Cell Sorter Optimizing Biomass Yield Under Dynamic Culture Conditions
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Solution Overview
Problem
Conventional methods for analyzing anti-cancer agents and bioinformatics struggle to identify optimal combinations of cells or microorganisms that can effectively inhibit cancer under various environments, and existing portfolio selection methods do not apply to bioinformatics, making it difficult to determine suitable combinations for stable cancer inhibition and efficient biomass production.
Innovation Solution
A cell selecting apparatus and method that maps production outputs and variables indicating variation in a yield risk space, using optimization techniques such as Pareto optimization or genetic algorithms to calculate an optimum combination of cells or microorganisms, considering multiple disturbances and their probability of occurrence, to maximize production outputs while managing risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cells are cultured under fixed culture conditions, then proliferation inhibition can be examined, but only cells fitted to the fixed condition can proliferate and survive selectively, making it difficult to specify anti-cancer agents that can stably inhibit cancers under various environments
Solution Approach 1:
The patent applies dynamics by transitioning from static fixed culture conditions to dynamic fluctuating culture conditions that simulate in vivo environmental variations. The culture conditions are continuously changed over time to create a dynamic selection pressure that favors cells with adaptability rather than just fitness for a single condition.
Solution Approach 2:
The patent implements parameter changes by systematically varying multiple culture condition parameters (nutrient composition, pH, temperature, oxygen levels) over time. These parameter fluctuations mimic the diverse and changing environments that cancer cells encounter in the body, enabling selection of agents that maintain efficacy across varying conditions.
2Adaptability or versatility
If conventional portfolio selection methods are applied to bioinformation, then investment diversification can be achieved, but it cannot determine an optimum combination of cells or microorganisms
Solution Approach 1:
The patent introduces a computational optimization algorithm as an intermediary between portfolio selection theory and cell combination determination. This intermediary translates financial portfolio concepts into biological applications by calculating optimal cell combinations based on measured proliferation data under multiple conditions, bridging the gap between theoretical diversification and practical optimization.
Solution Approach 2:
The patent achieves universality by creating a multi-functional system that can handle both investment portfolio optimization and cell combination optimization using the same mathematical framework. The system universally applies portfolio selection principles across different domains (finance and biology) while adapting to domain-specific requirements through appropriate data input and interpretation.
3Ease of manufacture
If anti-cancer agents are screened under fixed culture conditions, then screening process is simple, but it is difficult to specify agents that can stably inhibit cancers under various environments
Solution Approach 1:
The patent applies preliminary action by pre-establishing a multi-condition culture system that automatically fluctuates between different environmental states. This preliminary setup enables simultaneous testing of anti-cancer agents across multiple conditions without requiring separate screening experiments for each condition, thereby maintaining simplicity while improving reliability.
Data Source
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AI summary
The present invention sets, for each cells or each condition, a production output of the cells or the biomass based on the measured data and a variable indicating an extent of variation of the production output, calculates a variable range of the production output and the variable in case of combining the multiple cells or conditions based on the production output and the variable set, and calculates an optimum combination within the variable range by using an optimization method.