism that heterologously expresses one or more of the pathways necessary to produce a metabolite, wherein the genetically engineered organism may be one or more of the indigenous gut bacteria, wherein the indigenous gut bacteria may include, but are not limited to, beneficial microorganisms, commercially available probiotic strains, or facultative anaerobes that may reside in the gut; wherein the genetically engineered organism is also referred to as a modified bacterium, wherein the modified bacterium may be administered as a food, a food supplement, or a pharmaceutical composition or probiotic formulation.

9. A system (100) comprising: Input / Output Interface (106); one or more memories (102); and one or more hardware processors (104), one or more memories (104) coupled to the one or more hardware processors (104), the one or more hardware processors (104) configured to execute programmed instructions stored in the one or more memories (102): receiving, via one or more hardware processors, a set of input data, the set of input data including data relating to: Multiple metabolites, multiple metabolic pathways, multiple taxa, Information on nutrients, probiotics, prebiotics and supplements to replenish or break down multiple metabolic products; Multiple food components, multiple food items, microbial abundance matrices of healthy cohorts, multiple dietary optimization parameters, wherein the plurality of metabolites includes a set of beneficial metabolites and a set of harmful metabolites, the set of beneficial metabolites being beneficial to the intestinal health of the individual, and the set of harmful metabolites being harmful to the intestinal health of the individual; wherein the multiple metabolic pathways include either (a) beneficial pathways or (b) harmful pathways; wherein the beneficial pathway comprises one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites; and wherein the harmful pathway comprises one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of harmful metabolites; wherein a plurality of beneficial microorganisms possess a beneficial pathway and a plurality of harmful microorganisms possess a harmful pathway; wherein the plurality of taxa comprises a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, wherein the plurality of microorganisms comprises a plurality of genes having distinct genomic locations, a set of nucleotide sequences and protein sequences of the bacterial strains, and wherein the plurality of dietary optimization prerequisite parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix, the food component matrix tabulating the contents of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a food component matrix, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters relating to carbohydrate, protein, fat and calories, and a user-selected food; generating, via one or more hardware processors, a set of knowledge bases using a set of input data (204), the set of knowledge bases including a genus-function map (FGmap), a genome-metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap, the generation of the knowledge bases including the steps of: generating an FGmap and a GMP map, where the FGmap is a matrix with a function of a plurality of bacterial genera belonging to one of a plurality of taxonomic groups and a plurality of metabolic pathways as columns, where a first predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway, and the GMP is a matrix of a plurality of bacterial strains as rows and a plurality of metabolic pathways as columns, where a second predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway; generating a TAXA_NET graph based on the plurality of taxa and the FGmap (wherein the TAXA_NET graph is an undirected graph), where each node of the TAXA_NET graph corresponds to a taxon from the plurality of taxa, and edges indicate positive relationships between pairs of taxa, where each taxon from the plurality of nodes is associated with a function of the plurality of taxa from the FGmap, and the positive relationships include one of mutualism, mutualism, facultativeism, and cooperation; generating a TAXA-food component network based on the plurality of food components for each taxon in TAXA_NET, where the TAXA-food component network is an undirected network of each taxon associated with food components used by the taxon as a metabolic or nutrient source; generating a food component network based on the food items of each food component in the TAXA-food component network, where the food component network is an undirected network of each food component present in the food item; and generating a transpose of the GMP to obtain a ProbMap, where the ProbMap is a matrix with rows including a plurality of metabolites and each metabolite from a plurality of metabolic pathways, and columns including a plurality of bacterial strains, probiotics, prebiotics, and dietary components capable of biosynthesis and / or degradation of the corresponding plurality of metabolites; receiving, via one or more hardware processors, a plurality of intestinal samples from the individual at two timestamps, the two timestamps including timestamp 1 and timestamp 2 at which the two samples were collected; generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including a taxon set 1 and a taxon set 2, the taxon set 1 and the taxon set 2 being generated using the timestamp 1 and the timestamp 2; generating pathway abundances for each of a plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap via one or more hardware processors, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp; identifying a perturbed pathway, the perturbed pathway indicative of impaired gut health of the individual, the perturbed pathway being identified based on a comparison of pathway abundances of the multiple gut samples obtained at timestamp 1 and timestamp 2 using statistical methods, the perturbed pathway being identified based on any of the following: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease indicates a decrease in a set of beneficial microorganisms, or (b) Case B: An increase in pathway abundance of a population of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of beneficial pathways was decreased and the pathway abundance of harmful pathways was increased, suggesting an increase in harmful microbial groups and a decrease in beneficial microbial groups; determining an initial set of personalized therapeutics and dietary therapies, the initial set of personalized therapeutics and dietary therapies including at least one of the following: personalized therapies and personalized dietary therapies using a ProbMap, the initial set of personalized therapeutics and dietary therapies being rows corresponding to the identified perturbed pathways, the dietary therapies, where the personalized therapeutics are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having a pathway for degradation or / and biosynthesis of a high probability metabolite, the high probability metabolite being a metabolite corresponding to the perturbed pathway in the ProbMap; identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, where the taxon set is composed of TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes predefined first values ​​for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes predefined second values ​​for one or more of the harmful pathways in one of the FGmap and the GMP; identifying a set of first neighbors for each tax element in TAXA_SET_P at timestamp 2 from the TAXA_NET graph, the set of first neighbors being direct neighbors of TAXA_SET_P including TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW; and recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving the individual's gut health, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and an initial set of personalized therapeutic and dietary recommendations, wherein the personalized therapeutic intervention includes at least one or more of the following: (a) For case A (reduced pathway abundance of a group of beneficial microorganisms), we recommend a prebiotic cocktail, a probiotic cocktail, and an optimized diet, which can replenish multiple beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of a population of harmful microorganisms), recommending an optimized diet, which promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; (c) For case C (reduced pathway abundance of beneficial microbial populations and increased pathway abundance of harmful microbial populations), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, the probiotic cocktail and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms and suppress the growth of harmful microorganisms.

