Human Metabolic Network Reconstruction Using Hierarchical Segmentation
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Solution Overview
Problem
There is a need for a computational model that can effectively simulate and predict the activity of human metabolic networks, which are complex and influenced by various factors such as genetic mutations and environmental changes, to understand cellular behavior and contribute to the development of new medicines and therapies.
Innovation Solution
A computer-readable medium containing a data structure that relates human reactants to reactions, including substrates, products, stoichiometric coefficients, and constraints, which allows for the determination of flux distributions that predict physiological functions, enabling the simulation of different cellular conditions and the identification of new therapeutic targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive model of human metabolic networks is constructed to predict cellular behavior, then the predictive capability and understanding of physiological functions are improved, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the complex human metabolic network into modular functional units organized in a hierarchical structure. The model divides metabolic pathways into distinct modules (e.g., glycolysis, TCA cycle, amino acid metabolism) that can be independently analyzed and simulated. This segmentation reduces computational complexity by allowing localized analysis rather than requiring simultaneous processing of all network interactions, while maintaining predictive capability through the integrated hierarchical organization of these modules.
Solution Approach 2:
The patent introduces a hierarchical dimension to organize metabolic network data, transitioning from flat two-dimensional representations to multi-level hierarchical structures. This dimensional change allows the model to organize metabolic pathways across multiple levels (e.g., organism level, tissue level, pathway level, reaction level), enabling efficient navigation and analysis of complex metabolic interactions without requiring exhaustive computation of all possible interactions at once.
2Reliability
If detailed biochemical information and genome sequencing data are integrated into the metabolic model, then the comprehensiveness and accuracy of physiological predictions are improved, but the data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing genome sequencing data and biochemical information into structured formats before model construction. Metabolic pathways are pre-annotated with gene associations, enzyme commissions, and pathway memberships. This preliminary organization of data into the hierarchical structure enables faster querying and analysis during model execution, reducing data processing time while maintaining comprehensive integration of genomic and biochemical information for accurate predictions.
3Adaptability or versatility
If the model simulates multiple cellular conditions and genetic mutations to predict physiological functions, then the versatility and applicability to disease research are improved, but the computational burden and simulation time increase
Solution Approach 1:
The patent implements dynamics by creating a flexible hierarchical model structure that can dynamically adapt to different simulation conditions and genetic mutations. The model allows dynamic modification of pathway activities, gene expression levels, and metabolic fluxes based on simulated conditions. This dynamic capability enables the same model framework to efficiently simulate multiple cellular states (normal, diseased, mutant) without requiring separate models for each condition, thereby maintaining high versatility while improving simulation speed through reusable modular components.
Data Source
AI summary
The present invention provides Homo sapiens Recon 1, a manually assembled, functionally validated, bottom-up reconstruction of human metabolism. Recon 1's 1496 genes, 2004 proteins, 2766 metabolites, and 3311 biochemical and transport reactions were extracted from more than 50 years of legacy biochemical knowledge and Build 35 of the human genome sequence.