Abstract Summary (Max 250 words)
Addressing mixing issues in an industrial bioreactor requires describing the variability within cellular populations as well as the two-way coupling between cells behavior and concentration gradients developing in large-scale aerated, stirred reactors. A multivariate population balance model is used to discriminate individuals according to their properties and, coupled with mass balances. It provides a framework for describing the dynamics of cell populations driven by internal noise and external forcing. We developed a particle-based Monte Carlo code to address these challenges in a general way [1]. It combines a CFD-based compartment model for scalars, a stochastic cell transport model and particle-based methods for biological source term calculations. Cell division and the partitioning of properties at division are treated explicitly and act as a source of diversification. Mixing, gas–liquid mass transfer, population dynamics over time and space, uptake adaptation, metabolically governed cell division, and cell elongation processes are included [2]. We validated the code through a comparison with available analytical solutions [3] considering length, age, instantaneous elongation rate, and metabolic reaction rates as cell properties. With this tool, we predict the emergence of sub-population with distinct metabolic behavior as a consequence of the exposure to heterogeneous concentration fields. [1] J. Morchain, C. Mayorga, P. Villedieu, A. Liné, A dynamic compartment model for spatially heterogeneous reactors: Scalar and Monte-Carlo particle mixing, https://doi.org/10.1016/j.cherd.2024.04.014. [2] K. Biswas, N. Brenner, Universality of phenotypic distributions in bacteria, https://doi.org/10.1103/PhysRevResearch.6.L022043. [3] J. Cullum, M. Vicente, Cell growth and length distribution in Escherichia coli, https://doi.org/10.1128/jb.134.1.330-337.1978.
