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US-12626804-B2 - System and method for determining a personalized probiotic therapeutic regimen

US12626804B2US 12626804 B2US12626804 B2US 12626804B2US-12626804-B2

Abstract

Existing techniques fail to provide a method to cumulate effects of interactions between groups of gut-associated microbes to predict efficiency of a probiotic organism in an individual. The present disclosure collects a test biological sample from the subject requiring personalization and extracts DNA from test biological sample and information specific to dietary preferences of the subject. Organisms from probiotic organisms dataset are obtained and a plurality of genome scale metabolic models are created for microbes comprised in gut microbiota of subject and obtained probiotic organisms. Metabolic simulations are performed to ascertain monoculture and co-culture growth of every pair of organisms comprised in gut microbiota of subject and obtained probiotic organisms. Sustainability is computed for evaluating capability of each organism to proliferate within gut. Net-effect is computed by quantifying an overall influence of each probiotic organism. An efficacious probiotic organism is selected based on at least one of net-effect and sustainability.

Inventors

  • Tungadri BOSE
  • Anirban DUTTA
  • Nishal Kumar PINNA
  • Sharmila Shekhar Mande
  • ROHAN SINGH

Assignees

  • TATA CONSULTANCY SERVICES LIMITED

Dates

Publication Date
20260512
Application Date
20240827
Priority Date
20230926

Claims (18)

