Personalized Probiotic Selection Using Gut Metabolic Models
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Solution Overview
Problem
The effect of probiotics on individuals with different gut microbiota compositions and dietary preferences remains largely unexplored due to the challenges of large-scale longitudinal in vivo experiments and ethical concerns, and there is a lack of clear guidelines for probiotic usage in clinical conditions.
Innovation Solution
A method and system for determining a personalized probiotic therapeutic regimen using hardware processors to analyze dietary preferences, extract DNA from a biological sample, determine microbial abundance, create genome-scale metabolic models, and simulate mono- and co-culture growth to select efficacious probiotic organisms based on net-effect and sustainability metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probiotic interventions are applied to individuals with different gut microbiota compositions, then the therapeutic effect may be improved, but the complexity of determining the appropriate probiotic regimen increases
Solution Approach 1:
The system changes parameters by analyzing multiple variables including gut microbiota composition (taxonomic profile), dietary preferences, and probiotic organism characteristics to determine the optimal probiotic regimen. This multi-parameter approach allows customization of therapy based on individual variations in microbiota makeup and dietary habits, improving therapeutic effectiveness while providing a structured method to manage the complexity of regimen determination
Solution Approach 2:
The system creates in silico metabolic models that replicate the metabolic behavior of probiotic organisms and gut microbiota members under different dietary conditions. These computational models serve as virtual copies that can be simulated to predict probiotic outcomes without requiring extensive in vivo experimentation on diverse individuals, thereby improving reliability while managing complexity through computational abstraction
2Measurement precision
If in vitro experiments are conducted to evaluate probiotic effects on different gut microbiota compositions, then the data quality improves, but the feasibility decreases due to culturing challenges
Solution Approach 1:
The system introduces in silico metabolic models as an intermediary between the probiotic organisms and the gut microbiota environment. These computational models allow evaluation of probiotic effects on different microbiota compositions without requiring physical co-culturing experiments. The models serve as a virtual medium that captures metabolic interactions, maintaining data quality while overcoming the feasibility barriers of in vitro experimentation
Solution Approach 2:
The system replaces the mechanical/biological system of physical co-culturing experiments with a computational simulation system. Instead of physically mixing probiotic organisms with diverse gut microbiota cultures in the laboratory, the system uses in silico metabolic models to simulate these interactions. This substitution maintains measurement precision by accurately modeling metabolic pathways while dramatically improving ease of manufacture/experiment feasibility
3Manufacturing precision
If metabolic simulations are performed to evaluate probiotic sustainability and net-effect, then the personalization accuracy improves, but the computational complexity increases
Solution Approach 1:
The system segments the evaluation process into distinct computational modules: (1) constructing in silico metabolic models of probiotic organisms, (2) simulating mono-culture growth under defined dietary constraints, (3) simulating co-culture growth with gut microbiota members, (4) calculating sustainability metrics, and (5) calculating net-effect scores. This segmentation allows the complex personalization task to be broken into manageable computational steps, improving accuracy while making the overall process more tractable
Data Source
AI summary
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.