Description:FIELD OF THE INVENTION
The present invention relates to the fields of computational biology, systems biology, microbiome science, precision nutrition, and health informatics. More particularly, it relates to computational and experimental systems and methods for designing personalized and precision combinatorial interventions for modulating microbial communities and associated metabolic functions to influence health and disease outcomes which integrates of food, nutrient, microbiome, metabolite and environmental data with genome-scale metabolic models (GSMMs) and flux balance analysis (FBA), knowledge graph-based modeling, simulation systems, and experimental validation frameworks to predict, optimize and validate individualized interventions.
BACKGROUND OF THE INVENTION
The human gut hosts trillions of diverse microbial communities that coevolved with humans and play a key role in metabolism as well as the development of the nervous and immune systems, thereby influencing various physical physiological processes. An imbalance of unhealthy and healthy microbes in the intestines may contribute to weight gain, high blood sugar, high cholesterol, and other disorders.and support metabolism, immunity, and nervous system development. The microbiota contributes to human metabolism and the development of the nervous and immune systems, thereby influencing physical physiological processes. An imbalance of unhealthy and healthy microbes in the intestines may contribute to weight gain, high blood sugar, high cholesterol and other disorders.
Despite its importance, the factors influencing gut microbial composition are beyond diet and genetics, are not fully understood or effectively managed.
Existing approaches primarily focus on identifying microbial compositions linked to various health conditions, along with the use of probiotics, dietary supplements and static knowledge-based recommendations. However, these approaches are limited by the lack of dynamic modelling of microbial interactions, absence of combinatorial optimization of nutrients and interventions, insufficient integration of environmental, lifestyle, and biochemical factors, and an inability to generate truly personalized intervention formulations.
Thus, there is need to develop precision intervention targeted strategies to monitor, maintain and restore a healthy microbial community dynamic by integrating personalized dietary interventions, probiotic/prebiotic supplementation, and consideration of environmental factors such as travel and geography, thereby supporting optimal metabolic, immune and neurological health.
The present invention provides a robust and precision-driven therapeutic and wellness outcomes by providing a closed-loop computational and experimental validation framework configured to model and optimize individualized microbiome interventions.
OBJECTS OF THE INVENTION
An object of the present invention is to provide a computational system for designing personalized and precision nutritional and microbiome-based interventions that optimize microbial community behaviour for improved health outcomes.
Another object of the present invention is to provide a method integrating a bacterial community network with nutrient-metabolite knowledge graphs for predicting microbial and metabolic responses to dietary interventions.
Yet another object of the present invention is to provide an integrated platform that combines nutrient, metabolite and microbial interaction knowledge graphs with advanced computational models to predict bacterial community responses and metabolite output.
Further object of the present invention is to provide a framework that enables the identification of functional foods, prebiotics, and nutrient-guided intervention strategies for health optimization and microbiome modulation.
Another object of the present invention is a precision intervention framework for modelling food, nutrient, metabolites and gut microbiome interactions for driving health and wellness applications through network-guided microbial modulation.
SUMMARY OF THE INVENTION
The present invention relates to computational and experimental systems and methods for designing personalized and precision combinatorial interventions for modulating microbial communities and associated metabolic functions to influence health and disease outcomes.
In an embodiment, the invention provides an integrated computational framework configured to model interactions among foods, nutrients, microbial communities, metabolites, environmental factors, and physiological parameters using genome-scale metabolic modeling (GSMM), flux balance analysis (FBA), knowledge graph-based modeling, and simulation systems.
In another embodiment, the invention receives as input microbial abundance profiles, nutrient compositions, food composition data, environmental constraints, physiological parameters, and user-defined microbial modulation objectives. The system constructs microbial community models using genome-scale metabolic reconstructions and simulates microbial growth behaviour, metabolite exchange, and nutrient utilization under defined dietary and environmental conditions.
In a further embodiment, the invention computes nutrient intervention profiles configured to induce desired directional shifts in microbial abundance and metabolic activity. The computed nutrient requirements are mapped to real-world food compositions to generate personalized dietary and combinatorial intervention formulations.
In another embodiment, the invention utilizes iterative optimization and comparison modules configured to evaluate predicted microbial community responses against target microbial states and refine intervention formulations based on match scores, metabolite fluxes, and community growth dynamics.
In yet another embodiment, the invention provides a closed-loop validation framework integrating computational predictions with experimental validation systems including microbial culture assays, metabolite profiling, nutrient extraction workflows, and in vitro or in vivo testing systems for validating predicted microbial and metabolic responses.
In a further embodiment, the invention enables identification and optimization of functional foods, prebiotics, nutrient combinations, microbiome-directed interventions, and metabolite-associated health modulation strategies for personalized health, wellness, disease management, and environmental adaptation applications.
BRIEF DESCRIPTION OF DRAWINGS
The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the annexed drawings wherein:
Fig. 1 illustrates the Food–Nutrient–Bacteria–Metabolite–Health Function axis used in the present invention for modelling interactions between dietary inputs, microbial communities, metabolite production, and health outcomes.
Fig. 2 illustrates the precision intervention framework emphasizing food-based and nutrient-driven modulation of the gut microbiome in comparison with conventional probiotic or isolated active ingredient approaches.
Fig. 3 illustrates a food–nutrient–microbe–metabolite–health knowledge graph integrated within the computational framework of the present invention.
Fig. 4 illustrates simulation of dietary nutrient inputs and their influence on microbial growth dynamics and metabolite production using genome-scale metabolic modelling.
Fig. 4 Combinatorial dietary intervention modelling workflow illustrating simulation of food-derived nutrient inputs, microbial growth dynamics, and metabolite production using genome-scale metabolic modelling and flux balance analysis.
Fig. 5 illustrates the end-to-end computational workflow of the present invention from microbiome input acquisition to precision diet generation.
Fig. 6: Initial microbiome processing and community metabolic model construction workflow for personalized dietary intervention prediction.
