Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Abstract

A soft sensor approach is described for controlling metabolic overflow from mixed acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.

Virtual bioreactor cultivation for operator training and simulation: application to ethanol and protein production

“…Recombinant GFP was cultured in a 6 L fed‐batch bioreactor (Belach Bioteknik AB, Sweden). A previously reported cultivation procedure and analysis protocol was used …”

Control of specific carbon dioxide production in a fed-batch culture producing recombinant protein using a soft sensor

“…This could not be met with previous soft sensors used by us (Warth et al, 2010;Gustavsson and Mandenius., 2013) where delay times of up to 20 min seriously hampered the controllability of the system. An alternative setup would have been to extend the gas analyser with on-line measurement of O 2 , hence allowing monitoring of oxygen uptake rate (OUR) as well as respiratory quotient (RQ) by combining CO 2 and O 2 measurements (Levisauskas et al, 1996;Jeude et al, 2006).…”

“…The setpoint value for q CO 2 was arbitrarily chosen based on previous experience with the GFP expression system (Warth et al, 2010;Gustavsson and Mandenius, 2013;Gerlach et al, 2014). The values were here changed by trail-and-error and the effect in growth was observed.…”

“…A third approach has been estimation of the culture's growth state from responses in dissolved oxygen to feed transients measured by a DO-electrode (Konstantinov et al, 1990;Åkesson et al, 1999, 2001. Other examples of growth control are exploitation of multi-sensor based trajectories (Bachinger et al, 2000;Cimander and Mandenius, 2002;Cimander et al, 2003;Sagmeister et 2013), mass balance equations (Cornelissen et al, 2003) and estimations from mixed acid products (Xu et al, 1999;Gustavsson and Mandenius, 2013).…”

Genetically-encoded biosensors for monitoring cellular stress in bioprocessing

“…By monitoring the accumulation of acetate and other overflow metabolites, they were able to increase the batch-to-batch reproducibility [40]. Recent reports from the synthetic biology community have set new milestones by showing precise control over biological behaviour using sophisticated control engineering algorithms.…”