Flow Cytometry Configuration via Machine Learning
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Solution Overview
Problem
Current flow cytometry systems lack automation in configuring settings, leading to reproducibility challenges and inefficiencies in cell sorting, as configuration is typically done manually and does not learn from historical experiments to improve future sorting.
Innovation Solution
Implementing a biological cell analysis sorting machine with a machine learning system that analyzes historical data and configuration settings from various sources to automatically select and adjust flow cytometry settings in real-time, optimizing cell sorting by predicting optimal configuration settings and reducing experimental errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of flow cytometry settings is used, then flexibility in adjusting parameters is maintained, but reproducibility and consistency of cell sorting results deteriorate
Solution Approach 1:
The system automatically configures flow cytometry settings by analyzing historical experiment data and configuration parameters, enabling the system to self-configure without manual intervention. The processor extracts relevant features from historical data, determines optimal configuration settings, and applies them automatically, making the system serve itself rather than requiring continuous human operation.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical experiment results and configuration settings to continuously improve future configurations. The processor uses past experimental outcomes to refine configuration recommendations, creating a closed-loop system where previous results inform subsequent settings, thereby enhancing reproducibility over time.
2Reliability
If automated configuration using historical data analysis is implemented, then reproducibility of cell sorting is improved, but system complexity increases
Solution Approach 1:
The patent replaces manual mechanical configuration processes with an automated computational system. The processor automatically analyzes historical data, extracts features, determines configuration settings, and applies them without manual intervention. This substitution of manual operations with automated information processing reduces the need for complex manual procedures while maintaining system manageability.
3Measurement precision
If real-time evaluation and feedback of experimental error is performed, then cell sorting accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary analysis of historical experiment data and configuration settings before actual cell sorting experiments. By pre-processing and extracting relevant features from historical data, the system prepares optimal configuration settings in advance, reducing the computational burden and time required during real-time experiments while maintaining high accuracy.
Data Source
AI summary
Computer based methods, systems, and computer readable media are provided for intelligently sorting cells using machine learning. A biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system, may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment.