Biological System Modeling With Closed-Loop Graph Learning
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Solution Overview
Problem
The lack of governing equations for biological systems hinders accurate modeling of drug-disease-cell interactions, leading to heuristic approaches that often yield undesirable outcomes.
Innovation Solution
A method involving a bipartite graph structured as a closed-loop control system, utilizing machine learning models initialized with architectural and class data, and trained using sample data to model biological systems, enabling data-driven representation of interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If first-principles modeling is used for biological systems, then model accuracy would improve, but it cannot be implemented because governing equations are unknown
Solution Approach 1:
The patent replaces traditional physics-based first-principles modeling with a data-driven machine learning approach. Instead of using governing equations from physical laws, the system uses neural networks trained on biological data to predict system behavior, substituting mechanical/mathematical equation-based modeling with statistical learning methods.
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed governing equations to learned parameters. The machine learning model learns optimal parameters from training data that capture biological system dynamics, allowing the model to adapt to specific biological contexts without requiring universal governing equations.
2Ease of manufacture
If heuristic approaches are used for modeling biological systems, then model implementation becomes easier, but model accuracy deteriorates leading to undesirable outcomes
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model is trained on experimental or clinical data, validated against independent test sets, and iteratively improved. This closed-loop approach ensures the model learns accurate representations of biological systems while maintaining implementation feasibility through standardized training procedures.
Solution Approach 2:
The patent creates a virtual copy of the biological system through the machine learning model that replicates system behavior without requiring physical experimentation. This digital twin approach allows accurate modeling of complex biological interactions while avoiding the limitations of heuristic simplifications.
3Measurement precision
If complex machine learning models are trained on biological data, then modeling accuracy improves, but computational complexity and data requirements increase
Solution Approach 1:
The patent segments the biological system into discrete components represented as nodes in a graph structure, with interactions represented as edges. This segmentation allows the complex system to be modeled through localized node representations and edge relationships, reducing the overall computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces graph neural networks as an intermediary layer between raw biological data and predictions. The GNN architecture serves as a mediator that automatically learns meaningful representations from structured biological data, simplifying the modeling process while improving accuracy through hierarchical feature extraction.
Data Source
AI summary
The present disclosure provides methods and systems for modeling cellular behavior. A method for generating a model of a biological system may include obtaining sample data including records derived from samples of the biological system. The records may indicate the presence, absence, and/or expression levels of entities in respective samples of the biological system. The method may further include dividing the sample data into a training set and a validation set, providing biological system data as input to a machine learning model to initialize the model, training the model to model dynamic behavior of the biological system based on the training set, and validating the trained model using the validation set. The biological system data may include a bipartite graph representing the biological system and structured as an optimal control loop.