In Silico Nutraceutical Composition Prediction via Metabolic Networks
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Solution Overview
Problem
Current methods for optimizing nutraceutical compositions to affect the gut microbiota for health benefits rely heavily on in vivo experimentation, which is time-consuming and prone to human error, and existing in silico models are limited in their predictive capabilities due to incompleteness.
Innovation Solution
A computer-implemented method and system that receive user inputs on microorganisms, nutraceuticals, and health conditions, extracting genome-scale metabolic networks from a database to generate optimized nutraceutical compositions using reaction rules derived from enzymes, enabling accurate prediction of metabolic interactions and health impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in vivo experimentation is used to optimize nutraceutical compositions, then accuracy of prediction is improved, but time consumption increases
Solution Approach 1:
The patent creates in silico models that are computational copies of in vivo biological systems. These models replicate metabolic pathways, gene expressions, and physiological responses to nutraceuticals, allowing virtual experimentation that mirrors real biological processes without requiring physical human or animal subjects. This copying approach maintains predictive accuracy while eliminating time-consuming in vivo procedures.
Solution Approach 2:
The patent performs preliminary computational analysis and model validation before conducting actual in vivo experiments. By pre-screening nutraceutical compositions and predicting their effects through in silico models, the system identifies promising candidates that can then be tested in vivo with reduced sample sizes and shorter durations, thereby reducing overall time consumption while maintaining accuracy.
2Loss of time
If in silico models are used to predict effects of nutraceuticals, then time consumption is reduced, but prediction accuracy deteriorates due to model incompleteness
Solution Approach 1:
The patent segments the complex biological system into distinct computational modules representing different physiological levels: genomic pathways, transcriptomic regulation, proteomic interactions, and metabolomic outputs. Each module is modeled separately with appropriate detail, then integrated to produce comprehensive predictions. This segmentation allows the model to achieve high accuracy in specific predictions while maintaining computational efficiency.
Solution Approach 2:
The patent creates a composite in silico model that integrates multiple types of biological data and computational approaches. The model combines genomic annotations, transcriptomic expression patterns, proteomic interaction networks, and metabolomic pathway information into a unified predictive framework. This composite structure leverages the strengths of different data types to overcome the limitations of any single approach, thereby improving overall prediction accuracy.
3Reliability
If genome-scale metabolic networks are extracted and used, then model completeness is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic genome-scale metabolic networks that can adapt their complexity based on the specific query or prediction task. The model dynamically activates or deactivates specific pathways, reactions, and regulatory mechanisms depending on the nutraceutical being analyzed and the physiological context. This dynamic approach allows the system to maintain high model completeness when needed while reducing computational complexity for routine predictions, effectively managing the complexity-completeness trade-off.
Data Source
AI summary
The present disclosure relates to methods and systems for obtaining a nutraceutical composition. The method comprises the use of genome-scale metabolic networks of microorganisms to identify nutraceutical compositions for one or more health conditions.