Generative Predictive Model Phenotype Optimization
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Solution Overview
Problem
In synthetic biology, finding the optimal genetic sequence to optimize the production of a specific molecule or compound is challenging due to the large number of possible genetic designs and the high computational complexity of evaluating acquisition functions in high-dimensional solution spaces.
Innovation Solution
A method combining a generative model and a predictive model to efficiently optimize the phenotype of a biological system. The generative model generates new genotype vectors, while the predictive model predicts phenotypic attributes, reducing the computational complexity and identifying optimal genetic designs with fewer experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the search space is reduced to known variants of genes or parts of DNA, then the number of experiments required is reduced, but the number of possible genetic designs remains quite large
Solution Approach 1:
The patent uses a predictive model to create virtual copies of genetic design evaluations through computational simulations. Instead of physically testing each genetic variant in the laboratory, the system generates computational predictions of phenotypic outcomes, thereby reducing the need for actual experimental iterations while still exploring a large design space.
2Ease of operation
If automated techniques and algorithms are used to reduce the search space, then the manual effort is reduced, but the computational complexity scales exponentially
Solution Approach 1:
The patent applies preliminary filtering and constraints to the genetic design space before performing computationally intensive evaluations. By pre-defining feasible regions based on known biological constraints and prior knowledge, the system reduces the effective search space that requires exponential computational resources, making automated optimization tractable.
3Loss of time
If predictive models with acquisition functions are used to evaluate the solution space, then the experimental evaluations are reduced, but evaluating the acquisition function on all points in high-dimensional spaces is virtually impossible
Solution Approach 1:
The patent introduces an intermediary sampling strategy that bridges the gap between the high-dimensional solution space and the predictive model evaluations. Instead of directly evaluating all points in the high-dimensional space, the system uses intermediate representations or projections that reduce dimensionality while preserving the essential information needed for accurate phenotype prediction.
Data Source
AI summary
A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a combination of a generative and a predictive model, training a phenotype prediction model based on experiential genotype vectors, training a genotype generation model based on sample genotype vectors, generating new genotype vectors, applying the phenotype prediction model to the new genotype vectors 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.