  1. 1 . A method, comprising: receiving, via one or more hardware processors, an information specific to one or more dietary preferences of a subject requiring personalization from one or more communication media; defining, via the one or more hardware processors, a set of metabolic constraints for the one or more dietary preferences of the subject requiring personalization, wherein the set of metabolic constraints are used for one or more metabolic simulations; receiving, via the one or more hardware processors, a test biological sample of the subject requiring personalization from the one or more communication media; extracting, via the one or more hardware processors, DNA (Deoxyribonucleic Acid) from the test biological sample, using a DNA extraction technique; determining, via the one or more hardware processors, a microbial abundance of each microbe present in the test biological sample, from the microbial DNA which comprises a gut microbiota, using a set of probes specific to each of the microbes from stretches of DNA sequences sequenced from the microbial DNA extracted from the test biological sample, to obtain a microbial taxonomic profile associated with the test biological sample, wherein the microbial taxonomic profile associated with the test biological sample comprises microbial abundance of each of the microbes corresponding to a set of microbial DNA sequences present in the test biological sample; obtaining, via the one or more hardware processors, an information specific to a plurality of probiotic organisms from at least one probiotic organisms dataset to evaluate the efficacy of the plurality of probiotic organisms; creating, via the one or more hardware processors, a plurality of genome scale metabolic models of each of a plurality of microbes comprised in the gut microbiota of the subject and each of the plurality of probiotic organisms whose efficacy are be evaluated; assigning, via the one or more hardware processors, M-type to the plurality of microbes comprised in the gut microbiota as T=+1 for at least one of beneficial microbes and commensal microbes and T=−1 for at least one of pathogenic microbes and opportunistic microbes; performing, via the one or more hardware processors, the one or more metabolic simulations to ascertain a mono-culture growth of each of the plurality of microbes comprising the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject; performing, via the one or more hardware processors, the one or more metabolic simulations to ascertain a co-culture growth of every pair of the plurality of microbes comprised in the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject, wherein the one or more metabolic simulations include in-silico metabolic simulations including a flux balance simulation for simulating metabolic behaviour of an organism under the set of metabolic constraints and simulating an effect of probiotic interventions on a plurality of gut microbiota compositions and the one or more dietary preferences of the subject; computing, via the one or more hardware processors, sustainability of each of the organism comprised in the plurality of probiotic organisms for evaluating the capability of each of the organism comprised in the plurality of probiotic organisms to proliferate within the gut, wherein the sustainability is defined as: D [ Sustainability ] a = ∑ ( G a p G a s ) * A b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preferences of the subject requiring personalization, ‘a’ is the probiotic organism, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, A b is the abundance of organism ‘b’ the gut microbiota of the subject requiring personalization, G a p is the growth rate of organism ‘a’ in co-growth condition with each of the microbes constituting the gut microbiota of the subject requiring personalization ‘b’ in ‘D’, G a s is the growth rate of organism ‘a’ in mono-culture condition in ‘D’; computing, via the one or more hardware processors, net-effect of each of the organism comprised in the plurality of probiotic organisms for quantifying an overall influence of the plurality of probiotic organisms as a score by summating a plurality of positive influences and a plurality of negative influences on each of the microbes comprised in the gut microbiota, wherein the plurality of positive influences comprise the growth and proliferation of at least one of the beneficial microbes and at least one of the commensal microbes and the plurality of negative influences comprises the growth and proliferation of at least one of the pathogenic microbes and at least one of the opportunistic microbes, wherein the net-effect reflects a net change in growth rates of the plurality of microbes in the gut microbiota as a consequence of administering a probiotic, and wherein the net-effect is defined as: D [ Net - effect ] a = ∑ ( G b p G b s ) * A b * T b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preference of the subject requiring personalization, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, G b p is the growth rate of organism ‘b’ in co-cultured condition with the probiotic organism ‘a’ in ‘D’, G b s is the growth rate of organism ‘b’ in mono-culture condition in ‘D’, A b is the abundance of organism ‘b’ in the gut microbiota of the subject requiring personalization, T b is the M-types of organism ‘b’, wherein the abundance of the organism ‘b’ in the gut microbiota of the subject requiring personalization is capped by an abundance factor to avoid over-estimating an influence including the sustainability or the net-effect due to large variation in the abundance of the microbe comprising the gut microbiota of the subject requiring personalization; selecting, via the one or more hardware processors, an efficacious probiotic organism based on at least one of the net-effect and the sustainability; designing a personalized