Computational Model for Biological Sample Pre-Quantitation Analysis
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Solution Overview
Problem
Current methods for determining biological sample quality are inadequate when the sample history is unknown, and existing technologies require additional equipment or fail to account for advances in sample degradation analysis.
Innovation Solution
A method and system that use computational models developed from biological data pairs to determine unknown pre-quantitation attributes of target biological samples by analyzing post-quantitation attributes from altered samples with known degradation, allowing for the assessment of sample quality without additional equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational models are developed using post-quantitation attributes from altered samples, then the ability to determine unknown pre-quantitation attributes is improved, but the complexity of data processing and model development increases
Solution Approach 1:
The system performs preliminary actions by collecting post-quantitation attributes from multiple altered biological samples and computationally developing predictive models before actual sample analysis. This preprocessing of data and model creation enables accurate determination of pre-quantitation attributes without requiring complex real-time measurements during sample analysis.
Solution Approach 2:
The system creates computational copies or representations of the relationship between sample alterations and post-quantitation attributes through predictive models. These computational models serve as virtual replicas that can predict pre-quantitation attributes from post-quantitation measurements, eliminating the need for direct physical measurement of difficult-to-obtain pre-quantitation parameters.
2Reliability
If rigorous pre-measurement protocols are applied to ensure sample quality, then sample quality is improved, but the time and resources required for sample preparation increase
Solution Approach 1:
The system replaces mechanical and manual quality control procedures with computational analysis. Instead of physically verifying sample quality through rigorous pre-measurement protocols, the system uses predictive computational models that analyze post-quantitation attributes to infer pre-quantitation quality parameters, significantly reducing preparation time while maintaining reliability.
Solution Approach 2:
The system enables samples to essentially self-verify their quality characteristics through the computational model. By analyzing easily obtained post-quantitation attributes, the model allows samples to provide their own quality assessment without requiring extensive external verification procedures, reducing both time and resource requirements.
3Measurement precision
If additional equipment is used to determine sample quality, then measurement capability is improved, but device complexity and cost increase
Solution Approach 1:
The system makes existing quantitation equipment multi-functional by enabling it to determine both post-quantitation attributes and infer pre-quantitation attributes through computational models. This universality eliminates the need for separate specialized equipment for quality assessment, as the same instrument that measures post-quantitation parameters can also determine pre-quantitation characteristics via the predictive model.
Solution Approach 2:
The computational model serves as an intermediary that bridges the gap between easily measurable post-quantitation attributes and difficult-to-measure pre-quantitation attributes. Rather than requiring direct measurement equipment for all parameters, the computational intermediary translates readily available measurements into predictions of harder-to-obtain quality parameters.
Data Source
AI summary
Systems and methods of determining pre-quantitation attributes of biological samples using post-quantitation attributes of those samples is disclosed. By altering a set of biological samples in a measurable way before running the set through an instrument (e.g., a mass spectrometer), a model can be developed that enables determination of the unknown pre-quantitation attributes in other biological samples as a function of post-quantitation attributes.