Fig. 7: Nutrient-to-food mapping and diet-constrained microbial community medium generation workflow.
Fig. 8: Iterative dietary simulation, microbial growth prediction, and diet optimization workflow for achieving target microbial abundance states.
Fig. 9: Proposed experimental validation workflow for correlating computationally predicted microbial metabolic responses with experimentally observed metabolite production using representative food-derived nutrients.
DETAILED DESCRIPTION OF THE INVENTION
The embodiments of the present disclosure are described herein below to enable a person of skill in the art to make and use the present disclosure.
The terms “for example” and “such as,” and grammatical equivalences thereof, the phrase “and without limitation” is understood to follow unless explicitly stated otherwise.
As used herein, the term “about” is intended to account for variations arising from experimental error or variations commonly accepted in the art for a numeric value. In one embodiment, such variation may be within ±10% of the stated numeric value. All measurements reported herein are understood to be modified by the term “about”, whether or not the term is explicitly recited, unless stated otherwise. Further, for the purposes of the present invention, ranges may be expressed as from “about” one particular value to “about” another particular value. When such a range is expressed, another embodiment includes values from the one particular value to the other particular value. The recitation of numerical ranges by endpoints includes all numerical values subsumed within the respective range.
As used herein, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.
Unless otherwise defined, all technical and scientific terms used herein shall have the same meanings as commonly understood by a person skilled in the art to which the present disclosure pertains. Methods and materials similar or equivalent to those described herein may be used in the practice or testing of the present disclosure, and suitable methods and materials known in the art may also be used. The materials, methods, and examples described herein are illustrative only and are not intended to be limiting in any manner. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. In the event of any conflict, the present specification, including the definitions provided herein, shall control.
Throughout this specification, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, shall be understood to imply the inclusion of a stated element, step, or group of elements or steps, but not the exclusion of any other element, step, or group of elements or steps.
The term “including” is intended to mean “including but not limited to”, and the terms “including” and “including but not limited to” are used interchangeably.
The embodiments described herein relate to a method and system that link the Food–Nutrient–Bacteria–Metabolite–Health Function axis.
The present invention provides an end-to-end computational framework that integrates real-world nutrient profiles from foods with gut microbial community models to predict:
(i) bacterial responses; and
(ii) metabolite outputs.
This framework enables the identification of functional foods and prebiotics, and supports nutrient-guided intervention strategies aimed at health optimization. Furthermore, these dietary intervention strategies are personalized based on individual gut microbiome signatures and microbial abundance profiles.
As illustrated in Fig. 1, the present invention focuses on precision interventions, specifically modelling food-derived metabolites and gut microbiome interactions for improving health and wellness outcomes, building upon network-guided personalization approaches for achieving optimal microbiome states.
The uniqueness of the present invention lies in the personalization of nutritional interventions based on the microbial profile of an individual user. The system computes a desired microbial outcome using an optimization function configured to emphasize food-based and nutrient-based prebiotic interventions. Unlike conventional approaches that rely primarily on isolated active ingredients or live biotherapeutic products (LBPs), the present framework prioritizes whole-food and nutrient-driven strategies for modulation of the gut microbiome, as illustrated in Fig. 2.
Information on Microbial community (MICOM), Genome-Scale Metabolic Models (GSMMs), Assembly of Gut Organisms through Reconstruction and Analysis (AGORA) and Flux-Balance Analysis (FBA)
MICOM is a Python‐based software package designed to perform constraint-based simulations of complex microbial ecosystems. Its principal capabilities include:
Multi-taxa integration: Combines multiple genome-scale metabolic models, each weighted by its relative abundance, into a single community optimization problem.
Minimal medium computation: Identifies the smallest set of nutrient import fluxes required to sustain a specified community growth rate.
Cooperative trade-off FBA: Executes a two-stage linear programming routine that (1) maximizes overall community biomass production and (2) enforces equitable growth constraints on individual taxa.
Flux extraction: Outputs community and per-taxon growth rates alongside detailed uptake and secretion fluxes for all metabolites.
GSMMs are comprehensive representations of an organism’s metabolic network, reconstructed from genomic and biochemical data. They consist of:
Metabolites (substrates, products, cofactors)
Reactions (enzymatic transformations and transport processes)
Stoichiometry and mass-balance constraints
AGORA is a curated repository of gut-associated GSMMs. Its value lies in:
Taxonomic breadth-models for the most prevalent human gut genera and species.
Standardization-uniform identifiers and nomenclature enable seamless combination of models.
Curation-automated drafts refined by manual gap-filling ensure biochemical consistency and completeness.
FBA is a mathematical optimization technique for predicting steady-state reaction fluxes in metabolic networks. Key features include:
Objective function: A linear combination of fluxes (commonly biomass synthesis) to be maximized or minimized.
Stoichiometric constraints: Enforced via the stoichiometric matrix (S), ensuring mass balance (S · v = 0).
Flux bounds: Upper and lower limits on each reaction flux, encoding nutrient availability and thermodynamic directionality.
Linear programming: Identification of the optimal flux distribution that satisfies all constraints while optimizing the objective.
In microbial community contexts, FBA underlies both single-taxa growth predictions and multi-taxa simulations, providing quantitative estimates of growth rates and metabolite exchange.
Architecture and Computational Workflow
In one embodiment, the present invention is configured to compute a precision dietary intervention for modulating a microbial community toward a desired microbial abundance state.
Given
Current Bacterial Abundance vector = Ac = (A1c,A2c,…,Anc), i=1 ∑nAic=1; where Aic is the measured (relative) abundance of microbes Mi.
Desired Abundance Vector = AT = (A1T,A2T,…,AnT) ,i=1∑nAiT=1; where AiT is the desired (relative) abundance of microbe Mi.
Food Nutrient Vector: F = (F₁, F₂, …, Fₘ); where Fₖ is the amount (e.g., flux) of nutrient k provided for each food item in food nutrient database
Intermediate Variables
Minimal medium
A baseline diet F = {F1,F2,...,Fm} where Fi is the flux (rate of input) of the ith metabolite.