probiotic intervention regimen in the form of a probiotics treatment to the subject based on the selected efficacious probiotic organism; and administering the efficacious probiotic organism to the subject to improve the gut microbiota, wherein a dosage requirement for the efficacious probiotic organism is least when the efficacious probiotic organism is selected with a highest sustainability and the net-effect to deliver optimal therapeutic outcome, wherein when a probiotic organism with both the highest sustainability and the net-effect is not identified, then a probiotic organism with the highest sustainability and moderately high net-effect is selected and administration or the dosage requirement of the probiotic organism is the least of all available probiotic therapy options, wherein when the probiotic organism with a highest net-effect and a moderately high sustainability is selected, then the effect of the probiotic intervention is optimal of all the available probiotic therapy options, and the dosage requirement of the probiotic organism is more frequent or higher dosage, and wherein the method is implemented for the probiotics treatment to the subject requiring personalization based on the efficacious probiotic organism to improve the gut microbiota.
  2. 2 . The processor implemented method of claim 1 , wherein defining the set of metabolic constraints includes (a) identifying and quantifying one or more nutrients constituting the one or more dietary preferences of the subject requiring personalization, and (b) converting the one or more quantified nutrients into the set of metabolic constraints, and wherein the set of metabolic constraints is defined based on the common diets of at least one of a region and an ethnic population to which the subject belongs to, if the one or more dietary preferences of the subject requiring personalization is not available and the information regarding the one or more dietary preferences of the subject is obtained from the database.
  3. 3 . The processor implemented method of claim 1 , wherein the step of determining the microbial abundance comprises determining the microbial abundance of a predefined set of microbes using multiple DNA characterization techniques including Next Generation Sequencing protocols (NGS), a multiplex quantitative Polymerase Chain Reaction (qPCR) technique, nucleic acid hybridization techniques and the like, and wherein the microbial abundance of each microbe constituting the gut microbiota of the subject is defined as at least one of the mean microbial abundance of each microbe constituting the gut microbiota of an ethnic group and one or more lifestyle habits to which the subject belongs to, using information from the database, if the taxonomic profile associated with the test biological sample is not available.
  4. 4 . The processor implemented method of claim 1 , wherein the probiotic organisms including Bacillus strains are beneficial against Clostridium perfringens, Listeria monocytogenes, Staphylococcus aureus and Clostridium botulinum infections, wherein the probiotic organisms including Bifidobacterium based probiotics, Leuconostoc mesenteroides subsp cremoris ATCC 19254, Pediococcus acidilactici 7 4 and Streptococcus thermophilus LMG 18311 are efficient against Clostridium perfringens infection, wherein the probiotic organisms including Lactococcus lactis subsp lactis 111403 against L. mesenteroides, C botulinum and Salmonella infections and Bifidobacterium adolescentis ATCC 15703 and B. longum JCM 1217 against Escherichia coli infection, wherein the abundance factor is represented as A factor = ( 3 ⁢ σ + A max ) A mean A max =max abundance of the organism b in a population sample, A mean =mean abundance of the organism b in the population sample, σ=standard deviation of the organism b in the population sample A capped = min ⁡ ( A b , A factor ) , if ⁢ T b > 0 max ⁡ ( A b , A factor ) , if ⁢ T b < 0 where A capped is the capped abundance of microbe b in the gut microbiota and T b denotes a bacteria type including beneficial and harmful of organism b.
  5. 5 . The processor implemented method of claim 1 , wherein a list of probiotic organisms stored in the database is used, if at least one probiotic organisms dataset is not available.
  6. 6 . The processor implemented method of claim 1 , wherein the genome scale metabolic models of microbes comprising the gut microbiota and the plurality of probiotic organisms available in the database is used.
  7. 7 . The processor implemented method of claim 1 , wherein the information regarding M-type of the microbes constituting the gut microbiota of the subject requiring personalization is obtained from at least one of database, a public repository, and a literature survey.
  8. 8 . A system comprising: a memory storing instructions; and one or more hardware processors, wherein the one or more hardware processors are configured by the instructions to: receive an information specific to one or more dietary preferences of a subject requiring personalization from one or more communication media; define a set of metabolic constraints for the one or more dietary preferences of the subject requiring personalization, wherein the set of metabolic constraints are used for one or more metabolic simulations; receive a test biological sample of the subject requiring personalization from the one or more communication media; extract DNA (Deoxyribonucleic Acid) from the test biological sample, using a DNA extraction technique; determine a microbial abundance of each microbe present in the test biological sample, from the microbial DNA which comprises the gut microbiota, using a set of probes specific to each of the microbes from stretches of DNA sequences sequenced from the microbial DNA extracted from the test biological sample, to obtain a microbial taxonomic profile associated