Biological Process Setup Using Learned Parameter Feedback
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Solution Overview
Problem
Existing methods for setting up biological processes, such as cell culture growth, are inefficient and time-consuming, relying heavily on manual experimentation to find optimal growth conditions and dosages, which are skill-dependent and costly.
Innovation Solution
A computer-assisted method and apparatus that automatically captures and evaluates process states to specify learned set-up parameters, using machine learning and random or deterministic decision-making to optimize biological processes efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual experimentation is used to find optimal growth conditions, then the process can be carried out with simple equipment, but the time and cost required increase significantly
Solution Approach 1:
The system performs self-learning through automated experimentation where the apparatus automatically adjusts parameters, captures process states, evaluates results, and iterates to find optimal conditions without human intervention, thereby reducing time loss while maintaining equipment simplicity
Solution Approach 2:
The system performs preliminary automated experiments to learn optimal parameters before actual production use, capturing process states and evaluating them against objectives to establish optimal conditions in advance, reducing the time needed for future experiments
2Ease of manufacture
If manual experimentation is used to find optimal dosages, then the equipment requirements remain simple, but the skill dependency and cost increase
Solution Approach 1:
The system replaces manual human operation with an automated control system that uses algorithms to adjust parameters, capture states, and evaluate results, eliminating skill dependency while keeping equipment requirements simple through software-based intelligence
Solution Approach 2:
The system implements automated feedback loops where process states are continuously captured and evaluated against objectives, and parameter adjustments are made based on evaluation results, replacing human judgment with automated decision-making that reduces skill dependency
3Measurement precision
If repeated experiments are conducted to obtain optimum dosage, then accurate results can be achieved, but the process becomes very time-consuming and costly
Solution Approach 1:
The system performs continuous automated experimentation without interruption, automatically adjusting parameters and conducting experiments in sequence based on previous results, maintaining measurement precision while dramatically improving productivity through uninterrupted automated operation
Solution Approach 2:
The system dynamically adapts its experimental approach by learning from previous results and adjusting subsequent experiments based on evaluated process states, allowing it to efficiently converge on optimal dosages with fewer iterations while maintaining accuracy
Data Source
AI summary
A method for setting up an apparatus (1) for biological processes (3), in which process parameters are specified for a plurality of biological processes (3) with computer assistance, that for each biological process (3) a process state is automatically captured, that the particular process state is evaluated using a specified objective with computer assistance, and that from the evaluations the apparatus (1) is set up, with computer assistance, through specification of learned set-up parameters. In addition, an apparatus (1) for biological processes (3) is provided with which the proposed method can be carried out in a particularly advantageous manner