Community Growth Rate
μ_comm: The overall growth rate of the entire microbial community (gDW/hr), computed by MICOM’s cooperative trade-off function under the given nutrient input.
Individual Microbial Growth Rates
G = {g₁, g₂, ..., gₙ} The predicted growth rates of each microbe Mᵢ within the community, based on its interactions and abundance under the given medium and diet.
Final Exchange Fluxes
V = {vᵢⱼ}: A flux matrix that describes for each microbe i:
The uptake (negative) or secretion (positive) of each metabolite j
These represent the final metabolic exchange activity under the
given food input and abundance configuration
Compute: The system computes an updated dietary intervention vector:
The new diet FNew = {FNew1,FNew2,...,FNewn}, where FNewi is the adjusted flux of the ith metabolite required to achieve your target growth rates.
For each metabolite i in final exchange fluxes F≠ 0, look up its associated disease Diseaseᵢ.
For example:
ith metabolite Flux ≠ 0 Type 2 Diabetes
Final Output: The system generates a precision intervention report comprising:
recommended food items and dietary compositions;
predicted microbial community growth rate μ_comm;
predicted per-taxon growth rates G;
predicted metabolic exchange flux distribution V; and
disease-metabolite or health-function association mappings.
Computational Workflow
The end-to-end computational workflow of the present invention is illustrated in Fig. 3 and Table 1. The workflow represents a closed-loop computational framework configured to generate personalized dietary intervention strategies for modulation of gut microbial composition and metabolite output. The workflow integrates microbiome abundance profiling, genome-scale metabolic modelling, flux balance analysis, nutrient optimization, and food composition mapping to compute real-world dietary recommendations capable of shifting a microbial community toward a desired target state.
In one embodiment, the computational workflow of the present invention comprises sequential modules configured to process microbiome abundance data, construct microbial community metabolic models, compute minimal nutrient requirements, map nutrients to food compositions, simulate dietary interventions, and iteratively optimize diet compositions toward a desired microbial target state. Each component of the workflow is described below in detail.
Table 1: Components of the computational framework for personalized dietary interventions

Component Function Inputs Outputs
1. Data Input Module • Reads and validates all raw input files.
• Specifically:   – user gut-microbe abundance CSV (bacterial taxon & Current Abundance)   – AGORA metadata + JSON models folder for GSMM •abundance_current.csv (genus × current abundance)
•abundance_target.csv (genus × target abundance)
• AGORA model folder (one JSON per genus) • Parsed: Current abundances 𝐀ᶜ (genus → abundance)
• Parsed: Target abundances 𝐀ᵀ (genus → abundance)
• AGORA model list {M₁, M₂, …, Mₙ}
2. Preprocessing & Fuzzy Matcher • Cleans genus names and ensures each genus in 𝐀ᶜ (and 𝐀ᵀ) matches an available AGORA model.
• Uses fuzzy string matching to resolve minor naming differences. • 𝐀ᶜ (genus names + abundances)
• AGORA model manifests (available genus names in AGORA folder) • List of matched pairs (genus ↔ AGORA model ID)
• Cleaned abundance tables 𝐀ᶜ_clean, each with confirmed AGORA associations
3. Community Construction Module • Instantiates a MICOM Community object by loading each AGORA JSON model and assigning it a weight equal to its current abundance (𝐀ᶜ) community₁. • Matched AGORA models for each genus in 𝐀ᶜ ( {M₁, M₂, …, Mₙ} )
• Current abundance weights 𝐀ᶜ (genus → abundance) • community₁ (MICOM Community object #1) with: – Reaction stoichiometry of each genus model – Biomass reactions for each genus – Abundance weights = 𝐀ᶜ
4. Baseline Minimal Media Computation • Runs MICOM’s minimal_medium on community₁ to minimize the sum of all nutrient uptake fluxes, subject to:   1. Each genus’s biomass ≥ (its target fraction) using 𝐀ᵀ.  
• Output is the “minimal media flux vector” 𝐅ᴮ, i.e. the exact uptake rate of each exchange metabolite required to reach the specified target growth proportions. • community₁
• Target abundance 𝐀ᵀ (genus → desired fraction) • Optimization objective: minimize Σₖ  List of minimum set metabolic reactions and their fluxes to achieve the target growth rate.
5. Nutrient-to-Food Mapping Module • Takes 𝐅ᴮ (list of exchange reactions + uptake rates) and queries food nutrient database to find candidate foods rich in required metabolite. • 𝐅ᴮ = { (EX_r₁ = f₁), (EX_r₂ = f₂), …, (EX_rₘ = fₘ) }
• food nutrient database (repository of “nutrient ↔ food ↔ flux”) • For each EX_rᵢ: a table { food_id, mmol or μmol per g) }
• Ranked candidate-food list (top N foods for all EX_rᵢ)
6.Community medium update Update the community with fetched nutrient composition for the selected food item in the medium with the minimal set of reactions required to trigger the growth rate calculation. Food nutrient composition with fluxes fetched from food nutrient database • Medium updated with food nutrient composition for the community to feed on.
7. Diet Simulation Module • Runs MICOM’s cooperative_tradeoff FBA on community₂ to:   (a) maximize community-level growth given the fixed dietary fluxes.
• Extracts per-genus growth rates G = {g₁, g₂, …, gₙ} and the full exchange-flux matrix V. • community₂ (with exchange bounds from foods, from Module 7) • Cooperative tradeoff parameter α (e.g., 0.8 to enforce 80 % of maximal community growth before minimizing flux) • Community growth rate μ_comm₂ • Per-genus growth rates G = { gᵢ } (i = 1…n)
• Final exchange flux matrix V (size n_genus × p_metabolites), which shows uptake/secretion of each metabolite by each genus
8. Results Aggregation & Comparison Module • Compares (a) predicted per-genus growth ratios under the diet (ŝᵢ = gᵢ/∑ⱼ gⱼ) vs. (b) target abundances 𝐀ᵀ. • Computes a “distance metric” D, between ŝ = (gᵢ/∑ⱼ gⱼ) and 𝐀ᵀ.
