Microbiome Characterization via Segmented Data Processing
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Solution Overview
Problem
Current methods for characterizing human microbiomes and providing personalized therapeutic measures based on microbiome composition and functional features are limited due to technological constraints, such as inefficient sample processing and data analysis, leading to incomplete understanding and ineffective therapies for diet-related health conditions.
Innovation Solution
A method and system for generating microbiome datasets and processing supplementary data to determine diet-related conditions, using computational models to characterize microbiome features and provide personalized health-supporting measures, including Concordance Score analysis for comparing different dietary interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current sample processing and data analysis techniques are used for microbiome characterization, then the process is simpler, but the characterization accuracy and completeness is insufficient
Solution Approach 1:
The microbiome characterization process is divided into distinct segments: sample collection, DNA extraction, sequencing, data processing, and analysis. Each segment is optimized independently with specialized protocols and tools, allowing for improved accuracy at each stage without overwhelming complexity in the entire system.
Solution Approach 2:
The patent implements preliminary actions by preparing comprehensive reference databases of microbial genomes and functional annotations before actual sample analysis. This pre-processing enables more accurate characterization during the actual analysis phase without requiring complex real-time computations.
2Loss of information
If comprehensive microbiome data is collected and analyzed, then the understanding of diet-related conditions is improved, but the time and computational resources required increase
Solution Approach 1:
The patent extracts and focuses on specific microbial taxa and functional pathways most relevant to diet-related conditions, rather than analyzing all possible microbiome data. This selective extraction maintains information completeness for the conditions of interest while significantly reducing processing time and computational requirements.
Solution Approach 2:
The analysis methodology dynamically adjusts parameters such as sequencing depth, taxonomic resolution, and functional annotation thresholds based on the specific research question and available resources. This allows comprehensive analysis when resources permit while enabling faster, targeted analysis when time is constrained.
3Reliability
If personalized therapeutic measures are developed based on microbiome composition, then the effectiveness of therapy is improved, but the complexity of treatment planning increases
Solution Approach 1:
The patent applies local quality by tailoring therapeutic recommendations to the specific microbiome characteristics of each individual or patient group. Instead of universal treatment protocols, the system identifies and addresses specific microbial imbalances, functional deficiencies, or pathogenic taxa present in each case, thereby improving effectiveness without requiring overly complex planning.
Solution Approach 2:
The patent develops a multi-functional therapeutic framework that can address multiple diet-related conditions through a unified microbiome-based approach. The same analytical platform and intervention strategies can be applied across different conditions (e.g., obesity, diabetes, IBD), simplifying treatment planning while maintaining personalized effectiveness.
Data Source
AI summary
Embodiments of a method and/or system for characterizing a diet-related condition for a user can include one or more of: generating a microbiome dataset for each of an aggregate set of biological samples associated with a population of subjects, based on sample processing of the biological samples; processing a supplementary dataset associated with one or more diet-related conditions for the set of users; and performing a diet-related characterization process for the one or more diet-related conditions, based on the supplementary dataset and/or microbiome features extracted from the microbiome dataset.