Unified Cell Culture Model for Simulating Process Phases
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Solution Overview
Problem
Existing cell culture process models are not adequately versatile to simulate and control different types and phases of cell culture processes, requiring separate models or parameter sets for each type and phase.
Innovation Solution
A computer-implemented method and system that uses a unified model described by coupled ordinary differential equations, incorporating both measurable and unmeasurable parameters, to simulate and control cell culture processes. This model estimates unmeasurable parameters using Bayesian inference and adjusts operating conditions to achieve desired cell growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models or parameter sets are constructed for different types and phases of cell culture processes, then the model can accurately represent each specific process type, but the device complexity and model construction effort increase significantly
Solution Approach 1:
The patent implements a universal model structure that can represent multiple cell culture process types (batch, fed-batch, continuous) and phases (exponential growth, stationary, harvest) through a single unified framework. The model uses common differential equations with parameters that can be adjusted based on the specific process type, eliminating the need to construct separate models for each scenario while maintaining accuracy.
Solution Approach 2:
The patent employs parameter changes to adapt the universal model to different process types and phases. By modifying specific parameter values (such as growth rates, death rates, and yield coefficients) within the unified model structure, the system can accurately represent various cell culture conditions without changing the underlying model architecture, thus reducing complexity while preserving reliability.
2Device complexity
If a unified model is used for all cell culture process types and phases, then the model construction effort is reduced, but the model may not adequately capture the specific characteristics of each process type
Solution Approach 1:
The patent implements a dynamic universal model where parameters can change over time and adapt to different process phases. The model includes time-varying parameters and conditional logic that allow it to dynamically adjust to represent specific process types (batch, fed-batch, continuous) and phases (exponential growth, stationary, harvest) while maintaining a single unified structure, thus achieving both simplicity and accuracy.
3Adaptability or versatility
If unmeasurable parameters are directly used in the model, then the model can represent complex biological processes, but the measurement and verification of these parameters becomes difficult
Solution Approach 1:
The patent implements feedback mechanisms where unmeasurable parameters (such as viable cell density, dead cell density, and lysed cell density) are estimated based on measurable outputs (substrate consumption, product formation, optical density). The model uses feedback from measurable parameters to continuously update and verify unmeasurable parameters, allowing the system to represent complex biological processes while maintaining the ability to verify parameter values through indirect measurement and validation.
Data Source
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AI summary
A computer-implemented method for simulating a cell culture process is provided. The method includes: obtaining (S10) measurable parameter values that are measured with respect to at least one operation of the cell culture process, the measurable parameter values being values of measurable parameters in a model of the cell culture process, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process; estimating (S20), using Bayesian inference with the obtained measurable parameter values, values of unmeasurable parameters in the model, wherein the model describes the cell culture process with coupled ordinary differential equations including the measurable parameters and the unmeasurable parameters, wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process; receiving (S30) one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process; simulating (S40) the cell culture process using: the model of the cell culture process; the estimated values of the unmeasurable parameters in the model; and the received one or more new measurable parameter values.