Biotech Digital Twin Modeling for Predictive Process Optimization
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Solution Overview
Problem
Current Digital Twins lack predictive qualities and are costly to create due to the complexity of biotechnological processes, with insufficient experimental data and unknown cellular mechanisms, limiting their application in biotechnological production processes.
Innovation Solution
A Digital Twin is developed by combining a cell model, reactor model, growth model, and extracellular reaction kinetics with machine learning, utilizing quasi-stationary intracellular concentrations and standard measurement data from biopharmaceutical processes, and applying well-known metabolic networks and data-driven learning to automate training and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional biotechnological process models are used, then model creation cost is reduced, but predictive qualities are insufficient
Solution Approach 1:
The patent combines traditional biotechnological process models (cell model, reactor model, growth model, extracellular reaction kinetics) with machine learning algorithms to create a hybrid Digital Twin system. This merging allows the system to leverage the interpretability of traditional models while gaining the predictive power of machine learning, directly resolving the contradiction between predictive quality and model complexity
Solution Approach 2:
The patent introduces an intermediary layer that maps easily measurable process variables (substrate consumption, product formation, biomass growth) to intracellular metabolic states. This intermediary approach enables predictive capabilities without requiring direct measurement or modeling of complex intracellular mechanisms, thus improving predictive quality while managing model complexity
2Measurement precision
If comprehensive experimental data is collected for model training, then model accuracy is improved, but data acquisition cost and time increase
Solution Approach 1:
The patent applies partial action by selecting only the most critical and easily measurable process variables (substrate consumption rates, product formation rates, biomass growth) for model training. This selective approach achieves sufficient model accuracy without requiring comprehensive collection of all possible experimental data, thereby reducing data acquisition time and cost while maintaining adequate predictive performance
3Reliability
If detailed cellular mechanisms are modeled, then model predictive power is improved, but model creation cost increases due to complexity
Solution Approach 1:
The patent extracts and focuses on the essential extracellular reactions and observable metabolic fluxes that drive biotechnological production processes. By taking out only the critical components (substrate uptake, product secretion, biomass growth) and representing them through a simplified Digital Twin framework combined with machine learning, the system achieves predictive power without incurring the high costs of detailed intracellular mechanism modeling
4Measurement precision
If Digital Twin is customized for specific processes and products, then model accuracy is improved, but adaptability to different processes decreases
Solution Approach 1:
The patent designs the Digital Twin framework with universal components that can be applied across different biotechnological processes, cell types, and product formats. The core architecture (combining process models with machine learning) remains consistent, while only the specific parameters and training data need to be adapted. This universal design enables the system to maintain high accuracy for specific processes while being easily adaptable to new applications, directly resolving the contradiction between accuracy and versatility
Data Source
AI summary
A new method for the automatic generation and validation of a Digital Twin for the production of biotechnological products and the application of the Digital Twin for the purpose of increasing product concentration, productivity, biomass concentration and product quality by optimizing media composition and/or feeding profiles. The Digital Twin can be linked directly to production for online optimization or offline for decision support.