Engineered Bacterial Strains for Threonine Production via Machine Learning
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Solution Overview
Problem
Current methods for producing threonine in biomanufacturing face limitations such as the need for extensive knowledge, numerous factors affecting yield, and the complexity of strain modifications, which makes exhaustive testing infeasible and hides effects that only appear in combinations of modifications.
Innovation Solution
Engineering bacterial strains through identifying optimized parameters for increased threonine production, constructing strains with these parameters, collecting production data, performing computational analysis, and iterating to refine the strains using machine learning and metabolic modeling to predict key engineering elements for enhanced production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If exhaustive experimental testing is performed to optimize bacterial strains, then production yield can be improved, but the complexity and time required for testing increases significantly
Solution Approach 1:
The patent applies preliminary action by using computational modeling and machine learning algorithms to predict optimal strain configurations before performing experimental testing. Metabolic models simulate bacterial metabolism to forecast threonine production outcomes, and machine learning algorithms analyze historical data to identify promising strain modifications in advance, thereby reducing the scope of exhaustive testing while maintaining high production yield.
Solution Approach 2:
The patent introduces computational models and algorithms as intermediaries between strain design and experimental testing. These intermediaries process complex biological data, predict production outcomes, and guide experimental decisions, thereby mediating the relationship between strain engineering and productivity measurement to reduce testing complexity while optimizing yield.
2Productivity
If multiple strain modifications are combined to improve production, then yield can increase, but interactions between modifications become difficult to detect and measure
Solution Approach 1:
The patent replaces manual experimental detection of interaction effects with computational algorithms that automatically analyze strain performance data. Machine learning models process production data from multiple modified strains, identifying interaction effects between genetic modifications through pattern recognition, thereby substituting complex manual measurement and analysis with automated computational detection.
Solution Approach 2:
The patent changes the approach to measuring interaction effects by transforming qualitative biological interactions into quantifiable computational parameters. The system encodes strain modifications as digital parameters and uses algorithms to detect non-linear relationships between these parameters and production outcomes, making previously difficult-to-measure interaction effects detectable and measurable.
3Ease of operation
If reductionist approach is used to test single strain modifications, then simplicity is maintained, but combinatorial effects are hidden
Solution Approach 1:
The patent applies segmentation by dividing the complex task of strain optimization into modular components: individual genetic modifications are tested separately to establish baseline effects, then computational algorithms systematically combine these modular results to predict overall performance. This allows maintenance of testing simplicity for individual components while achieving comprehensive evaluation of combinatorial effects through computational integration.
Solution Approach 2:
The patent adds a computational dimension to the reductionist testing approach. Instead of relying solely on sequential physical experimentation, the system layers computational modeling and data analysis on top of simple individual tests, enabling detection of combinatorial effects that would be invisible in single-modification experiments while maintaining the simplicity of basic testing procedures.
Data Source
AI summary
The present disclosure provides novel engineered target-biomolecule-producing bacterial strains and methods of producing the same. To engineer bacterial strains capable of producing substantial levels of a target biomolecule, the methods may implement the use of metabolic modeling and machine learning methods. The methods and bacterial strains produced by the methods may be implemented in further optimizing a biosynthetic pathway, e.g., to improve the production of a target biomolecule of interest, e.g., an amino acid, such as threonine.