10. 10. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to perform generation of an FGmap and a GMP map, the FGmap and GMP map including: generating a matrix metabolite map (Mmap) based on the list of metabolites, the metabolic pathways, and organisms associated with each of the metabolic pathways, wherein the Mmap is generated for each of the metabolites; generating a bacterial genome map (BGM) using a bacterial genome database (BGD), the BGD comprising an exhaustive list of the bacterial strains, the BGM comprising information related to the bacterial strains including names of the bacterial strains, identification numbers of the bacterial strains, gene locations of the bacterial strains, and protein domains of the bacterial strains; generating a Metabolite Pathway Domain Map (MPDM) for each metabolite from the plurality of metabolites based on the Mmap and the BGM, where the MPDM is comprised of an exhaustive list of protein domains associated with each of the plurality of metabolic pathways; identifying the presence of multiple metabolic pathways in the list of bacterial strains and genomes using MPDM and BGM based on a predefined first threshold criterion; generating a GMP using the BGM, the MPDM, the metabolic pathways, and the metabolites, the GMP being a matrix with a set of bacterial genomes (strains) as rows and a list of metabolic pathways corresponding to the formation of each metabolite as columns; A step of generating an FGmap using GMP, the FGmap being a matrix with multiple bacterial genera as rows and multiple metabolic pathways as columns.

11. 10. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to perform sample processing techniques to generate the pathway abundances, the sample processing techniques including: extracting a plurality of nucleic acids from the plurality of intestinal samples based on a nucleic acid extraction technique; generating a plurality of nucleotide sequences from the extracted plurality of nucleic acids using a sequencer; using the plurality of nucleotide sequences to obtain a set of bacterial taxonomic abundance matrices at each taxonomic hierarchical level, the bacterial taxonomic abundance matrices being obtained by classifying the plurality of nucleotide sequences based on a predefined taxonomic level using a classification algorithm; Generating pathway abundances using a set of bacterial taxonomic abundance matrices, GMPs, and FGmaps.

12. 10. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to perform a process for recommending the optimized diet, the process comprising: The normalized change (d k ) calculating Importance scores of all taxa in the combined taxon set 1 and taxon set 2 (b k ) calculating The importance score of all taxa in the union of taxon set 1 and taxon set 2 (b k ) and a predefined taxon-compound utilization matrix were used to generate an individualized gut compound score (GCS j ), where the personalized gut compound score quantifies the ability of the compound to increase multiple beneficial microorganisms in the gut; The gut compound scores and food component matrix were used to calculate the gut food score (gfs) for all foods in the set of foods. j ), where the gut food score quantifies the ability of a food item to increase multiple beneficial microorganisms; and optimizing the gut food score using an optimization technique to obtain an optimized diet, the gut food score being optimized subject to a number of constraint parameters including a calorie parameter, a carbohydrate parameter, a protein parameter and a fat parameter.

