Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors
Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a
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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors Christopher Sarkizi Shams Hajian, Julia Zieringer, and Ralf Takors
Contents 1 Introduction 2 Embedding Cells in Microenvironmental Heterogeneities of Bioreactors 2.1 The Core Idea of Lifeline Analysis 2.2 How to Get Biologically Sound Readouts? 3 Lifeline Analysis in Practice 3.1 Eulerian Simulation Setup 3.2 Eulerian Simulation Outputs 3.3 Lagrangian Setup 3.4 Lagrangian Readouts 4 Scale-Down Examples and Methods from the Literature 5 Advantages and Considerations 6 Conclusion and Outlook References
Abstract Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not
C. S. S. Hajian, J. Zieringer, and R. Takors (*) Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany e-mail: [email protected]
C. S. S. Hajian et al.
achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed. Graphical Abstract qs
Gradient establishment + Cell tracking
Lifeline analysis
Metabolic engineering
Representative scale down reactor
t
Reactor setup
Optimized mirco-organism
Keywords Cell life-lines, Digital twins, Large-scale bioprocesses, Scale-down, Scale-up
1 Introduction The physiological state of microorganisms and its impact on growth and product formation are the result of complex interactions between the cellular environment and the cells. Large-scale studies have shown that homogeneous culture conditions are difficult to establish. This is expressed in the common correlation for stirred tank 1=3 reactors indicating that mixing time τmix is proportional to VP (with P as power input and V as volume) pinpointing to increasing mixing times with reduced powerto-volume inputs. The latter occurs typically in large-scale bioreactors because of limiting power supply, engines, gearings, etc. Nevertheless process engineering and
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