physical environment or the virtual environment models with bidirectional
exchange of information. Typical changes could include the size of a bioreactor,
variations of cell culture, and process parameter controls.
• Finally, a more advanced level provides the ability to simulate situations in the
virtual environment that can then be applied to predict evolution in the physical
environment and/or direct changes to the physical phenomenon. In the context of
Biopharma, digital twins’ potential scenarios would range from adapting control
strategies of cell fermentation to optimizing supply chain policies such as stock
levels of transport preferences. In this case, the digital twin is in a continuous
learning state (supervised or unsupervised).
2 Technical Prerequisites and Components of Digital Twins
2.1 Context
So, in a nutshell, a digital twin consists of: The physical or biological product, the
virtual manifestation, and the seamless and bidirectional connection between these
two elements. Prerequisites and components will increase as we build increasingly
sophisticated levels of twins.
The most essential prerequisite of a digital twin is to have an operational goal and
defined value. Key examples would be higher yield for a process, lower variability
and deviations, or better asset utilization. It is, however, one of the key missed
prerequisites in many projects.
For the first level, static physical entity and virtual model with one-directional
data connection, the main prerequisites are the following:
• Sensors on the physical system that can measure its state
• Connectivity to establish the link between the physical and virtual environment
• A virtual model of the physical assets (typically a 3D model in engineering
related scenarios but could be also more abstract such as cell models)
The second level of twin where we manage lifecycle changes of both physical
entity and virtual models requires the additional components:
• An asset framework to manage the relationship between sensors and the
virtual model
• Configuration management of the different components
And to complete the list, for the third level of twin where the virtual model takes
on a life of its own and starts being used to drive change in the physical entity, we
have these additional elements:
• A dynamic virtual model (that can evolve independently from the “real” system)
• Data from executions of the system in variable conditions
• Data modeling and ontologies
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exchange of information. Typical changes could include the size of a bioreactor,
variations of cell culture, and process parameter controls.
• Finally, a more advanced level provides the ability to simulate situations in the
virtual environment that can then be applied to predict evolution in the physical
environment and/or direct changes to the physical phenomenon. In the context of
Biopharma, digital twins’ potential scenarios would range from adapting control
strategies of cell fermentation to optimizing supply chain policies such as stock
levels of transport preferences. In this case, the digital twin is in a continuous
learning state (supervised or unsupervised).
2 Technical Prerequisites and Components of Digital Twins
2.1 Context
So, in a nutshell, a digital twin consists of: The physical or biological product, the
virtual manifestation, and the seamless and bidirectional connection between these
two elements. Prerequisites and components will increase as we build increasingly
sophisticated levels of twins.
The most essential prerequisite of a digital twin is to have an operational goal and
defined value. Key examples would be higher yield for a process, lower variability
and deviations, or better asset utilization. It is, however, one of the key missed
prerequisites in many projects.
For the first level, static physical entity and virtual model with one-directional
data connection, the main prerequisites are the following:
• Sensors on the physical system that can measure its state
• Connectivity to establish the link between the physical and virtual environment
• A virtual model of the physical assets (typically a 3D model in engineering
related scenarios but could be also more abstract such as cell models)
The second level of twin where we manage lifecycle changes of both physical
entity and virtual models requires the additional components:
• An asset framework to manage the relationship between sensors and the
virtual model
• Configuration management of the different components
And to complete the list, for the third level of twin where the virtual model takes
on a life of its own and starts being used to drive change in the physical entity, we
have these additional elements:
• A dynamic virtual model (that can evolve independently from the “real” system)
• Data from executions of the system in variable conditions
• Data modeling and ontologies
Digital Twins: A General Overview of the Biopharma Industry
169