Phenotype Prediction Model for Genetic Design Optimization
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Solution Overview
Problem
The challenge in synthetic biology is to efficiently optimize the phenotype of a biological system by finding the optimal genetic design among a vast number of possible genetic sequences, which is hindered by the exponential scaling of combinatorial possibilities and the high computational complexity of search problems.
Innovation Solution
A specialized prediction model is developed to optimize the phenotype of a biological system by encoding genotype information into experiential genotype vectors, training a phenotype prediction model, and using Sequential Model Based Optimization (SMBO) to iteratively select and evaluate genetic designs, thereby reducing computational complexity and identifying optimal genetic constructs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If exhaustive search of all possible genetic sequences is performed to optimize phenotype, then optimal genetic design can be found, but the number of experiments required becomes prohibitively large due to exponential scaling
Solution Approach 1:
The patent segments the vast genetic search space into smaller, manageable subspaces by identifying and focusing on specific gene regions and variants that are most likely to influence the desired phenotype. This segmentation allows exhaustive or near-exhaustive search within subspaces rather than the entire space, dramatically reducing experiment numbers while maintaining optimization accuracy.
Solution Approach 2:
The patent performs preliminary actions by using bioinformatics tools and databases to pre-filter and prioritize genetic variants before experimental testing. This preliminary computational screening identifies the most promising candidates for experimental validation, reducing the number of wet-lab experiments required while ensuring the optimal design is found.
2Loss of time
If the search space is narrowed to known gene variants involved in compound production, then the number of experiments is reduced, but the computational complexity and difficulty in quantifying phenotype expressions for unassessed genotypes remain high
Solution Approach 1:
The patent introduces intermediary computational models and prediction algorithms that bridge the gap between genotype sequences and phenotype expressions. These intermediary tools estimate phenotype outcomes for unassessed genotypes based on known relationships, reducing the need for extensive experimental testing while managing computational complexity through efficient algorithms.
Solution Approach 2:
The patent changes parameters by transforming the representation of genetic data and using dimensionality reduction techniques. By converting genetic sequences into numerical representations and applying mathematical transformations, the patent reduces computational complexity while preserving the essential information needed to predict and optimize phenotype expressions.
Data Source
AI summary
A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a specialized prediction model, including receiving constraints, encoding genotype information in experimental data points corresponding to the constraints experiential genotype vectors, the experimental data points comprising the genotype information and phenotype information corresponding to the genotype information, training a phenotype prediction model based on the experiential genotype vectors, the corresponding phenotype information, and the one or more constraints, applying the phenotype prediction model to available genotypes corresponding to the constrains to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating, a result based on the result genotypes, the result indicating one or more genetic constructs for testing.