Abstract

Industrial bioreactors often present spatial and temporal inhomogeneities in key variables, such as dissolved oxygen, pH, and substrate concentration, leading to physiological stress and reduced performance in microbial and mammalian cell cultures in terms of biomass and metabolites of interest in the industrial microbial processes. This comprehensive bibliometric study examines how these environmental fluctuations have been replicated and studied in laboratory-scale systems using scale-down bioreactor designs and computational tools. Bibliometric analysis, based on data retrieved from the Scopus database, was employed to quantitatively evaluate publication trends, research productivity by country and institution, and regional disparities in scientific output. This work analyzes the evolution of scale-down approaches from 1997 to 2024, covering experimental configurations and computational modeling strategies. Two-compartment systems – particularly stirred-tank reactor (STR)– plug-flow reactor (PFR) and STR–STR – remain the most widely used to replicate mixing limitations and gradient formation, while three-compartment designs and alternative feeding strategies enable more complex perturbation studies. Corynebacterium glutamicum, Escherichia coli, Penicillium chrysogenum, and mammalian cells have been the most studied strains in scale-down configurations. Computational tools have advanced from mass-balance and stochastic residence-time models to high-resolution computational fluid dynamics simulations that capture turbulence, multiphase flows, and cell-level dynamics. The integration of these physical and digital approaches is strengthening process understanding, enabling predictive scale-up, and supporting the design of robust, efficient industrial bioprocesses. Emerging trends in machine learning integration and high-throughput microsystems are also discussed as future avenues for optimizing bioprocess robustness.

Key words: bioprocess, scale-down, bioreactor, scale-up, fermentation