Genome-Scale Flux Analysis for Comparative Metabolite Optimization
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Solution Overview
Problem
Current methods lack benchmarking approaches to compare organisms for metabolic potential and require tailoring cell-design objective functions for specific applications, which is complex and lacks interpretability, hindering the optimization of bioactive compound production.
Innovation Solution
A system utilizing Genome-scale metabolic models (GSMM) with Flux-derived Demand-Supply Exchange of Metabolites (FDSeM) and Reaction interaction Graph (RiG) for interactive simulation and optimization, enabling selection of organisms with high target compound yield and minimal impurities, and allowing genetic modifications for improved production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual iterative simulations are performed to optimize cell-design objective functions, then the yield of target compounds can be improved, but the complexity of the optimization process increases and interpretability decreases
Solution Approach 1:
The patent introduces FDSeM and RiG as intermediary visualization tools that translate complex simulation outputs into interpretable formats. FDSeM visualizes metabolite fluxes and RiG displays reaction interactions, enabling SMEs to understand optimization results without dealing with raw mathematical complexity, thus resolving the contradiction between achieving high yield and maintaining process interpretability
Solution Approach 2:
The system implements automated feedback loops where simulation results are automatically visualized and interpreted through FDSeM/RiG, allowing SMEs to iteratively refine cell-design objective functions based on clear feedback about flux distributions and reaction interactions, reducing the manual effort and complexity while maintaining optimization effectiveness
2Measurement precision
If multiple iterative simulations are performed to derive appropriate objective functions, then optimization accuracy improves, but the time required for decision-making increases
Solution Approach 1:
The patent performs preliminary automated simulations and generates FDSeM/RiG visualizations in advance, allowing SMEs to review multiple optimization scenarios simultaneously before making decisions. This preliminary action reduces the time required for iterative decision-making while maintaining optimization accuracy through pre-computed flux analyses
Solution Approach 2:
The system creates visual copies of complex simulation data in the form of FDSeM and RiG diagrams, which can be reviewed and compared without requiring SMEs to analyze raw mathematical outputs. This copying approach enables faster decision-making by presenting optimized representations of simulation results
3Loss of information
If complex GSMM simulation outputs are generated, then comprehensive metabolic analysis is achieved, but interpretability and comparison between organisms becomes difficult
Solution Approach 1:
The patent segments comprehensive GSMM simulation outputs into distinct visual components: FDSeM for metabolite-level flux analysis and RiG for reaction-level interaction analysis. This segmentation allows SMEs to interpret different aspects of metabolic behavior separately, maintaining comprehensive information while improving interpretability through structured visualization
Solution Approach 2:
The system transforms complex numerical simulation outputs into spatial visual representations through FDSeM and RiG diagrams. By adding a visual dimension to the data, the system enables intuitive interpretation of metabolic fluxes and interactions that would be difficult to discern from tabular or mathematical representations alone
4Measurement precision
If quantitative benchmarking methods are implemented to compare organisms, then selection accuracy for high-yield organisms improves, but the complexity of the comparison system increases
Solution Approach 1:
The patent creates universal visualization frameworks (FDSeM and RiG) that can be applied across different organisms and metabolic models. These multi-functional tools enable consistent quantitative comparison of metabolic potential across diverse organisms without requiring organism-specific analysis methods, thus improving comparison accuracy while managing system complexity through standardized approaches
Data Source
AI summary
A method, device, and system are disclosed. One example of a method includes loading a model associated with a first organism, where the model expresses a plurality of reaction pathways including one or more intermediate metabolites to produce a target substance from a consumed substance in the first organism, and where the model further provides an association between a first reaction and a second reaction present in each reaction pathway and one or more genes that are used to produce the target substance. The method further includes obtaining a cell design objective function and optimizing one or both of the target substance and the consumed substance using the second flux flow.