• If D ≤ tolerance τ, the diet is accepted. Otherwise, the module identifies under-supplied exchange reactions (Δ fₖ = fᴮₖ – ∑ᵢ vᵢₖ) and triggers an iteration back to Module 6 to refine the food set. • G (per-genus growth rates under diet) • Target abundances 𝐀ᵀ (genus → desired fraction) • Similarity score S = similarity(ŝ, 𝐀ᵀ)
• Decision flag: “Accept diet” or “Refine food set”
• If accepted: Final Diet Report (foods + serving sizes, predicted G, V, D) • If refine: List of under-supplied metabolites {Δ fₖ > 0} → feedback to Module 6
Component 1: Data Input Module
The objective of this component is to ingest, validate, and organize all input data required for construction of the microbial community model and downstream dietary optimization workflow.
Inputs
The Data Input Module receives:
(i) a current microbiome abundance dataset representing the microbial composition of an individual user;
(ii) a target microbiome abundance dataset representing a desired microbial composition state;
(iii) a repository of genome-scale metabolic models corresponding to microbial taxa; and
(iv) one or more food composition databases containing nutrient-level composition information for food items.
In one embodiment, the current microbiome abundance dataset is provided as a structured comma-separated values (CSV) file containing microbial genus or species names and their measured relative abundances.
An example structure is shown below:
• “Genus_Name”
• “Current_Abundance”
Similarly, the target abundance dataset may comprise:
• “Genus_Name”
• “Target_Abundance”
wherein the target abundance represents a desired relative abundance value for a corresponding microbial taxon.
The module further loads AGORA genome-scale metabolic reconstruction files corresponding to microbial taxa identified within the microbiome dataset. In one embodiment, each microbial taxon is associated with a corresponding JSON-format AGORA metabolic model.
The module additionally initializes access to one or more nutrient composition repositories, including food-metabolite mapping databases and nutrient ontology datasets. In one embodiment, the Virtual Metabolic Human (VMH) database is initialized for downstream nutrient-to-food mapping operations.
Process
The module performs the following operations:
Reading and parsing microbiome abundance files.
Validating file structure and taxonomic formatting.
Normalizing abundance values where required.
Loading AGORA model manifests and associated metabolic model files.
Establishing connections to nutrient composition databases.
Creating internal data structures for downstream computational modules.
Outputs
The Data Input Module outputs:
A current microbial abundance vector: 𝐀ᶜ = { genusᵢ → current_abundanceᵢ }
A target microbial abundance vector: 𝐀ᵀ = { genusᵢ → target_abundanceᵢ }
A list of AGORA metabolic model files corresponding to microbial taxa.
A database connection or client interface for nutrient-to-food mapping.
Structured in-memory representations of all validated input datasets for downstream processing.
As illustrated in Fig. 4, the Data Input Module forms the foundational layer of the computational workflow by organizing microbiome, metabolic model, and nutrient composition data into standardized structures suitable for microbial community modelling and dietary optimization.
Component 2: Preprocessing and Fuzzy Matching Module
The objective of this component is to ensure accurate correspondence between microbial taxa identified in the microbiome abundance datasets and genome-scale metabolic models available within the AGORA repository.
Inputs
The Preprocessing and Fuzzy Matching Module receives:
Current microbial abundance vector 𝐀ᶜ;
Target microbial abundance vector 𝐀ᵀ;
AGORA model manifests containing available microbial taxa and corresponding model identifiers.
Process
In one embodiment, microbial genus or species names present in the microbiome abundance datasets may differ from AGORA naming conventions due to typographical variations, abbreviations, formatting inconsistencies, or taxonomic nomenclature differences.
Accordingly, the module performs preprocessing and taxonomic harmonization operations including:
Removal of duplicate entries.
Standardization of taxonomic naming formats.
Conversion of special characters and whitespace normalization.
Elimination of invalid or incomplete taxonomic records.
The module subsequently performs taxonomic matching between microbiome taxa and AGORA model identifiers.
In one embodiment, exact string matching is first attempted between each microbial taxon and AGORA model names.
If an exact match is not identified, the module executes a fuzzy matching algorithm configured to identify the closest matching AGORA taxon based on string similarity metrics.
In one embodiment, the module executes a fuzzy matching function configured to identify the closest matching AGORA taxon based on similarity between microbial taxonomic names.
Where multiple candidate matches exceed a predefined similarity threshold, the module may:
(i) select the highest-ranked match;
(ii) flag the taxon for manual review; or
(iii) exclude the taxon from downstream analysis.
Unresolved taxa are optionally logged into an exception report for quality control and reproducibility.
Outputs
The module outputs:
A cleaned microbial abundance dataset: 𝐀ᶜ_clean
A cleaned target abundance dataset: 𝐀ᵀ_clean
A validated mapping table linking microbial taxa to AGORA metabolic models.
A filtered list of recognized microbial taxa suitable for community construction.
A log file containing unresolved or excluded taxa.
As illustrated in Fig. 4, this module ensures taxonomic consistency between microbiome input datasets and genome-scale metabolic model repositories prior to microbial community construction.
Component 3: Community Construction Module
The objective of this component is to construct a microbial community metabolic model representing the microbiome composition of the individual user.
Inputs
The Community Construction Module receives:
• Cleaned microbial abundance dataset 𝐀ᶜ_clean;
• Validated genus-to-model mapping table;
• AGORA genome-scale metabolic model files corresponding to identified microbial taxa.
Process
In one embodiment, the module instantiates a microbial community object using MICOM by integrating multiple genome-scale metabolic models corresponding to microbial taxa present in the microbiome abundance dataset.
