Metabolome-Based Phenotype Prediction Model for Plant Breeding
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Solution Overview
Problem
Current methods for predicting plant phenotypes and traits are time-consuming, costly, and require growing plants to maturity under various environmental conditions, limiting the efficiency and accuracy of metabolomics-based assessments in plant breeding.
Innovation Solution
Establishing unbiased models using metabolic profiles, phenotypic profiles, and trait profiles of plants grown under different conditions, employing techniques like partial least squares analysis, mass spectrometry, and chromatography to predict phenotypes or traits in immature plants without the need for mature growth under stress conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metabolome analysis methods are used to predict plant phenotypes, then comprehensive metabolic profiling can be achieved, but the process becomes complex, expensive, and time-consuming
Solution Approach 1:
The patent segments the complex metabolome analysis into distinct functional modules: (1) obtaining metabolic profile data through high-throughput screening, (2) preprocessing and normalizing the data, (3) training machine learning models on the processed data, and (4) using the trained models to predict phenotypes. This segmentation transforms an overwhelming complex analysis into manageable, automated steps that reduce operational complexity while maintaining prediction accuracy
Solution Approach 2:
The patent replaces manual, labor-intensive metabolome analysis with automated machine learning models. Instead of manually processing metabolic profiles and making predictions, the system uses computational algorithms to automatically analyze the data and generate phenotype predictions, significantly reducing time and cost while maintaining or improving accuracy
2Measurement precision
If plants are grown to maturity under various environmental conditions for phenotype assessment, then accurate phenotype evaluation can be obtained, but significant time and cost are required
Solution Approach 1:
The patent performs preliminary action by training machine learning models on metabolic profile data obtained from plants grown under controlled conditions. Once trained, these models can predict phenotypes that would normally require growing plants to maturity under various environmental stresses. This preliminary modeling allows early-stage prediction without waiting for plants to complete their life cycle under stress conditions, dramatically reducing evaluation time while maintaining accuracy
Solution Approach 2:
The patent creates computational copies (machine learning models) that simulate the complex phenotype expression process. Instead of physically growing plants under multiple environmental conditions to observe phenotype development, the system uses trained models that copy and replicate the phenotype prediction function, allowing rapid virtual evaluation without time-consuming physical experiments
3Productivity
If indirect or direct detection methods are used to screen genomes for genes of interest, then gene presence can be determined, but reliable phenotype prediction at maturity is not achieved
Solution Approach 1:
The patent changes the fundamental parameter used for prediction from genetic information (genes present in the genome) to metabolic information (actual metabolite profiles). While genome screening can quickly identify gene presence with high throughput, it cannot reliably predict phenotypes because genes alone don't capture environmental interactions. The patent uses metabolic profiles, which reflect the actual physiological state resulting from gene-environment interactions, thereby improving prediction reliability while maintaining efficiency through automated analysis
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and early prediction of plant phenotypes or traits, reducing the time and cost associated with traditional evaluation methods, allowing for more efficient plant breeding and selection.
Implementation Method 1
employing techniques like partial least squares analysis, mass spectrometry, and chromatography to predict phenotypes or traits
Implementation Method 2
employing techniques like partial least squares analysis, mass spectrometry, and chromatography to predict phenotypes or traits
Data Source
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AI summary
The invention provides methods for characterizing metabolic profiles, phenotypic profiles and trait profiles in plants or groups of plants. Additionally, methods for establishing an unbiased model between a phenotypic profile and a metabolic profile, or between a trait profile and metabolic profile, are also provided by the invention. Further, methods for using such unbiased models to accurately predict the development of a phenotype of interest or a trait of interest in an independent, immature plant are also provided. In one embodiment, immature plants are selected for use based on their predicted development of a phenotype or trait of interest.