Cell Digital Twin for Compound Prediction
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Solution Overview
Problem
Ascertaining the mode of action, target disease, or immune response of a bioactive compound from cell response data is difficult, time-consuming, and resource-intensive, requiring numerous laboratory experiments and manual analysis that may not identify significant correlations between compounds.
Innovation Solution
A system utilizing a cell digital twin with a generative neural network and feedback loop to generate validated predictions for a compound of interest based on input data, including a prediction engine and validation engine that analyze cell response profiles to predict immune response, mode of action, and target diseases without the need for biological experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual analysis of cell response data is performed, then resource consumption is reduced, but prediction accuracy and identification of significant correlations deteriorate
Solution Approach 1:
The patent creates a digital twin model that copies and replicates cell response data and characteristics in a computational environment. This digital replica allows extensive analysis and prediction generation without consuming additional physical resources, enabling high-accuracy predictions through repeated simulations and correlations while maintaining low resource consumption.
Solution Approach 2:
The patent replaces manual biological experimentation and analysis with a computational digital twin system. The digital twin uses algorithms and machine learning models to simulate cell responses, substitute manual laboratory experiments with in silico testing, and replace human analysis with automated pattern recognition, thereby improving prediction accuracy while reducing resource consumption.
2Reliability
If numerous laboratory experiments are conducted to ascertain compound effects, then prediction reliability is improved, but time consumption and resource intensity increase
Solution Approach 1:
The patent performs preliminary computational analysis and prediction generation using the digital twin model before conducting any laboratory experiments. By pre-simulating compound effects and identifying potential targets in silico, the system reduces the number of required wet lab experiments, thereby maintaining high prediction reliability while significantly reducing time consumption and resource intensity.
Solution Approach 2:
The digital twin creates a virtual copy of cell responses and biological systems that can be repeatedly queried and analyzed without time cost. This computational replica allows parallel exploration of multiple compounds and conditions simultaneously, providing reliable predictions instantaneously without the sequential time constraints of physical experimentation.
3Device complexity
If manual analysis of cell response data is performed, then operational complexity is reduced, but ability to identify significant correlations between compounds deteriorates
Solution Approach 1:
The patent replaces manual data analysis with automated machine learning algorithms and computational models within the digital twin. These algorithms automatically detect patterns, correlations, and relationships between compounds and cell responses that would be imperceptible to human analysts, thereby preventing loss of correlation information while maintaining simple operational interfaces.
Solution Approach 2:
The digital twin serves as an intermediary layer between raw cell response data and meaningful correlations. It processes and transforms complex data through computational models, acting as a mediator that extracts significant relationships between compounds and biological responses without requiring users to directly handle complex data structures.
Data Source
AI summary
The present disclosure provides for generation of predictions for a compound based on input data corresponding to the compound. A cell digital twin receives the input data corresponding to the compound and generates predictions based on the input data. The cell digital twin comprises a prediction engine including a model generated using reduced representations of known cell response profiles corresponding to tested compounds. The model is updated by a feedback loop between a validation engine of the cell digital twin and the model.