For each microbial taxon identified in 𝐀ᶜ_clean:
The corresponding AGORA genome-scale metabolic model is loaded.
The microbial taxon is assigned a weighting factor proportional to its measured abundance within the microbiome dataset.
Biomass reactions associated with each microbial model are scaled according to relative abundance values.
The module subsequently combines all microbial metabolic models into a unified community-level stoichiometric framework.
In one embodiment, MICOM constructs a community stoichiometric matrix representing all microbial reactions, exchange reactions, biomass equations, and interspecies metabolic interactions.
The resulting community model enables simulation of:
• nutrient competition;
• metabolite exchange;
• cooperative metabolic interactions;
• cross-feeding mechanisms; and
• community-wide biomass optimization.
At this stage, exchange reaction bounds may remain unconstrained or minimally constrained to permit downstream minimal-medium optimization.
Outputs
The module outputs:
A MICOM microbial community object representing the user-specific microbiome community;
A community-level stoichiometric matrix;
Weighted microbial biomass functions;
Community exchange reaction definitions;
A simulation-ready microbial community metabolic framework.
In one embodiment, the community model serves as the foundational simulation object for subsequent minimal-medium optimization, dietary simulation, and metabolite prediction steps.
As illustrated in Fig. 4, the Community Construction Module transforms microbiome abundance information into a computationally executable microbial ecosystem model suitable for constraint-based metabolic simulations.
Component 4: Baseline Minimal Media Computation Module
The objective of this component is to compute a minimal nutrient medium capable of supporting microbial growth while driving the microbial community toward the desired target abundance configuration.
Inputs
The Baseline Minimal Media Computation Module receives:
The MICOM community object constructed in Component 3;
The cleaned target abundance vector 𝐀ᵀ_clean;
Community biomass constraints and optimization parameters.
Process
In one embodiment, the module applies constraint-based metabolic optimization to determine the smallest set of nutrient uptake fluxes capable of supporting microbial growth under the specified target abundance conditions.
The module imposes target abundance constraints on microbial biomass reactions associated with each microbial taxon present within the community model.
For each microbial taxon i:
the corresponding biomass reaction is identified; and
a lower bound constraint proportional to the desired target abundance Aᵀᵢ is applied.
The module subsequently executes MICOM’s minimal_medium optimization routine configured to minimize the total nutrient uptake flux required to sustain microbial growth.
In one embodiment, the optimization objective comprises minimizing the cumulative sum of exchange uptake fluxes across all metabolites while satisfying community growth constraints and target abundance requirements.
The optimization routine computes:
(i) the minimal set of exchange metabolites required by the microbial community;
(ii) the uptake flux associated with each exchange metabolite; and
(iii) the resulting community growth rate.
Outputs
The module outputs:
A baseline minimal-medium flux vector: 𝐅ᴮ = { EX_r₁ → f₁, EX_r₂ → f₂, …, EX_rₘ → fₘ }
where EX_rₖ represents an exchange reaction and fₖ represents the corresponding nutrient uptake flux.
A predicted community growth rate μ_comm under the computed minimal medium.
A list of metabolites essential for achieving the target microbial abundance configuration.
In one embodiment, the computed minimal-medium flux vector serves as the nutritional requirement profile used for downstream nutrient-to-food mapping operations.
As illustrated in Fig. 4, the Baseline Minimal Media Computation Module identifies the minimal metabolic requirements necessary to transition the microbial community toward the target microbial abundance state.
Component 5: Nutrient-to-Food Mapping Module
The objective of this component is to translate computed nutrient flux requirements into real-world food recommendations capable of supplying the required metabolites.
Inputs
The Nutrient-to-Food Mapping Module receives:
The baseline minimal-medium flux vector 𝐅ᴮ generated in Component 4;
One or more food composition databases;
Nutrient-to-food association datasets.
Process
For each exchange metabolite identified in the minimal-medium flux vector 𝐅ᴮ:
The corresponding metabolite identifier is mapped to nutrient composition records within a food database.
Foods containing the required nutrient or metabolite are identified.
Nutrient quantities associated with each food item are extracted.
In one embodiment, the system queries a nutrient composition database including food-metabolite relationships and nutrient concentrations.
For each food item identified, the module computes a similarity or match score representing the degree to which the food composition satisfies the required nutrient flux.
The match score may be calculated based on:
nutrient concentration;
proportional contribution toward required uptake fluxes;
nutrient completeness;
dietary constraints; or
combinations thereof.
The module subsequently ranks food items according to their ability to satisfy the computed microbial nutrient requirements.
In one embodiment, multiple food items may be combined to collectively satisfy the minimal-medium flux requirements.
Outputs
The module outputs:
A nutrient-to-food mapping dictionary associating exchange metabolites with candidate food items;
Ranked food recommendation lists for each required metabolite;
Food composition vectors representing nutrient flux contributions from selected foods;
Match scores representing dietary suitability for microbiome modulation.
In one embodiment, the module converts abstract microbial nutrient requirements into real-world dietary intervention candidates suitable for downstream simulation and optimization.
As illustrated in Fig. 5, the Nutrient-to-Food Mapping Module bridges microbial metabolic modelling with food composition databases to generate biologically relevant dietary recommendations.
Component 6: Community Medium Update Module
The objective of this component is to transform selected food compositions into a simulation-compatible microbial growth medium for downstream microbial community simulations.
Inputs
The Community Medium Update Module receives:
The microbial community object generated in Component 3;
Selected food composition vectors generated in Component 5;
Nutrient flux values corresponding to selected food items.
Process
In one embodiment, the module converts food-derived nutrient compositions into exchange flux constraints compatible with MICOM-based microbial community simulations.
For each selected food item:
Nutrient composition values are extracted from the food composition database.
Nutrients are mapped to corresponding microbial exchange reactions.
Nutrient concentrations are converted into exchange flux bounds.
Exchange reactions associated with the microbial community model are updated with food-derived flux values.
