Machine Learning Metabolic Pathway Dynamics Simulation
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Solution Overview
Problem
Current computational models for predicting biological outcomes in synthetic biology are limited by their inability to accurately and efficiently simulate metabolic pathway dynamics, relying on insufficient traditional methods like Michaeles-Menten kinetics.
Innovation Solution
The use of time-series multiomics data, specifically combining metabolomics and proteomics data, to train machine learning models that simulate metabolic pathway dynamics, allowing for the prediction of metabolite concentrations and the design of virtual strains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational models like Michaeles-Menten kinetics are used to predict biological outcomes, then the modeling process is simple and traditional, but the predictive accuracy and efficiency are insufficient
Solution Approach 1:
The patent transforms the modeling approach by changing from traditional kinetic parameters to machine learning parameters. It uses time-series multiomics data to train ML models that learn complex nonlinear relationships, replacing the simplified Michaeles-Menten equations with data-driven predictive models that capture dynamic metabolic behavior more accurately
Solution Approach 2:
The patent substitutes traditional mechanical/mathematical kinetic models with machine learning systems. Instead of using differential equations based on enzymatic mechanisms, it employs neural networks and other ML algorithms that automatically learn from multiomics data, replacing the mechanistic approach with a data-driven approach that achieves superior predictive accuracy
2Reliability
If more comprehensive data and complex models are used to improve predictive accuracy, then the prediction quality improves, but the computational complexity and data requirements increase
Solution Approach 1:
The patent segments the complex metabolic system into manageable components by analyzing different omics layers (transcriptomics, proteomics, metabolomics) separately and then integrating them. The machine learning models are trained on segmented data from multiple strains and conditions, allowing the system to handle complexity through modular analysis while maintaining high prediction reliability
Solution Approach 2:
The patent creates universal machine learning models that can predict dynamics across multiple metabolic pathways, organisms, and experimental conditions. The trained models serve multiple functions: predicting metabolite concentrations, identifying rate-limiting steps, and guiding strain design, thereby managing computational complexity through multi-functional predictive capabilities
Data Source
AI summary
Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.