with the test biological sample, wherein the microbial taxonomic profile associated with the test biological sample comprises microbial abundance of each of the microbes corresponding to a set of microbial DNA sequences present in the test biological sample; obtain an information specific to a plurality of probiotic organisms from at least one probiotic organisms dataset to evaluate the efficacy of the plurality of probiotic organisms; create a plurality of genome scale metabolic models of each of a plurality of microbes comprised in the gut microbiota of the subject and each of the plurality of probiotic organisms whose efficacy are be evaluated; assign M-type to the plurality of microbes comprised in the gut microbiota as T=+1 for at least one of beneficial microbes and commensal microbes and T=−1 for at least one of pathogenic microbes and opportunistic microbes; perform the one or more metabolic simulations to ascertain a mono-culture growth of each of the plurality of microbes comprising the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject; perform the one or more metabolic simulations to ascertain a co-culture growth of every pair of the microbes comprised in the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject, wherein the one or more metabolic simulations include in-silico metabolic simulations including a flux balance simulation for simulating metabolic behaviour of an organism under the set of metabolic constraints and simulating an effect of probiotic interventions on a plurality of gut microbiota compositions and the one or more dietary preferences of the subject; compute sustainability of each of the organism comprised in the plurality of probiotic organisms for evaluating the capability of each of the organism comprised in the plurality of probiotic organisms to proliferate within the gut, wherein the sustainability is defined as: D [ Sustainability ] a = ∑ ( G a p G a s ) * A b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preferences of the subject requiring personalization, ‘a’ is the probiotic organism, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, A b is the abundance of organism ‘b’ the gut microbiota of the subject requiring personalization, G a p is the growth rate of organism ‘a’ in co-growth condition with each of the microbes constituting the gut microbiota of the subject requiring personalization ‘b’ in ‘D’, G a s is the growth rate of organism ‘a’ in mono-culture condition in ‘D’; compute net-effect of each of the organism comprised in the plurality of probiotic organisms for quantifying an overall influence of the plurality of probiotic organisms as a score by summating a plurality of positive influences and a plurality of negative influences on each of the microbes comprised in the gut microbiota, wherein the plurality of positive influences comprise the growth and proliferation of at least one of the beneficial microbes and at least one of the commensal microbes and the plurality of negative influences comprise the growth and proliferation of at least one of the pathogenic microbes and at least one of the opportunistic microbes, wherein the net-effect reflects a net change in growth rates of the plurality of microbes in the gut microbiota as a consequence of administering a probiotic, and wherein the net-effect is defined as: D [ Net - effect ] a = ∑ ( G b p G b s ) * A b * T b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preference of the subject requiring personalization, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, G b p is the growth rate of organism ‘b’ in co-cultured condition with the probiotic organism ‘a’ in ‘D’, G b s is the growth rate of organism ‘b’ in mono-culture condition in ‘D’, A b is the abundance of organism ‘b’ in the gut microbiota of the subject requiring personalization, T b is the M-types of organism ‘b’, wherein the abundance of the organism ‘b’ in the gut microbiota of the subject requiring personalization is capped by an abundance factor to avoid over-estimating an influence including the sustainability or the net-effect due to large variation in the abundance of the microbe comprising the gut microbiota of the subject requiring personalization; select an efficacious probiotic organism based on at least one of the net-effect and the sustainability; design a personalized probiotic intervention regimen in the form of a probiotics treatment to the subject based on the selected efficacious probiotic organism; and administer the efficacious probiotic organism to the subject to improve the gut microbiota, wherein a dosage requirement for the efficacious probiotic organism is least when the efficacious probiotic organism is selected with a highest sustainability and the net-effect to deliver optimal therapeutic outcome, wherein when a probiotic organism with both the highest sustainability and the net-effect is not identified, then a probiotic organism with the highest sustainability and moderately high net-effect is selected and administration or the dosage requirement of the probiotic organism is the least of all available probiotic therapy options, wherein when the probiotic organism with a highest net-effect and a moderately high sustainability is selected, then the effect of the probiotic intervention is optimal of all the available probiotic therapy options, and the dosage requirement of the probiotic organism is more frequent or higher dosage, and wherein the system is implemented for the probiotics treatment to the subject requiring personalization based on the efficacious probiotic organism to improve the gut microbiota.
