Multi-Omic Cell Profiling for Bioreactor State Prediction
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Solution Overview
Problem
Existing methods struggle to effectively analyze and optimize cellular biological processes using pooled biological data sets, lacking a comprehensive approach to integrate multi-omic data and bioreactor conditions for precise control and prediction of cellular states.
Innovation Solution
A computer-implemented method using a generative machine learning model trained with multi-omic data and bioreactor conditions to learn low-dimensional representations, identify clusters, assign labels, and predict output modalities, enabling adjustment of bioreactor conditions for desired cellular states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pooled biological data sets are analyzed using traditional methods, then basic analytics can be performed, but comprehensive integration of multi-omic data and bioreactor conditions for precise control and prediction is not achieved
Solution Approach 1:
The patent combines multiple data types (multi-omic data including genomics, transcriptomics, proteomics, metabolomics) with bioreactor condition data into a unified integrated data structure. This merging enables comprehensive analysis that captures both molecular states and environmental conditions, achieving precise prediction of cellular states while managing complexity through structured integration.
Solution Approach 2:
The system creates a universal platform that can process diverse biological data types and predict multiple cellular states simultaneously. The machine learning model serves multiple functions: classification, regression, and clustering across different data modalities, enabling precise control and prediction without requiring separate specialized systems for each data type.
2Reliability
If multi-omic data and bioreactor conditions are integrated for comprehensive analysis, then precise control of cellular states is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the complex data integration process into distinct modules: data preprocessing for each omic layer, feature selection, machine learning model training, and prediction. This segmentation allows computational tasks to be divided and processed efficiently, reducing overall energy consumption while maintaining high accuracy through systematic processing of each data component.
Solution Approach 2:
The system performs preliminary data processing, normalization, and feature engineering before feeding data into the machine learning model. By preparing and pre-processing the multi-omic and bioreactor condition data in advance, the system reduces computational burden during actual prediction, lowering real-time energy requirements while ensuring reliable and accurate cellular state prediction.
3Adaptability or versatility
If comprehensive integration of multi-omic data is implemented, then system biology knowledge can be fully utilized, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model layer that mediates between the complex multi-omic data and the biological knowledge base. This intermediary component processes and integrates the diverse data types, translating them into predictions about cellular states and phenotypes, thereby reducing the direct complexity of processing pipelines while maintaining comprehensive adaptability.
Solution Approach 2:
The system dynamically adjusts processing parameters and model configurations based on the specific data types and cellular states being analyzed. By changing parameters such as feature selection criteria, normalization methods, and model architecture adaptations, the system maintains high versatility for modeling different cellular phenotypes while managing data processing complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.