Automated Fraction Selection for Nucleic Acid Pooling
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Solution Overview
Problem
The manual examination of characterization data for nucleic acid molecule fractions, such as guide RNA, becomes impractical with an increasing number of fractions, leading to inconsistent quality and yield in large-scale manufacturing, as existing methods rely heavily on manual analysis of mass spectrometry and liquid chromatography data.
Innovation Solution
An automated method using processor-based systems to simulate a predicted metric for a combined pool of nucleic acid fractions by aligning and aggregating chromatogram data, determining weighted averages of mass spectrometry data, and optimizing fraction selection to streamline the pooling process without compromising product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual examination of characterization data is used for fraction selection, then flexibility in decision-making is maintained, but the process becomes impractical and inconsistent with an increasing number of fractions
Solution Approach 1:
The system performs self-service by automatically analyzing characterization data, simulating pool metrics, and recommending fraction combinations without requiring manual intervention. The automated system serves itself by integrating data processing, metric simulation, and decision support functions that previously required human operators.
Solution Approach 2:
Manual mechanical analysis operations are replaced with automated computational systems. The patent substitutes human manual examination with computer-based algorithms that process mass spectrometry and liquid chromatography data, simulate pool metrics, and generate fraction selection recommendations automatically.
2Manufacturing precision
If the number of fractions is increased to improve purification resolution, then separation quality improves, but the complexity of examining all characterization data becomes overwhelming
Solution Approach 1:
The system creates virtual copies of the pooling process through simulation. Instead of physically combining all fractions to test outcomes, the system generates simulated pool metrics by computationally modeling the results of different fraction combination scenarios, allowing evaluation of multiple possibilities without physical experimentation.
Solution Approach 2:
The system performs preliminary analysis by simulating pool metrics for different fraction combinations before actual pooling occurs. This advance computational evaluation identifies optimal fraction selections, allowing the system to prepare recommended combinations in advance rather than reacting to results after pooling.
3Reliability
If manual fraction selection is used, then expert judgment can be applied, but consistent quality and yield suffer as the task becomes mundane or impossible
Solution Approach 1:
The system implements feedback by simulating pool metrics for different fraction combinations and using these simulated results to refine and improve fraction selection recommendations. The automated system continuously evaluates outcomes and adjusts selections to maintain optimal quality and yield consistency.
Solution Approach 2:
The system changes the state of fraction selection from manual discrete decisions to automated continuous optimization. By transforming the selection process into a computational parameter optimization problem, the system can evaluate multiple parameters simultaneously and identify optimal combinations that maximize quality and yield consistency.
Data Source
AI summary
A method for manufacturing nucleic acid molecules, including: obtaining, using a processor, a plurality of fractions from a nucleic acid synthesis procedure; obtaining, using the processor, characterization information regarding each of the plurality of fractions, the characterization information including mass spectrometry and liquid chromatography data for each of the plurality of fractions; identifying, using the processor, a subset of the plurality of fractions to combine to generate a simulated pool based on a metric; simulating, using the processor, a predicted metric for the simulated pool based on identifying the subset of the plurality of fractions to combine; and providing, using the processor, information identifying the subset of fractions to a user to combine into a combined pool based on simulating the predicted metric for the simulated pool.