Culture Information Processing Device for Cell Growth Prediction
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Solution Overview
Problem
Current methods for culturing cells, such as iPS cells, lack efficient means to predict and optimize growth characteristics across multiple subculturing processes, making it difficult to determine the optimal timing and conditions for subsequent subculturing processes to achieve the required cell yield by a specific shipping date.
Innovation Solution
A culture information processing device that computes growth feature values from data acquired in a first subculturing process and uses these values, along with input culturing conditions, to calculate characteristics-related information for the next subculturing process, allowing for the determination of the starting date and adjustment of freezing periods to ensure timely cell yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cell culturing methods are used without predictive computation, then the culturing process is simple to operate, but it is impossible to accurately predict growth characteristics and determine optimal timing for subsequent subculturing processes
Solution Approach 1:
The system performs preliminary computation of growth feature values from first subculturing data before the second subculturing process begins. By calculating predicted growth characteristics in advance, the system enables proactive decision-making about optimal subculturing timing and conditions, rather than reacting to observed growth after the fact.
Solution Approach 2:
The system creates a computational model that copies and extends the growth patterns observed in the first subculturing process to predict the second subculturing process. By fitting model formulas to acquired data and computing growth feature values, the system generates a virtual representation of expected growth characteristics without requiring identical experimental conditions.
2Reliability
If multiple subculturing processes are monitored and analyzed in detail, then growth characteristics can be accurately understood, but the time and computational resources required increase significantly
Solution Approach 1:
The system extracts only the essential growth feature values from the complete set of acquired data in the first subculturing process. By identifying and computing key parameters that characterize growth patterns, the system avoids the need to analyze every detail of the raw data, significantly reducing processing time while maintaining prediction accuracy.
Solution Approach 2:
The system transforms raw acquisition data into computed growth feature values through parameter transformation. By fitting model formulas to the data and extracting characteristic parameters, the system converts complex, time-consuming raw data into condensed, meaningful metrics that can be quickly processed and used for predictions.
3Manufacturing precision
If the starting date and freezing period are fixed without adjustment capability, then the culturing schedule is simple to manage, but it is impossible to ensure the required cell number is obtained by the shipping date
Solution Approach 1:
The system dynamically adjusts the starting date and freezing period based on computed predicted growth characteristics. Rather than using fixed, predetermined schedules, the system calculates optimal timing parameters that adapt to the specific growth patterns observed in the first subculturing process, enabling precise control over when the required cell number will be achieved.
Solution Approach 2:
The system uses feedback from the computed growth feature values and predicted characteristics to determine optimal culturing conditions. By analyzing the computed predictions and using them to adjust the starting date and freezing period, the system creates a closed-loop control mechanism that ensures the required cell number is obtained by the shipping date while maintaining operational simplicity through automated decision-making.
Data Source
AI summary
A culture information processing device includes: a feature value computing unit that computes growth feature values indicating features of growth characteristics of cells from data acquired in a particular first subculturing process selected from among a plurality of subculturing processes included in a culture period of the cells; a condition setting unit that sets culturing conditions of a second subculturing process one process after the first subculturing process; and an information computing unit that computes, on the basis of the growth feature values computed by the feature value computing unit and the culturing conditions set by the condition setting unit, characteristics-related information related to growth characteristics in the second subculturing process.