Cell Culture Data Processing for Protocol Optimization
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Solution Overview
Problem
Current cell culture methods are inefficient and time-consuming, particularly when optimizing multiple-stage protocols, as changes in one stage can affect subsequent stages, leading to a high number of experiments and a need for large-scale screening to identify effective protocols, which often results in false positives and negatives, and there is a lack of effective data analysis to guide further investigation.
Innovation Solution
A method and apparatus for processing cell culture data by analyzing results from multiple stages of cell culture experiments, utilizing computer-implemented analysis to identify effective protocols through ordering or grouping based on similarities between protocols, reducing false positives, and prioritizing protocols for further investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large-scale screening experiments are performed to identify effective protocols, then the number of successful protocols is improved, but the number of false positives and false negatives increases and experimental time and cost increase
Solution Approach 1:
The patent applies preliminary action by performing computational analysis on experimental data before conducting follow-up experiments. The system analyzes results from large-scale screening, identifies promising protocols through data processing, and prioritizes them for further investigation. This preliminary computational step filters out false positives and false negatives, allowing researchers to focus on the most promising protocols and reduce unnecessary experimental time.
Solution Approach 2:
The patent replaces mechanical experimental systems with computational analysis systems. Instead of performing numerous physical experiments to validate each protocol, the system uses computer-implemented data analysis to evaluate protocol effectiveness, identify patterns, and predict which protocols are most likely to succeed. This substitution of computational methods for mechanical experimentation significantly reduces time and resource consumption.
2Reliability
If conventional cell culture methods are used to optimize multiple-stage protocols, then protocol optimization is possible, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces conventional mechanical cell culture optimization methods with computational data analysis. The system processes experimental data from multiple-stage cell culture experiments, uses algorithms to identify effective protocol combinations, and provides guidance for optimization. This computational approach maintains the ability to optimize protocols while dramatically increasing productivity by avoiding trial-and-error experimentation.
Solution Approach 2:
The patent implements feedback by using experimental results to inform subsequent experimental design. The system analyzes data from completed experiments, identifies which protocol stages and combinations are most effective, and uses this information to guide the design of follow-up experiments. This feedback loop enables efficient protocol optimization by building on previous results rather than repeating experiments.
3Measurement precision
If follow-up experiments are performed on multiple promising protocols, then better statistical results are obtained, but the cost and time investment increases significantly
Solution Approach 1:
The patent applies taking out by extracting the most promising protocols from the large set of screened protocols through computational analysis. The system identifies a small subset of high-priority protocols that are most likely to succeed based on their performance in initial screening and similarity to known effective protocols. This extraction allows follow-up experiments to focus resources on only the most promising candidates, obtaining adequate statistical power without investing resources in testing numerous marginal protocols.
Solution Approach 2:
The patent applies partial action by performing follow-up experiments on only a partial set of protocols rather than all protocols that showed some promise in initial screening. The computational analysis identifies the top prioritized protocols that warrant further investigation, allowing researchers to obtain meaningful statistical results by focusing on a selective subset rather than exhaustively testing all potential protocols.
Data Source
AI summary
One embodiment of the invention provides a method of processing cell culture data. The data comprises results from a large number of samples, the results being obtained by performing multiple stages of cell culture in succession on each sample. Each stage represents a cell culture treatment having a particular set of conditions, such that each sample follows a protocol specified by the identity and order of the treatments applied to the cell culture. The method includes specifying a subset of the samples that yielded a desired cell culture outcome. The method further includes performing a computer-implemented analysis of the results from the samples in the subset to produce an ordering or grouping for the results. The ordering or grouping helps to identify one or more protocols that are effective for obtaining the desired cell culture outcome. The analysis for producing the ordering or grouping utilizes information on similarities between different protocols.