13. 13. The system of claim 12, wherein the one or more hardware processors are configured to: i ) a gut food score calculation, comprising: Where: is the food composition matrix, where C j,i Food i Compound per unit j The content of p = total number of compounds obtained in the food from the list of foods, and m = total number of foods; Where: T is the taxon-compound utilization matrix; where q = total number of bacterial taxa; I is the indicator function, i.e., PU: set of resident bacterial taxa; AP: pathogenic bacterial taxa; where Where: A 2 k is the taxa in sample T2 k is the abundance of A 1 k is the taxa in sample T1 k is the abundance of σ k Taxa of multiple healthy gut microbiota samples k is the standard deviation of

14. 10. The system of claim 9, wherein the one or more hardware processors are configured with instructions for performing a personalized therapeutic intervention, further comprising administering a genetically engineered bacterium, the genetically engineered bacterium comprising: (a) a plurality of microbial pathways for the production of one or more beneficial metabolites; or (b) a plurality of microbial pathways for the degradation / removal of one or more harmful metabolites; engineered into the genetic makeup of the gut microbes; and wherein the genetically engineered bacterium is administered in one of the manners shared below. (a) genetically engineered probiotics, used alone or in combination with prebiotics, metabiotics, or parabiotics; (b) A genetically engineered organism that heterologously expresses one or more of the pathways necessary to produce a metabolite, wherein the genetically engineered organism may be one or more of the indigenous gut bacteria, wherein the indigenous gut bacteria may include, but are not limited to, beneficial microorganisms, commercially available probiotic strains, or facultative anaerobes that may reside in the gut; wherein the genetically engineered organism is also referred to as a modified bacterium, wherein the modified bacterium may be administered as a food, a food supplement, or a pharmaceutical composition or probiotic formulation.