  9. 9 . The system of claim 8 , wherein the set of metabolic constraints is defined by (a) identifying and quantifying one or more nutrients constituting the one or more dietary preferences of the subject requiring personalization, and (b) converting the one or more quantified nutrients into the set of metabolic constraints, and wherein the set of metabolic constraints is defined based on the common diets of at least one of a region and an ethnic population to which the subject belongs to, if the one or more dietary preferences of the subject requiring personalization is not available and the information regarding the one or more dietary preferences of the subject is obtained from the database.
  10. 10 . The system of claim 8 , wherein the microbial abundance is determined by determining the microbial abundance of a predefined set of microbes using multiple DNA characterization techniques including Next Generation Sequencing protocols (NGS), a multiplex quantitative Polymerase Chain Reaction (qPCR) technique, nucleic acid hybridization techniques and the like, and wherein the microbial abundance of each microbe constituting the gut microbiota of the subject is defined as at least one of the mean microbial abundance of each microbe constituting the gut microbiota of an ethnic group and one or more lifestyle habits to which the subject belongs to, using information from the database, if the taxonomic profile associated with the test biological sample is not available.
  11. 11 . The system of claim 8 , wherein the microbial abundance is determined by determining the microbial abundance of a predefined set of microbes using multiple DNA characterization techniques including Next Generation Sequencing protocols (NGS), a multiplex quantitative Polymerase Chain Reaction (qPCR) technique, nucleic acid hybridization techniques and the like, and wherein the microbial abundance of each microbe constituting the gut microbiota of the subject is defined as at least one of the mean microbial abundance of each microbe constituting the gut microbiota of an ethnic group and one or more lifestyle habits to which the subject belongs to, using information from the database, if the taxonomic profile associated with the test biological sample is not available.
  12. 12 . The system of claim 8 , wherein a list of probiotic organisms stored in the database is used, if at least one probiotic organisms dataset is not available.
  13. 13 . The system of claim 8 , wherein the genome scale metabolic models of microbes comprising the gut microbiota and the plurality of probiotic organisms available in the database is used.
  14. 14 . The system of claim 8 , wherein the information regarding M-type of the microbes constituting the gut microbiota of the subject requiring personalization is obtained from at least one of database, a public repository and a literature survey.
  15. 15 . One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving an information specific to one or more dietary preferences of a subject requiring personalization from one or more communication media; defining a set of metabolic constraints for the one or more dietary preferences of the subject requiring personalization, wherein the set of metabolic constraints are used for one or more metabolic simulations; receiving a test biological sample of the subject requiring personalization from the one or more communication media; extracting DNA (Deoxyribonucleic Acid) from the test biological sample, using a DNA extraction technique; determining a microbial abundance of each microbe present in the test biological sample, from the microbial DNA which comprises a gut microbiota, using a set of probes specific to each of the microbes from stretches of DNA sequences sequenced from the microbial DNA extracted from the test biological sample, to obtain a microbial taxonomic profile associated with the test biological sample, wherein the microbial taxonomic profile associated with the test biological sample comprises microbial abundance of each of the microbes corresponding to a set of microbial DNA sequences present in the test biological sample; obtaining an information specific to a plurality of probiotic organisms from at least one probiotic organisms dataset to evaluate the efficacy of the plurality of probiotic organisms; creating a plurality of genome scale metabolic models of each of a plurality of microbes comprised in the gut microbiota of the subject and each of the plurality of probiotic organisms whose efficacy are be evaluated; assigning M-type to the plurality of microbes comprised in the gut microbiota as T=+1 for at least one of beneficial microbes and commensal microbes and T=−1 for at least one of pathogenic microbes and opportunistic microbes; performing the one or more metabolic simulations to ascertain a mono-culture growth of each of the plurality of microbes comprising the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject; performing the one or more metabolic simulations to ascertain a co-culture growth of every pair of the plurality of microbes comprised in the gut microbiota and each of the organism comprised in the plurality of probiotic organisms by using the defined set of metabolic constraints for simulating the one or more dietary preferences of the subject, wherein the one or more metabolic simulations include in-silico metabolic simulations including a flux balance simulation for simulating metabolic behaviour of an organism under the set of metabolic constraints and simulating an effect of probiotic interventions on a plurality of gut microbiota compositions and the one or more dietary preferences of the subject; computing sustainability of each of the organism comprised in the plurality of probiotic organisms for evaluating the capability of each of the organism comprised in the plurality of probiotic organisms to proliferate within the gut, wherein the sustainability is defined as: D [ Sustainability ] a = ∑ ( G a p G a s ) * A b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preferences of the subject requiring personalization, ‘a’ is the probiotic