access to specific metabolic data in industrial cases [68]. Versatility of scale-down
reactors allows them to shed light on new characteristics of the process from
repetitive short stimuli faced by cells [17] and cell cycle in a yeast fermentation
[27] to mammalian cell cultures with specific interest in strain rate and carbon
dioxide gradients [69]. Once it is determined that such systems are not representative
of the desired conditions, more complex designs are considered if need be [62].
5 Advantages and Considerations
In this section we briefly go through some of the reasons that made EL simulations
relevant in the scaling up and down. We will also discuss some of the key considerations which should be address accordingly.
Compared to other methods that tend to address the question of heterogeneity, EL
offers intrinsically superior features compared to other methods. Although noticeable attempts were made to equip CFD simulations with a PBM for different classes
of growth rate [21], including a microorganism’s history is one of the essential
aspects that encourage the use of EL simulations over PBMs. This can serve as a
valuable tool in evaluating transient phenomena. Another benefit of using cell
tracking is that it allows integrating of multiple variables in growth rate calculations
without any major compromise in calculation time [36].
A versatile tool such as EL should be handled delicately. Assumptions from a
wide spectrum of phenomena from turbulence to cellular heterogeneity and regulatory networks within the cell have to be modeled with utmost attention to balance the
numerical burden with biological accuracy. Since the pioneering work of [36],
significant efforts have been made to find a robust foundation for E-L simulations.
Here are some of the lessons learned along the way:
• Flow field considerations
Since the start of EL simulations in bioprocess engineering, computational
resources have become more abundant and yet still are not enough to complete a
fully coupled transient simulation in a matter of hours or days. Having said that it
might be the time for the scale-down engineering to revisit some of the basics, that
could improve the reality of the simulations [13, 22]. The state-of-the-art EL
simulations mainly take place in a stepwise fashion introducing some artifacts to
the solution, for instance, when the turbulence from a transient solution is frozen in a
snapshot. Accordingly, one may wonder about the validity of lifelines extracted at
that distinct time window. Still, even with such compromises, EL is offering
valuable insights especially to the heterogeneity within the bioreactor.
• Particle tracking considerations
One of the key points is to consider the coupling mechanisms between Eulerian
phases and the Lagrangian particle. Basic assumption is that the condition St < 0.01
holds true for a single cell, and hence it can be assumed that the cell itself has no
246
C. S. S. Hajian et al.
reactors allows them to shed light on new characteristics of the process from
repetitive short stimuli faced by cells [17] and cell cycle in a yeast fermentation
[27] to mammalian cell cultures with specific interest in strain rate and carbon
dioxide gradients [69]. Once it is determined that such systems are not representative
of the desired conditions, more complex designs are considered if need be [62].
5 Advantages and Considerations
In this section we briefly go through some of the reasons that made EL simulations
relevant in the scaling up and down. We will also discuss some of the key considerations which should be address accordingly.
Compared to other methods that tend to address the question of heterogeneity, EL
offers intrinsically superior features compared to other methods. Although noticeable attempts were made to equip CFD simulations with a PBM for different classes
of growth rate [21], including a microorganism’s history is one of the essential
aspects that encourage the use of EL simulations over PBMs. This can serve as a
valuable tool in evaluating transient phenomena. Another benefit of using cell
tracking is that it allows integrating of multiple variables in growth rate calculations
without any major compromise in calculation time [36].
A versatile tool such as EL should be handled delicately. Assumptions from a
wide spectrum of phenomena from turbulence to cellular heterogeneity and regulatory networks within the cell have to be modeled with utmost attention to balance the
numerical burden with biological accuracy. Since the pioneering work of [36],
significant efforts have been made to find a robust foundation for E-L simulations.
Here are some of the lessons learned along the way:
• Flow field considerations
Since the start of EL simulations in bioprocess engineering, computational
resources have become more abundant and yet still are not enough to complete a
fully coupled transient simulation in a matter of hours or days. Having said that it
might be the time for the scale-down engineering to revisit some of the basics, that
could improve the reality of the simulations [13, 22]. The state-of-the-art EL
simulations mainly take place in a stepwise fashion introducing some artifacts to
the solution, for instance, when the turbulence from a transient solution is frozen in a
snapshot. Accordingly, one may wonder about the validity of lifelines extracted at
that distinct time window. Still, even with such compromises, EL is offering
valuable insights especially to the heterogeneity within the bioreactor.
• Particle tracking considerations
One of the key points is to consider the coupling mechanisms between Eulerian
phases and the Lagrangian particle. Basic assumption is that the condition St < 0.01
holds true for a single cell, and hence it can be assumed that the cell itself has no
246
C. S. S. Hajian et al.