The module further identifies the minimal set of exchange reactions and nutrient fluxes necessary to trigger microbial growth simulations under the selected dietary conditions.
Environmental or contextual constraints may additionally be incorporated into the simulation medium, including:
oxygen availability;
anaerobic conditions;
pH conditions;
micronutrient supplementation;
bioactive compounds; and
dietary restrictions.
In one embodiment, the module generates a unified simulation-compatible medium representing real-world dietary intake conditions.
Outputs
The module outputs:
An updated microbial community object with food-derived nutrient constraints;
A simulation-compatible microbial growth medium;
Exchange reaction bounds corresponding to selected dietary compositions;
A diet-constrained microbial community simulation framework.
As illustrated in Fig. 6, the Community Medium Update Module converts real-world food compositions into microbial nutrient environments suitable for constraint-based metabolic simulations.
Component 7: Diet Simulation Module
The objective of this component is to simulate microbial community growth and metabolite exchange behavior under the selected dietary intervention conditions.
Inputs
The Diet Simulation Module receives:
The updated diet-constrained microbial community object generated in Component 6;
Cooperative trade-off optimization parameters;
Community-level growth constraints.
Process
In one embodiment, the module executes MICOM cooperative trade-off flux balance analysis simulations to predict microbial growth and metabolic exchange activity under the selected dietary environment.
The module performs a two-stage optimization process comprising:
Stage 1: Community Biomass Optimization
The system maximizes total community biomass production subject to dietary nutrient constraints and microbial stoichiometric constraints.
Stage 2: Cooperative Trade-Off Optimization
The system constrains the microbial community to maintain a predefined fraction of maximal community growth while minimizing total flux distributions across microbial taxa.
In one embodiment, the cooperative trade-off parameter α defines the minimum fraction of maximal community growth to be maintained during optimization.
The simulation generates:
community-level growth predictions;
individual microbial growth predictions;
metabolite uptake fluxes;
metabolite secretion fluxes; and
interspecies metabolic exchange behavior.
The module additionally extracts exchange flux matrices representing metabolite consumption and production patterns across microbial taxa.
Outputs
The module outputs:
Predicted microbial community growth rate: μ_comm₂
Predicted microbial growth rate vector: G = { g₁, g₂, …, gₙ }
Exchange flux matrix: V = { vᵢⱼ }
where vᵢⱼ represents uptake or secretion fluxes associated with microbial taxon i and metabolite j.
Predicted metabolite exchange profiles for the selected dietary intervention.
In one embodiment, the Diet Simulation Module enables in silico evaluation of dietary interventions prior to real-world implementation.
As illustrated in Fig. 6, the Diet Simulation Module predicts microbial community dynamics and metabolic behavior resulting from food-derived nutrient interventions.
Component 8: Results Aggregation & Comparison Module
In one embodiment, the results aggregation and comparison module is configured to compare simulated microbial growth outcomes against the desired target microbial abundance profile and determine whether the computed dietary intervention satisfies the optimization criteria.
Inputs
The Results Aggregation and Comparison Module receives:
Predicted microbial growth rate vector G generated in Component 7;
Predicted community growth rate μ_comm₂;
Exchange flux matrix V;
Target microbial abundance vector 𝐀ᵀ_clean;
Baseline minimal-medium flux vector 𝐅ᴮ.
Process
1. Conversion of growth rates into simulated relative abundance profile
The module computes the total microbial growth:
"total_growth"=g_1+g_2+...+g_n

For each microbial genus i, the simulated relative abundance (S_i ) ̂is computed as:
(S_i ) ̂=g_i/(∑_j▒g_j )

This generates a predicted microbial abundance distribution under the simulated dietary intervention.
2. Distance computation between predicted and target abundance profiles
For each microbial genus i, the module computes the deviation between the predicted abundance (S_i ) ̂and the target abundance A_i^T:
∣(S_i ) ̂-A_i^T∣
The overall distance metric Dis then calculated as:
D=∑_i▒∣ (S_i ) ̂-A_i^T∣

A predefined tolerance threshold τis used to determine whether the predicted microbial state sufficiently matches the desired target state.
3. Diet acceptance or refinement
If:
D≤τ
the dietary intervention is accepted.
If:
D>τ

the module identifies under-supplied metabolites by comparing actual metabolite uptake against the baseline minimal-medium requirements.
For each exchange reaction k:
〖"actual_uptake" 〗_k=∑_i▒v_(i,k)
The nutrient gap is calculated as:
〖"gap" 〗_k=f_k^B-〖"actual_uptake" 〗_k
Where:
f_k^Brepresents the baseline minimal-medium flux requirement for metabolite k; and
〖"actual_uptake" 〗_krepresents the simulated uptake under the selected diet.
If:
〖"gap" 〗_k>0
the metabolite is classified as under-supplied.
The identified nutrient gaps are propagated back to the nutrient-to-food mapping and medium update modules for iterative refinement of the dietary composition until the predicted microbial abundance profile converges toward the desired target state.
Outputs
The module outputs:
A similarity or distance score representing correspondence between simulated and target microbial abundance profiles;
A decision flag indicating:
accepted dietary intervention; or
iterative refinement required;
A list of under-supplied metabolites requiring dietary adjustment;
A refined nutrient optimization profile for subsequent simulation iterations.
In one embodiment, the module enables closed-loop optimization of dietary interventions through iterative refinement cycles until a predefined similarity threshold is achieved.
As illustrated in Fig. 6, the Results Aggregation and Comparison Module functions as the decision-making and optimization feedback engine of the computational workflow.
Real-World Food Integration and Experimental Validation
The computational framework of the present invention is configured to directly process real-world food composition data rather than isolated nutrient abstractions alone.
In one embodiment, the system receives structured nutritional composition datasets representing complete food items, including:
macronutrients;
micronutrients;
phytonutrients;
polyphenols;
flavonoids;
antioxidants; and
bioactive compounds.