organism, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, A b is the abundance of organism ‘b’ the gut microbiota of the subject requiring personalization, G a p is the growth rate of organism ‘a’ in co-growth condition with each of the microbes constituting the gut microbiota of the subject requiring personalization ‘b’ in ‘D’, G a s is the growth rate of organism ‘a’ in mono-culture condition in ‘D’; computing net-effect of each of the organism comprised in the plurality of probiotic organisms for quantifying an overall influence of the plurality of probiotic organisms as a score by summating a plurality of positive influences and a plurality of negative influences on each of the microbes comprised in the gut microbiota, wherein the plurality of positive influences comprise the growth and proliferation of at least one of the beneficial microbes and at least one of the commensal microbes and the plurality of negative influences comprises the growth and proliferation of at least one of the pathogenic microbes and at least one of the opportunistic microbes, wherein the net-effect reflects a net change in growth rates of the plurality of microbes in the gut microbiota as a consequence of administering a probiotic, and wherein the net-effect is defined as: D [ Net - effect ] a = ∑ ( G b p G b s ) * A b * T b ; where ‘D’ is the set of metabolic constraints defining the one or more dietary preference of the subject requiring personalization, ‘b’ represents each of the microbes constituting the gut microbiota of the subject requiring personalization, G b p is the growth rate of organism ‘b’ in co-cultured condition with the probiotic organism ‘a’ in ‘D’, G b s is the growth rate of organism ‘b’ in mono-culture condition in ‘D’, A b is the abundance of organism ‘b’ in the gut microbiota of the subject requiring personalization, T b is the M-types of organism ‘b’, wherein the abundance of the organism ‘b’ in the gut microbiota of the subject requiring personalization is capped by an abundance factor to avoid over-estimating an influence including the sustainability or the net-effect due to large variation in the abundance of the microbe comprising the gut microbiota of the subject requiring personalization; selecting an efficacious probiotic organism based on at least one of the net-effect and the sustainability; designing a personalized probiotic intervention regimen in the form of a probiotics treatment to the subject based on the selected efficacious probiotic organism; and administering the efficacious probiotic organism to the subject to improve the gut microbiota, wherein a dosage requirement for the efficacious probiotic organism is least when the efficacious probiotic organism is selected with a highest sustainability and the net-effect to deliver optimal therapeutic outcome, wherein when a probiotic organism with both the highest sustainability and the net-effect is not identified, then a probiotic organism with the highest sustainability and moderately high net-effect is selected and administration or the dosage requirement of the probiotic organism is the least of all available probiotic therapy options, wherein when the probiotic organism with a highest net-effect and a moderately high sustainability is selected, then the effect of the probiotic intervention is optimal of all the available probiotic therapy options, and the dosage requirement of the probiotic organism is more frequent or higher dosage, and wherein a method is implemented for the probiotics treatment to the subject requiring personalization based on the efficacious probiotic organism to improve the gut microbiota.
  16. 16 . The one or more non-transitory machine-readable information storage mediums of claim 15 , wherein defining the set of metabolic constraints includes (a) identifying and quantifying one or more nutrients constituting the one or more dietary preferences of the subject requiring personalization, and (b) converting the one or more quantified nutrients into the set of metabolic constraints, and wherein the set of metabolic constraints is defined based on the common diets of at least one of a region and an ethnic population to which the subject belongs to, if the one or more dietary preferences of the subject requiring personalization is not available and the information regarding the one or more dietary preferences of the subject is obtained from the database.
  17. 17 . The one or more non-transitory machine-readable information storage mediums of claim 15 , wherein the step of determining the microbial abundance comprises determining the microbial abundance of a predefined set of microbes using multiple DNA characterization techniques including Next Generation Sequencing protocols (NGS), a multiplex quantitative Polymerase Chain Reaction (qPCR) technique, nucleic acid hybridization techniques and the like, and wherein the microbial abundance of each microbe constituting the gut microbiota of the subject is defined as at least one of the mean microbial abundance of each microbe constituting the gut microbiota of an ethnic group and one or more lifestyle habits to which the subject belongs to, using information from the database, if the taxonomic profile associated with the test biological sample is not available.
  18. 18 . The one or more non-transitory machine-readable information storage mediums of claim 15 , wherein a list of probiotic organisms stored in the database is used, if at least one probiotic organisms dataset is not available, wherein the genome scale metabolic models of microbes comprising the gut microbiota and the plurality of probiotic organisms available in the database is used, and wherein the information regarding M-type of the microbes constituting the gut microbiota of the subject requiring personalization is obtained from at least one of database, a public repository, and a literature survey.

