Reproduction of Large-Scale Bioreactor Conditions on Microfluidic Chips

Abstract

Microbial cells in industrial large-scale bioreactors are exposed to fluctuating conditions, e.g., nutrient concentration, dissolved oxygen, temperature, and pH. These inhomogeneities can influence the cell physiology and metabolism, e.g., decelerate cell growth and product formation. Microfluidic systems offer new opportunities to study such effects in great detail by examining responses to varying environmental conditions at single-cell level. However, the possibility to reproduce large-scale bioreactor conditions in microscale cultivation systems has not yet been systematically investigated. Hence, we apply computational fluid dynamics (CFD) simulations to analyze and compare three commonly used microfluidic single-cell trapping and cultivation devices that are based on (i) mother machines (MM), (ii) monolayer growth chambers (MGC), and (iii) negative dielectrophoresis (nDEP). Several representative time-variant nutrient concentration profiles are applied at the chip entry. Responses to these input signals within the studied microfluidic devices are comparatively evaluated at the positions of the cultivated cells. The results are comprehensively presented in a Bode diagram that illustrates the degree of signal damping depending on the frequency of change in the inlet concentration. As a key finding, the MM can accurately reproduce signal changes that occur within 1 s or slower, which are typical for the environmental conditions observed by single cells in large-scale bioreactors, while faster changes are levelled out. In contrast, the nDEP and MGC are found to level out signal changes occurring within 10 s or faster, which can be critical for the proposed application.

Dynamic Environmental Control in Microfluidic Single‐Cell Cultivations: From Concepts to Applications

“…For 2D chambers (Figure 1B) nutrient availability can be deteriorated, and exchange of the complete chamber volume ranges between 10 s and minutes ( Figure A). This depends on chamber geometry and size, type of supply (convection and diffusion), and the number of nutrient inlets [ 4,75,76 ] because the nutrient exchange in the cultivation chamber occurs only via diffusion. The exchange of nutrients within the chamber is the limiting factor for high‐frequency DCE applications.…”

“…B) Reproduction of environmental profile with different cultivation chamber designs. [ 76 ] C) Bode diagram compares responses for three chamber designs. [ 76 ]…”

“…The “quality” of reproduction of a defined environmental profile generally depends on the cultivation chamber design which is important for a high precision of the DCE. Ho et al [ 76 ] have investigated this in detail by advanced computational fluid dynamics (CFD) modeling studies. In a first step, they investigated which cultivation chamber design can be used to create DCE at second and subsecond resolution.…”

“…Both nDEP and 2D systems completely miss the first peak and can by far not reproduce the amplitude of the second peak (44% for nDEP and 17% for 2D). In a next step Ho et al [ 76 ] systematically investigate features of which frequency in DCE profiles can be produced at the cell surface on the microfluidic chip and cultivation chamber designs. Therefore, they use sinus signals with different frequencies.…”

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

“…Such rapid simulations may find applications in process control (H. J. Noorman & Heijnen, ), and allow for systematic screening and reactor designs; combining simulations with nonlinear optimization routines such as SIEMENS HEEDS may even automate searching for the design optimum (https://www.plm.automation.siemens.com/global/en/products/simcenter/simcenter-heeds.html). Lifelines collected from these simulations may be reproduced with microfluidics rather than bench‐scale downscaling systems, as microreactors allow greater control over the imposed fluctuations (Haringa et al, ; Ho et al, ) and the advances in single‐cell omics and effluent analysis (Dusny, Lohse, Reemtsma, Schmid, & Lechtenfeld, ) may soon allow quantifying the metabolic response on a single‐cell level. The lessons learned may be used to design and evolve strains that are optimized for production under plant‐scale conditions; while traditionally the focus is on reactor design optimization (to make the plant as much alike the lab as possible), it is, in the end, the combination of reactor, strain and operating conditions that need to be optimized.…”