Deep Learning Genotype-Phenotype Mapping for Synthetic Biology
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Solution Overview
Problem
Current synthetic biology engineering processes rely on trial-and-error methods and ordinary differential equations (ODEs) that do not accurately represent cellular signaling dynamics, leading to inefficiencies and high costs due to uncertainty in cellular responses to genetic modifications.
Innovation Solution
A data-driven deep-learning based algorithm that correlates cellular morphologies with potential genetic insults by training a neural network with cellular morphology features from single and multiple genetic modifications, providing a genotype-phenotype mapping that highlights perturbation subspaces without making assumptions about genotype-phenotype interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error methods with ODEs are used to model cellular responses to genetic modifications, then the process can be implemented with existing tools, but the accuracy of cellular response prediction deteriorates and uncertainty increases
Solution Approach 1:
The patent replaces the mechanical/mathematical ODE-based modeling system with a data-driven deep learning neural network system. The neural network learns genotype-phenotype mappings directly from training data, substituting the traditional ODE approach that relies on differential equations to model cellular signaling dynamics. This substitution enables accurate prediction of cellular responses to genetic modifications without the limitations of ODE assumptions.
2Productivity
If trial-and-error processes are used to obtain synthetic biology products, then flexibility in product development is maintained, but time consumption and costs increase
Solution Approach 1:
The patent performs preliminary action by training the deep learning neural network in advance with extensive genotype-phenotype data from single and multiple genetic modifications. This pre-trained model can then rapidly predict cellular responses to new genetic modifications without requiring time-consuming trial-and-error experiments. The preliminary training phase enables efficient product development by providing a ready-to-use predictive tool.
Solution Approach 2:
The patent creates a computational copy of the genotype-phenotype relationship through the neural network model. Instead of physically performing trial-and-error experiments with cells, the system uses the trained neural network to simulate and predict cellular responses. This computational copying eliminates the need for repeated physical experiments, significantly reducing time and resource requirements.
3Measurement precision
If ODE-based modeling is used to represent cell signaling dynamics, then the approach is computationally straightforward, but the representation accuracy of cellular responses deteriorates
Solution Approach 1:
The patent fundamentally changes the parameters and structure of the computational model from ODE-based parameters (reaction rates, concentration changes over time) to neural network parameters (weights, biases, activation functions). The neural network uses different computational parameters that can capture non-linear genotype-phenotype relationships more accurately, including interactions between multiple genetic modifications that ODEs struggle to represent.
Data Source
AI summary
Provided is a data-driven deep-learning based algorithm for synthetic biology applications that makes no assumptions and/or hypotheses on genotype-phenotype interactions. deep-learning based algorithm trains a neural network with morphological features from single genetic modifications and tests the neural network with morphological features from multiple genetic modifications. The trained and tested neural network uses a link between the morphological features caused by the single and multiple gene modifications as input and outputs a genotype-phenotype mapping highlighting perturbation subspaces. The genotype-phenotype mapping is used to select one or more genetic insults as a starting point to engineer cells in synthetic biology applications.