Description

PRIORITY CLAIM This U.S. patent application claims priority under 35 U.S.C. § 119 to: Indian Patent Application number 202321064646, filed on Sep. 26, 2023. The entire contents of the aforementioned application are incorporated herein by reference. TECHNICAL FIELD The disclosure herein generally relates to the field of monitoring health of an individual, and, more particularly, to a system and method for determining a personalized probiotic therapeutic regimen. BACKGROUND The advent of metagenomics has led to significant advances in understanding how the bacterial microbiome is in symbiotic association with different body sites in humans. Gastro-intestinal (GI) tract is the major site of bacterial colonization, and it is well established that taxonomic constitution of gut microbiome influences gut health of the host. A microbiota residing within/on the human body is repeatedly being proven to be a significant modulator of health. While the bacteria harmful to gut represent the pathogens, the commensals represent beneficial gut bacteria. The human gut microbiota, which comprise of trillions of microbial cells, has been linked to host health status in several recent studies. Given the established role of gut microbiota in digestion, energy harvesting, immunity, etc., this link between the host health status and gut microbiota composition is not surprising. It is, however, important to note that the gut microbiota composition tends to vary even among apparently healthy individuals. In an earlier seminal work introducing the concept of ‘enterotypes’ in 2011, it was reported that (healthy) humans may be stratified into distinct categories (enterotypes) based on the intestinal microbiota makeup. While the original definition of only three ‘enterotype’ clusters and any expectation related to distinct boundaries of such clusters may be a generalization, several subsequent studies agree with the basic premise of ‘enterotypes’ and have shown that individuals of different ethnicities and those residing in different parts of the planet harbour different sets of microbes in their gut. Even, different dietary preferences are known to drive abundance of certain microbes in the gut. In this context, it may be expected that probiotic interventions on individuals with different gut-microbiota-types will lead to alternate outcomes. Probiotics are primarily bacteria or yeast strains that are used to restore/boost the beneficial functions of the gut microbiota, primarily through metabolic cooperation with the beneficial/commensal inhabitants of the gut or by sensitizing the host immune repertoire. While large scale longitudinal in vivo experiments to investigate the effect of different probiotic interventions among individuals of different ethnicity, dietary preferences, etc. would be costly and might raise ethical concerns, in vitro experiments are also infeasible due to the challenges associated with culturing most gut microbes in the laboratory. Consequently, the effect of probiotics in the context of different gut microbiota compositions and/or diet preferences has remained largely unexplored. Earlier studies have shown that the use of antibiotics to treat infections often cleanse the beneficial microbes from the gut as a collateral. This increases the chances of a secondary infection by an enteric pathogen or even an opportunistic pathogen. To mitigate this risk, physicians around the world are increasing prescribing probiotics alongside antimicrobial therapies for infections. However, there is a lack of clear guidelines on the choice and usage of probiotics for different clinical conditions. SUMMARY Embodiments of the present disclosure present technological improvements as solutions 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 determining a personalized probiotic therapeutic regimen is provided. The method includes receiving, via one or more hardware processors, an information specific to one or more dietary preferences of a subject requiring personalization from one or more communication media; defining, via the one or more hardware processors, a set of metabolic constraints for the one or more dietary preferences of the subject requiring personalization, wherein the set of metabolic constraints are used for one or more metabolic simulations; receiving, via the one or more hardware processors, a test biological sample of the subject requiring personalization from the one or more communication media; extracting, via the one or more hardware processors, DNA (Deoxyribonucleic Acid) from the test biological sample, using a DNA extraction technique; determining, via the one or more hardware processors, a microbial abundance of each microbe present in the test biological sample, from the microbial DNA which comprises a gut microbiota, using a set of probes specific to each of the microbes from stretches of DNA sequenc