FaceCon ShadowCon Modules for Metabolic Strain Design
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Solution Overview
Problem
Current metabolic engineering algorithms struggle to efficiently design strains that meet multiple design criteria, such as co-utilization of multiple substrates and elimination of undesirable by-products, while maintaining optimal chemical production, due to limitations in handling suboptimal flux distributions and complex network interactions.
Innovation Solution
The introduction of FaceCon and ShadowCon modules, which are integrated into mixed integer linear adaptive evolution metabolic engineering algorithms, allows for additional design criteria to be considered, enabling the control of flux coupling and chemical production levels, thereby filtering out undesirable solutions and optimizing strain design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If metabolic engineering algorithms are used to design strains for chemical production, then chemical production is improved, but the ability to meet multiple design criteria simultaneously deteriorates
Solution Approach 1:
The patent segments the strain design problem into multiple independent criteria that can be evaluated separately. Each design criterion (e.g., chemical production, by-product elimination, substrate co-utilization) is treated as an independent constraint that can be assessed and optimized individually, allowing the algorithm to handle multiple criteria simultaneously without compromising chemical production.
Solution Approach 2:
The patent adds a new dimension to the optimization problem by incorporating flux distribution quality assessment. Instead of solely optimizing for chemical production rate, the system evaluates flux distributions across multiple dimensions including by-product formation, substrate utilization patterns, and metabolic efficiency, thereby enabling simultaneous satisfaction of multiple design criteria.
2Productivity
If algorithms focus on maximizing chemical production at maximal growth rate, then chemical production is improved, but the ability to address complex problems like co-utilization of multiple substrates deteriorates
Solution Approach 1:
The patent performs preliminary assessment of flux distribution quality before final strain design optimization. By evaluating potential flux distributions against multiple design criteria in advance, the system identifies promising candidates that satisfy complex requirements such as substrate co-utilization and by-product elimination, then optimizes these candidates for maximal chemical production at growth rate.
Solution Approach 2:
The patent introduces dynamic evaluation of flux distributions that can adapt to different design scenarios. The system dynamically adjusts evaluation criteria and weights based on the specific design problem being addressed, enabling flexible handling of complex problems like substrate co-utilization while maintaining focus on chemical production optimization.
3Device complexity
If conventional algorithms are used, then computational simplicity is maintained, but the ability to evaluate suboptimal flux distributions and tailor strain behavior deteriorates
Solution Approach 1:
The patent segments the complex evaluation process into modular components that can be independently implemented and adjusted. Each module evaluates a specific aspect of flux distribution (e.g., by-product formation, substrate utilization, metabolic efficiency), allowing the system to handle complex evaluations while maintaining computational organization and simplicity through structured modularity.
Data Source
AI summary
Systems and methods for determining genetic variations of an organism for performing a physiological function. The systems include a number of modules that can be integrated into existing metabolic engineering algorithms. The methods include use of the modules. The physiological function may include growth, compound production, and/or metabolic reaction flux, among others.