Gene Network Endophenotype Mapping for Faster Trait Engineering
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Solution Overview
Problem
Existing methods for manipulating plant biological processes to achieve desired phenotypes are time- and space-inefficient, as they rely on waiting for phenotypes to develop in mature plants, while intermediate endophenotype biomarkers at smaller scales are often overlooked.
Innovation Solution
A method involving machine-learning models to predict and modify endophenotypes by selecting and introducing gene regulatory sequences into plants, utilizing models trained on gene regulatory sequences and data from RNAseq, microarrays, and single cell RNASeq to guide genome editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If genes are knocked in, knocked out, or mutated to produce a desired phenotype by waiting for phenotype development in mature plants, then the desired phenotype can be achieved, but the process is time- and space-inefficient
Solution Approach 1:
The patent applies preliminary action by using machine-learning models to predict endophenotype outcomes before performing gene editing. The system predicts the effects of potential gene regulatory sequence modifications on endophenotypes (intermediate biomarkers) in advance, allowing researchers to select optimal sequences that will produce desired phenotypes without having to wait for actual phenotype development in mature plants. This predictive approach enables preliminary selection of gene regulatory sequences based on their predicted effects on intermediate biomarkers such as gene expression levels, protein abundance, or metabolite concentrations.
2Productivity
If intermediate endophenotype biomarkers are used as indicators, then the process becomes more efficient, but these biomarkers are often overlooked in existing methods
Solution Approach 1:
The patent applies the intermediary principle by focusing on endophenotype biomarkers as intermediate indicators between gene regulatory sequences and final phenotypes. The machine-learning model is trained to predict these intermediate biomarkers (such as mRNA expression levels, protein abundance, or metabolite concentrations) that serve as mediators. By measuring and predicting these intermediate biomarkers rather than waiting for final phenotypes to develop, the system efficiently links gene regulatory modifications to their functional outcomes without overlooking the critical intermediate information.
3Loss of time
If machine-learning models are used to predict endophenotypes, then the time required for phenotype development is reduced, but the complexity of the system increases
Solution Approach 1:
The patent applies the copying principle by creating a computational model that replicates and predicts biological processes without physically performing them. The machine-learning model learns from training data representing gene regulatory sequences and their effects on endophenotypes, creating a virtual copy of the biological system's behavior. This computational replica allows the system to predict outcomes of gene editing experiments without actually performing the experiments in the lab, thereby reducing time requirements while managing complexity through software-based prediction rather than physical experimentation.
Data Source
AI summary
A method for predicting endophenotypes of interacting partner genes includes obtaining one or more endophenotype profiles corresponding to a genotype, partitioning the one or more endophenotype profiles into a first set of endophenotypes and a second set of endophenotypes, and receiving an input to modify the first set of endophenotypes to a desired level. The method thus includes inputting the modified first set of endophenotypes and unmodified second set of endophenotypes into a trained machine-learning model to obtain a prediction of an updated second set of endophenotypes. The updated second set of endophenotypes represents an updated version of the second set of endophenotypes after interacting with the modified subset of the first set of endophenotypes.