For example, a structured food composition file representing an apple may comprise nutrient quantities corresponding to carbohydrates, dietary fibre, vitamins, minerals, catechins, chlorogenic acid, quercetin, and additional food-derived compounds.
The framework maps individual nutrients and metabolites present within food items to microbial metabolic pathways and exchange reactions represented within the genome-scale metabolic models.
Using flux balance analysis and microbial community simulations, the system predicts:
microbial growth responses to food intake;
community-level metabolic shifts;
metabolite production profiles;
nutrient uptake patterns; and
metabolite exchange behavior between microbial taxa.
In one embodiment, the framework additionally identifies specific nutrients or bioactive compounds responsible for predicted microbial or metabolic changes.
For example, the framework may determine whether a compound such as quercetin contributes to:
increased growth of beneficial microbial taxa;
suppression of pathogenic taxa;
altered metabolite secretion profiles; or
changes in predicted health-associated metabolic pathways.
The ability to directly process complete food compositions enables the invention to generate realistic dietary intervention strategies with improved real-world applicability compared to systems limited to isolated nutrient supplementation approaches.
Table 2 illustrates an example structured nutritional composition dataset used within the computational framework for food-derived nutrient modelling and microbial response prediction.
To experimentally validate the computational framework predictions regarding gut microbial metabolism of specific nutrients, the present invention further comprises an experimental validation workflow configured to compare in silico predictions with experimentally observed microbial and metabolite responses.
In one embodiment, quercetin is used as a representative phytonutrient for validation of the computational framework.
The experimental workflow comprises:
nutrient extraction and validation;
mono-culture and co-culture microbial assays using quercetin-containing media; and
metabolite profiling and computational model correlation analysis.
In one embodiment, quercetin is extracted from food sources including apple peel and purified using standard extraction and chromatographic purification methods. The extracted compound may be characterized using analytical techniques including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), or combinations thereof.
Selected gut microbial strains including Escherichia coli, Lactobacillus plantarum, and Bifidobacterium animalis
may be cultured under controlled anaerobic conditions in minimal media supplemented with quercetin.
Microbial growth kinetics, metabolite production, nutrient uptake behavior, and microbial interaction patterns are monitored and compared against computationally predicted growth rates and metabolic exchange fluxes generated by the framework.

Table 2: Nutritional Composition of Food Items (per 100 g)

Food Item Calories (Kcal) Protein (g) Total Fat (g) Carbohydrates (g) Fibre (g) Vitamins (mg) Minerals (mg)
Apple 51 0.6 0.5 11.6 1.2 C: 0.006, A: 0.014, K: 0.005 K: 100, Ca: 5, P: 8, Mg: 4
Carrot 41 0.8 0.14 9.08 2.7 A: 5,
C: 2.05, B6:0.115, B2:0.116, B3:0.742 P: 237, Ca: 42, Mg:11.1,
Fe: 0.09
Almonds 626 21.4 51.1 20 10.8 B6:0.101, B7: 0.057 Mg: 258, Ca: 254, K: 733, P: 503, Fe: 3.74
Kale 43 2.92 1.49 4.42 4.1 K: 0.39,
A: 0.2,
C: 93.4, B6: 0.14, E: 0.66 Ca: 254, K: 348, Mg: 32.7,
Fe: 1.6
Bell Peppers 23 0.72 0.11 4.78 0.9 C: 99.5, B6: 0.247 K: 163, Mg: 9, Fe: 0.19, P: 17
Kiwi 65 1.01 0.6 13.8 2.1 C: 58.8, B6: 0.06 Ca: 24, K: 302, Mg: 14.8,
Fe: 0.04
Sweet Potato 79 1.58 0.3 17.3 4.4 C: 14.8 Ca: 22, P: 486, Mg: 19.1, Fe: 0.4
Fenugreek 323 23 6.4 58.4 24.6 C: 3, B6: 0.6, B9: 0.05 Fe:33.53, Mg: 191, K: 770, Zn: 2.5, Ca: 176
Lychee 66 0.83 0.44 16.5 1.3 C: 71.5, B3: 0.3 Ca: 5, Mg: 10, K: 171
Peach 46 0.9 0.27 10.1 1.5 C: 4.1, A: 0.02, K: Ca: 4, Mg: 8, K: 122, P: 22

Culture supernatants may further be analyzed using targeted metabolomics to identify predicted metabolic products associated with quercetin metabolism, including compounds such as 2-Protocatechuoylphloroglucinol carboxylic acid.
The experimental workflow thereby enables validation of:
predicted microbial growth behavior;
predicted metabolite production profiles;
predicted nutrient uptake fluxes; and
predicted metabolite exchange patterns generated by the computational framework.
Fig. 5 illustrates a representative experimental validation workflow integrating nutrient extraction, microbial culturing, metabolite profiling, and computational correlation analysis for validating computational predictions generated by the invention.
The experimental validation workflow described herein serves as a proof-of-concept embodiment demonstrating integration of computational microbiome modelling with experimental validation using food-derived nutrients and microbial metabolic assays.
The structured Nutritional Composition Dataset for Food-Derived Nutrient Modelling is shown in Table 2 and Table 3.
Table 3: Antioxidants, Polyphenols and Quercetin Content (per 100 g)
Food Item Antioxidants / Polyphenols (mg) Quercetin (mg)
Apple Catechins: 9.02; Chlorogenic acid: 8.65 4-5
Carrot Beta-carotene: 8.25; Alpha-carotene: 3.45 0.20
Almonds Quercetin: 0.72; Kaempferol: 0.60;
Epicatechins: 1.93; Catechins: 0.85 0.02
Kale Lutein: 39 23
Bell Peppers Lutein: 0.116; Zeaxanthin: 0.562;
Beta-carotene: 2.167 50.63
Kiwi Flavonoids: 0.70; Phenolic acids: 0.06 1.06
Sweet Potato Anthocyanins: 0.86 4.78
Fenugreek Saponins: 8.46; Flavonoids: 7.27 1.56
Lychee Not specified Lychee pulp: 0.325;
Lychee leaves: 1.47
Peach
Quercetin: 5.4; Beta-carotene: 11.6 5.4

In one embodiment, the computational framework utilizes structured nutritional composition datasets comprising macro-nutrient, micro-nutrient, phytonutrient, antioxidant, flavonoid, polyphenol, and bioactive compound information corresponding to food items intended for microbial metabolic modelling.
