Interactive Genome-Scale Flux Analysis for Organism Benchmarking
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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 and method for comparative interactive genome-scale flux balance analysis using Flux-derived Demand-Supply Exchange of Metabolites (FDSeM) and Reaction interaction Graph (RiG) to optimize target compound yield, select organisms with minimal impurities, and improve growth, enabling quantitative comparison and genetic modification strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual search for cell-design objective function is performed, then customization for specific application is achieved, but time consumption and complexity increase significantly
Solution Approach 1:
The system pre-calculates and stores multiple cell-design objective functions for different applications before actual use. When a user needs a specific objective function, it is retrieved from pre-computed results rather than being manually searched or derived in real-time, significantly reducing time consumption while maintaining application-specific customization.
Solution Approach 2:
The patent introduces an intermediate database or storage layer that holds pre-computed objective functions. This intermediary structure acts as a bridge between the complex simulation engine and the user interface, allowing users to access customized objective functions without directly engaging in the complex manual search process.
2Manufacturing precision
If multiple iterative simulations are performed to derive appropriate objective, then optimization quality improves, but automation and interpretability are reduced
Solution Approach 1:
The system implements automated feedback loops where simulation results are automatically analyzed, and objective functions are iteratively refined based on performance metrics. The system provides automated feedback to users about which objective functions yield the best results for their specific application, eliminating the need for manual iteration while maintaining high optimization quality.
Solution Approach 2:
The system performs self-service by automatically executing multiple iterative simulations, analyzing results, and selecting optimal objective functions without requiring continuous user intervention. The automation handles the entire iterative process from simulation execution to result interpretation, freeing users from manual repetition while delivering high-quality optimization.
3Measurement precision
If complex GSMM simulation is used, then metabolic potential analysis is achieved, but interpretability and comparison between organisms are hindered
Solution Approach 1:
The patent segments the complex simulation output into distinct, interpretable components such as objective function values, flux distributions, and metabolic pathway activities. By dividing the complex results into manageable segments, the system maintains precise metabolic potential analysis while improving interpretability through structured presentation of results.
Solution Approach 2:
The system employs visual encoding (analogous to color changes) to represent different metabolic states and objective function performances. Through color-coded visualizations of flux distributions and metabolic pathways, complex simulation data becomes intuitively interpretable, allowing users to quickly grasp key insights without being overwhelmed by raw numerical data.
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. The method may further include defining a molar relationship that includes stoichiometry between one or more intermediate metabolites and a target substance, defining, based on the molar relationship and one or more reaction pathways from the plurality of reaction pathways, a first ratio-based factor that establishes a relationship between a production rate of the target substance and a consumption rate of the consumed substance, performing a first bi-clustering operation to group two or more of the plurality of reaction pathways associated with the target substance, and optimizing, based on the first bi-clustering operation, the first ratio-based factor by placing one or more constraints on production of the target substance.