Metabolic Network Modeling for Strain Optimization
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Solution Overview
Problem
Current metabolic engineering methods for improving producer strains are costly and inefficient, as they often require a trial-and-error approach and do not effectively balance metabolic flux for both cell growth and substance production, leading to inverse proportionality between production and growth.
Innovation Solution
A method involving metabolic network modeling to calculate maximum and optimum metabolic flux values, using the FSEOP algorithm to select genes for amplification, and introducing these genes into the host organism to enhance useful substance production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trial-and-error metabolic engineering methods are used to improve producer strains, then strain improvement can be achieved, but the cost and effort required are excessively high
Solution Approach 1:
The patent changes the approach from trial-and-error to systematic parameter optimization by calculating maximum flux values and optimum flux values for multiple metabolic pathways simultaneously. This allows identification of specific genes whose modification will optimally increase useful substance production without requiring extensive experimental trials
Solution Approach 2:
The patent creates a computational model (metabolic network model) that copies and simulates the complex metabolic system in silico. This virtual model allows prediction of gene targets before actual experimental work, reducing the need for costly physical trial-and-error experiments
2Productivity
If metabolic flux is optimized for useful substance production, then production yield increases, but cell growth is inhibited due to inverse proportionality
Solution Approach 1:
The patent calculates both maximum flux values (for theoretical maximum production) and optimum flux values (balancing growth and production) for each metabolic pathway. By comparing these values across multiple pathways, the system identifies genes whose modification achieves production enhancement while maintaining acceptable growth levels, thus resolving the inverse proportionality problem
Solution Approach 2:
The patent extends the optimization from single-pathway to multi-pathway analysis, adding dimensional complexity to the metabolic network model. This allows simultaneous consideration of multiple competing pathways and identification of genes that affect overall system balance rather than just single pathway flux
Data Source
AI summary
Provided is a method for improving useful substance-producing organisms using metabolic flux analysis, and more particularly a method for improving a host organism producing a useful substance, the method including: calculating a maximum flux value corresponding to the theoretical maximum yield of the useful substance in the metabolic network model of the host organism for producing useful substance, and calculating the optimum value of metabolic flux associated with useful substance production in the metabolic network when the value of cell growth-associated metabolic flux is the maximum under the condition where fermentation data are applied or not applied; selecting metabolic fluxes whose absolute values increase from the range between the maximum value and the optimum value; screening genes associated with the selected metabolic fluxes; and introducing and/or amplifying the selected genes in the host organism. Production of the useful substance can be effectively improved by using the method.