An example structured food composition dataset is illustrated below-
The structured food composition datasets are configured to enable mapping of food-derived nutrients and metabolites to microbial exchange reactions and metabolic pathways represented within the genome-scale metabolic models.
In one embodiment, each nutrient entry within the dataset is converted into metabolite exchange flux constraints compatible with MICOM-based microbial community simulations and flux balance analysis workflows.
The framework thereby enables simulation of microbial responses to complete food compositions rather than isolated nutrient supplementation alone, thereby improving biological relevance and real-world applicability of the generated dietary intervention strategies.
Additional Embodiments and Applications of the Invention
In one embodiment, the computational framework of the present invention may be configured for personalized nutritional recommendation systems intended to improve gut microbial balance, metabolic health, gastrointestinal health, immune modulation, neurological health, cardiovascular health, or combinations thereof.
In one embodiment, the framework may be configured to identify dietary interventions capable of:
increasing beneficial microbial taxa;
reducing pathogenic or opportunistic microbial taxa;
modulating microbial metabolite production;
increasing production of beneficial metabolites including short-chain fatty acids;
reducing production of disease-associated metabolites;
or improving predicted microbial ecosystem stability.
In one embodiment, the framework may further be configured to simulate the effect of:
individual nutrients;
combinations of nutrients;
functional foods;
prebiotics;
polyphenols;
phytonutrients;
fermented foods;
synbiotic compositions; or
dietary patterns.
In one embodiment, the framework may be integrated with:
microbiome sequencing pipelines;
metagenomic analysis systems;
metabolomic analysis platforms;
wearable health monitoring systems;
clinical diagnostic systems; or
personalized nutrition recommendation platforms.
In one embodiment, the system may additionally incorporate:
host physiological parameters;
disease biomarkers;
blood metabolite profiles;
inflammatory markers;
dietary history;
lifestyle information; or
longitudinal microbiome measurements.
The computational framework may further be configured to predict:
disease-associated metabolite signatures;
microbiome-associated health outcomes;
therapeutic dietary responses;
microbial ecosystem resilience;
nutrient deficiency-associated microbial shifts; or
personalized nutritional optimization strategies.
In one embodiment, the invention may be implemented as:
a cloud-based computational platform;
a software application;
a web-based personalized nutrition engine;
a clinical decision-support system;
a microbiome analytics platform; or
a standalone computational modelling system.
The invention is not limited to the specific embodiments described herein, and various modifications, substitutions, additions, and alternative implementations may be made without departing from the scope of the present disclosure.
Although the invention has been described with reference to specific embodiments and representative examples, it shall be understood that the scope of the invention is defined by the appended claims and encompasses all equivalents and variations falling within the spirit and scope of the invention.

, Claims:WE CLAIM:
1. An end-to-end computational framework for predicting and optimizing personalized dietary interventions using microbial composition, food composition data, targeted microbial outcomes, the framework comprising:
a) receiving a bacterial abundance dataset representing the taxonomic composition of a gut microbiome sample at genus or species level resolution;
b) receiving a structured food composition data comprising macronutrient, micronutrient, and phytonutrient, polyphenols, antioxidants, or combinations thereof;
c) receiving a directive vector representing a desired shift in the microbial abundance
d) constructing a microbial community metabolic model using genome-scale metabolic models (GSMMs) corresponding to microbial taxa present in the microbial abundance dataset;
e) performing constraint-based flux balance analysis (FBA) on the microbial community metabolic model to:
• simulate nutrient uptake and metabolite exchange fluxes;
• compute microbial growth rates; and
• compute community-level metabolic interactions;
f) computing a minimal nutrient medium required to shift the microbial community to achieve the directive vector;
g) mapping computed nutrient requirements to real-world food composition data using a food nutrient database;
h) generating a diet-constrained microbial community model using nutrient compositions derived from selected food items;
i) iteratively simulating microbial growth and metabolite exchange under dietary nutrient constraints until a predefined similarity criterion between predicted microbial abundance and target microbial abundance is achieved; and
j) generating a precision dietary intervention report comprising recommended food compositions, predicted microbial growth responses, metabolite flux distributions, and disease-metabolite or health-function association mappings.
2. The end-to-end computational framework as claimed in claim 1, wherein the microbial community metabolic model is generated using MICOM and genome-scale metabolic models obtained from the AGORA repository.
3. The end-to-end computational framework as claimed in claim 1, wherein the framework performs cooperative trade-off flux balance analysis to optimize community-level microbial growth while maintaining individual microbial growth constraints.
4. The end-to-end computational framework as claimed in claim 1, wherein the framework performs taxonomic matching between microbial abundance datasets and corresponding microbial metabolic models.
5. The method as claimed in claim 1, wherein the framework computes differences between predicted nutrient uptake fluxes and baseline minimal nutrient requirements and iteratively refines dietary composition based on the computed differences.
6. The end-to-end computational framework as claimed in claim 1, wherein the structured food composition data comprises complete food-derived nutrient profiles rather than isolated nutrient supplementation data.
7. The end-to-end computational framework as claimed in claim 1, wherein the framework predicts microbial metabolite production associated with health conditions, disease biomarkers, or physiological functions.
8. The end-to-end computational framework as claimed in claim 1, further comprising validating predicted microbial metabolic responses using monoculture assays, co-culture assays, metabolomic analysis, or combinations thereof.

Dated this on 4th Day of June, 2026

Dr. Anjumani Talukdar
[IN/PA - 5013]
Agent for the Applicant