15. One or more non-transitory machine-readable information storage media comprising one or more instructions that, when executed by one or more hardware processors, cause: receiving, via one or more hardware processors, a set of input data, the set of input data including data relating to: Multiple metabolites, multiple metabolic pathways, multiple taxa, Information on nutrients, probiotics, prebiotics and supplements to replenish or break down multiple metabolic products; Multiple food components, multiple food items, microbial abundance matrices of healthy cohorts, multiple dietary optimization parameters, wherein the plurality of metabolites includes a set of beneficial metabolites and a set of harmful metabolites, the set of beneficial metabolites being beneficial to the intestinal health of the individual, and the set of harmful metabolites being harmful to the intestinal health of the individual; wherein the multiple metabolic pathways include either (a) beneficial pathways or (b) harmful pathways; wherein the beneficial pathway comprises one or more of a set of metabolic pathways for the biosynthesis of a plurality of beneficial metabolites and a set of metabolic pathways for the degradation of a plurality of harmful metabolites; and wherein the harmful pathway comprises one or more of a set of metabolic pathways for the degradation of a plurality of beneficial metabolites and a set of metabolic pathways for the biosynthesis of a plurality of harmful metabolites; wherein a plurality of beneficial microorganisms possess a beneficial pathway and a plurality of harmful microorganisms possess a harmful pathway; wherein the plurality of taxa comprises a plurality of microorganisms, wherein the plurality of microorganisms comprises a plurality of beneficial microorganisms, a plurality of harmful microorganisms, wherein the plurality of microorganisms are classified according to a taxonomic hierarchy, wherein the taxonomic hierarchy comprises a plurality of bacterial classes, a plurality of bacterial orders, a plurality of bacterial families, a plurality of bacterial genera, a plurality of bacterial species, and a plurality of bacterial strains, including one or more of a plurality of bacterial phyla, wherein the plurality of microorganisms comprises a plurality of genes having distinct genomic locations, a set of nucleotide sequences and protein sequences of the bacterial strains, and wherein the plurality of dietary optimization prerequisite parameters include a set of foods, a complete set of compounds found in the set of foods, a food component matrix, the food component matrix tabulating the contents of all compounds belonging to the set of compounds in all foods belonging to the set of foods, the total carbohydrate, protein, fat and calorie content of each food belonging to the set of foods, a food component matrix, a list of bacterial taxa in the human gut, a set of functions for identifying beneficial and harmful bacterial taxa, a taxa-compound utilization matrix, a set of user-defined upper and lower limit parameters relating to carbohydrate, protein, fat and calories, and a user-selected food; generating, via one or more hardware processors, a set of knowledge bases using a set of input data (204), the set of knowledge bases including a genus-function map (FGmap), a genome-metabolic pathway (GMP) map, a taxon-associated reference profile (TAXA_NET) graph, a TAXA-food component network, a food component network, and a ProbMap, the generation of the knowledge bases including the steps of: generating an FGmap and a GMP map, where the FGmap is a matrix with a function of a plurality of bacterial genera belonging to one of a plurality of taxonomic groups and a plurality of metabolic pathways as columns, where a first predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway, and the GMP is a matrix of a plurality of bacterial strains as rows and a plurality of metabolic pathways as columns, where a second predefined indicator indicates one of (a) the presence of the metabolic pathway and (b) the absence of the metabolic pathway; generating a TAXA_NET graph based on the multiple taxa and the FGmap (wherein the TAXA_NET graph is an undirected graph), where each node of the TAXA_NET graph corresponds to a taxon from the multiple taxa, and edges indicate positive relationships between pairs of taxa, where each taxon from the multiple nodes is associated with a function of the multiple taxa from the FGmap, and the positive relationships include one of mutualism, mutualism, facultativeism, and cooperation; generating a TAXA-food component network based on the plurality of food components for each taxon in TAXA_NET, where the TAXA-food component network is an undirected network of each taxon associated with food components used by the taxon as a metabolic or nutrient source; generating a food component network based on the food items of each food component in the TAXA-food component network, where the food component network is an undirected network of each food component present in the food item; and generating a transpose of the GMP to obtain a ProbMap, where the ProbMap is a matrix with rows including a plurality of metabolites and each metabolite from a plurality of metabolic pathways, and columns including a plurality of bacterial strains, probiotics, prebiotics, and dietary components capable of biosynthesis and / or degradation of the corresponding plurality of metabolites; receiving, via one or more hardware processors, a plurality of intestinal samples from the individual at two time stamps, the two time stamps including time stamp 1 and time stamp 2 at which the two samples were collected; generating, via the one or more hardware processors, a taxon abundance matrix for the plurality of gut samples, the taxon abundance matrix including a taxon set 1 and a taxon set 2, the taxon set 1 and the taxon set 2 being generated using the timestamp 1 and the timestamp 2; generating pathway abundances for each of a plurality of gut samples obtained at timestamp 1 and timestamp 2 using the taxon abundance matrix and the FGmap via one or more hardware processors, the pathway abundances being generated using sample processing techniques, the pathway abundances for each timestamp indicating the presence of multiple metabolic pathways of GMP in the gut microbiome of the individual at the corresponding timestamp; identifying a perturbed pathway, the perturbed pathway indicative of impaired gut health of the individual, the perturbed pathway being identified based on a comparison of pathway abundances of the multiple gut samples obtained at timestamp 1 and timestamp 2 using statistical methods, the perturbed pathway being identified based on any of the following: (a) Case A: A decrease in pathway abundance of a set of beneficial pathways, where the decrease indicates a decrease in a set of beneficial microorganisms, or (b) Case B: An increase in pathway abundance of a population of harmful pathways, where the increase indicates an increase in the population of harmful microorganisms; or (c) Case C: The pathway abundance of beneficial pathways was decreased and the pathway abundance of harmful pathways was increased, suggesting an increase in harmful microbial groups and a decrease in beneficial microbial groups; determining an initial set of personalized therapeutics and dietary therapies, the initial set of personalized therapeutics and dietary therapies including at least one of the following: personalized therapies and personalized dietary therapies using a ProbMap, the initial set of personalized therapeutics and dietary therapies being rows corresponding to the identified perturbed pathways, the dietary therapies, where the personalized therapeutics are determined as (a) a plurality of bacterial strains having a perturbed pathway, or (b) a plurality of bacterial strains having a pathway for degradation or / and biosynthesis of a high probability metabolite, the high probability metabolite being a metabolite corresponding to the perturbed pathway in the ProbMap; identifying a taxon set in the taxon abundance matrix obtained at timestamp 2, where the taxon set is comprised of TAXA_SET_P and TAXA_SET_A, where TAXA_SET_P includes predefined first values ​​for one or more of the beneficial pathways in one of the FGmap and the GMP, and TAXA_SET_A includes predefined second values ​​for one or more of the harmful pathways in one of the FGmap and the GMP (216); identifying a set of first neighbors for each tax element in TAXA_SET_P at timestamp 2 from the TAXA_NET graph, the set of first neighbors being direct neighbors of TAXA_SET_P including TAXA_SET_FN_COM, TAXA_SET_INTERSECT, and TAXA_SET_NEW; and recommending, via the one or more hardware processors, a personalized therapeutic intervention for improving the individual's gut health, the therapeutic intervention including recommending one of: (a) a prebiotic cocktail; (b) a probiotic cocktail; (c) an optimized diet based on the identified first neighborhood using the set of knowledge bases and an initial set of personalized therapeutic and dietary recommendations, wherein the personalized therapeutic intervention includes at least one or more of the following: (a) For case A (reduced pathway abundance of a group of beneficial microorganisms), we recommend a prebiotic cocktail, a probiotic cocktail, and an optimized diet, which can replenish multiple beneficial microorganisms and support the proliferation of beneficial microorganisms; (b) for case B (increased pathway abundance of a population of harmful microorganisms), recommending an optimized diet, which promotes the growth of multiple beneficial microorganisms and inhibits the growth of harmful microorganisms; (c) For case C (reduced pathway abundance of beneficial microbial populations and increased pathway abundance of harmful microbial populations), a prebiotic cocktail, a probiotic cocktail and an optimized diet are recommended, where the prebiotic cocktail, the probiotic cocktail and the optimized diet replenish multiple beneficial microorganisms, support the growth of beneficial microorganisms and suppress the growth of harmful microorganisms.

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