Plug flow versus stirred tank reactor flow characteristics in two‐compartment scale‐down bioreactor: Setup‐specific influence on the metabolic phenotype and bioprocess performance of Corynebacterium glutamicum

Abstract

In the last two decades, scale‐down studies based on compartmented reactor setups became the standard procedure to mimic inhomogeneous cultivation conditions. In the academic field and with application to industrial‐scale, two basic scale‐down bioreactor configurations both showing a stirred tank reactor (STR) as main compartment predominate this research field. The connection to a plug flow reactor (PFR) generates oscillatory gradients with a distinct residence time of the culture, while the STR provides a broad residence time distribution leading to more heterogeneous oscillations. The influence of these opposed hydrodynamic profiles for their applicability for scale‐down bioreactor setups as well as their specific influence on the metabolic phenotype of l‐lysine producing Corynebacterium glutamicum DM1800 strain was investigated. Batch cultivations under oscillatory oxygen deprivation and substrate excess were carried out in STR–PFR and STR–STR scale‐down devices. In both setups, the induced inhomogeneity resulted in a reduction of growth rate and increased the l‐lactate and l‐glutamate by‐product formation, while biomass and product yields stayed nearly constant. Apart from differing side‐product levels, very similar results were observed when comparing the metabolic phenotype and bioprocess performance of STR–PFR and STR–STR configuration, although opposed back mixing profiles were present.

Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines

“…In our simulations extracellular conditions fluctuate at timescales of seconds. However, current SD simulators typically employ fluctuation timescales of 100 − 500 s based on . We consider τ circ to be more representative for extracellular substrate variations between the extreme values, with smaller fluctuations occurring at even shorter timescales.…”

“…This can be done via the use of so‐called “scale‐down (SD) simulators” . The design parameters and operating conditions of these simulators are currently often chosen on the basis of intuition or engineering correlations, for example the circulation rate is often based on the vessel mixing time , or chosen as a variable . Whether the magnitude and frequency of fluctuations observed by organisms based on this assumption are representative is, however, questionable.…”

Metabolic profile of 1,5‐diaminopentane producing Corynebacterium glutamicum under scale‐down conditions: Blueprint for robustness to bioreactor inhomogeneities

“…Although intermediary formation of acidic by-product has been identified in previous studies (K€ aß et al, 2014b;Lemoine et al, 2015;Limberg et al, 2016;Schilling et al, 1999), the basis of its metabolic robustness on molecular level is still not understood. Although intermediary formation of acidic by-product has been identified in previous studies (K€ aß et al, 2014b;Lemoine et al, 2015;Limberg et al, 2016;Schilling et al, 1999), the basis of its metabolic robustness on molecular level is still not understood.…”

A Toolbox of Genetically Encoded FRET-Based Biosensors for Rapid l-Lysine Analysis

“…The offline determination of l -lysine was performed by HPLC as described by Limberg et al [40] and using the Ninhydrin assay as described by Unthan et al [18]. …”