Analyzing bioprocess heterogeneity from the microbial viewpoint: recent developments

There is considerable in the impact of environmental- and population heterogeneity on industrial bioprocess. One of the tools that has gained popularity is so-called ‘lifeline analysis’, where temporal fluctuations in the environment observed by a cell are analyzed. Analysis of such lifelines can provide insight in environmental stresses affecting the microbial response and thereby process performance, and forms a basis for bioreactor scale-down [1].

The most common form of lifeline analysis is done through Euler-Lagrange Computational Fluid Dynamics (CFD). A main bottlenecks for routine application of lifeline concepts is the high computational expense, but recent advances are alleviating this bottleneck. In particular, GPU-based Lattice-Boltzmann methods are promising: more accurate results are acquired with reduced computation time. We have utilized GPU-driven Lattice-Boltzmann simulations for parcel-based downscaling studies [2], and more recently, two-way coupling of structured biokinetics has been introduced in the Lattice-Boltzmann framework. These advances allow for much faster evaluation of performance snapshots of bioreactors, but CFD remains too computationally expensive to analyze full bioprocesses. To this end, stochastic particle tracking in compartment models with structured kinetics forms an interesting route. [3]

Besides this computational lifeline analysis, developments regarding flow-following sensor particles and the use of microfluidic single-cell cultivations to mimic lifelines experimentally open the door to a more interdisciplinary concept [4]. I will briefly discuss this broader concept, and provide a perspective how lifeline analysis can contribute to bioprocess evaluation, optimization and operation.

[1]: Haringa et al., Biochemical Engineering Journal (2018)

[2]: Haringa, Engineering in Life Sciences (2022)

[3]: Haringa, Tang, Noorman, Biotechnology & Bioengineering (2022)

[4]: Blöbaum, Haringa, Grünberger, Biotechnology Advances (2023)