holds true for the microorganism even with the extracellular fluctuations calculated;
the cellular response can only be representative if and only if it is based on real-life
behavior of the cell. The fact that the macro-twin (digital bioreactor) and micro-twin
(digital microorganism) go hand in hand has another advantage other than being
representative. The timescales indicate how fine the resolution of the computations
should be and hence allowing to a have an idea of what resources would be needed
beforehand. The main traits of interest in the biological side are associated with the
sensitivity of the organism to different occurrences and how fast and on what level
the response takes place. Another aspect to focus on is to evaluate the response
initiation and termination. Once this is set, the cellular machinery involved can be
included in the micro-twin with hopes of shedding light on the effect of complex
biological features on an industrial bioprocess. Accordingly, proper data are crucially needed to develop a thorough mechanistic understanding [6, 11, 13, 49].
3 Lifeline Analysis in Practice
In this section readers will be introduced to an exemplified bioprocess for creating a
holistic idea of this particular EL concept in a practical manner. This will serve as a
foundation to highlight strengths and bottlenecks presented by the technique. It is
worth noting that the simulation environment (here: ANSYS Fluent 2019 R1) might
have some practical superiority to other software packages, but the basics remain the
same and are not software specific.
Figure 1 aims to illustrate how interconnected elements of a multiphase simulation can be thereby disclosing the complexity of the network. On the one hand, one
Mass balances
Momentum balances
Heat balances
Phases
• Liquid phase: Newtonian or non-Newtonian
• Gas phase: PBM, bubble diameter
• Biomass phase (cell tracking)
Exchanges
• Mass transfer
• Heterogeneous reactions
• Drag force
• Lift
• Turbulent interactions
• Turbulent dispersion
Fig. 1 Various sets of interacting equations which are of interest in a typical CFD simulation of a
multiphase bioreactor
Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance. . .
237
the cellular response can only be representative if and only if it is based on real-life
behavior of the cell. The fact that the macro-twin (digital bioreactor) and micro-twin
(digital microorganism) go hand in hand has another advantage other than being
representative. The timescales indicate how fine the resolution of the computations
should be and hence allowing to a have an idea of what resources would be needed
beforehand. The main traits of interest in the biological side are associated with the
sensitivity of the organism to different occurrences and how fast and on what level
the response takes place. Another aspect to focus on is to evaluate the response
initiation and termination. Once this is set, the cellular machinery involved can be
included in the micro-twin with hopes of shedding light on the effect of complex
biological features on an industrial bioprocess. Accordingly, proper data are crucially needed to develop a thorough mechanistic understanding [6, 11, 13, 49].
3 Lifeline Analysis in Practice
In this section readers will be introduced to an exemplified bioprocess for creating a
holistic idea of this particular EL concept in a practical manner. This will serve as a
foundation to highlight strengths and bottlenecks presented by the technique. It is
worth noting that the simulation environment (here: ANSYS Fluent 2019 R1) might
have some practical superiority to other software packages, but the basics remain the
same and are not software specific.
Figure 1 aims to illustrate how interconnected elements of a multiphase simulation can be thereby disclosing the complexity of the network. On the one hand, one
Mass balances
Momentum balances
Heat balances
Phases
• Liquid phase: Newtonian or non-Newtonian
• Gas phase: PBM, bubble diameter
• Biomass phase (cell tracking)
Exchanges
• Mass transfer
• Heterogeneous reactions
• Drag force
• Lift
• Turbulent interactions
• Turbulent dispersion
Fig. 1 Various sets of interacting equations which are of interest in a typical CFD simulation of a
multiphase bioreactor
Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance. . .
237