Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein

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

“…Other signal disturbances due to changes in flow pattern caused by impeller rate adjustments were compensated for by a filter function implemented the instrument [11].…”

“…A variety of combinations of soft sensor is reported for process monitoring and control applications in industry [4][5][6][7]. During the past ten years, the soft sensors have also extensively been discussed in biotechnology, in particular for the monitoring and control of bioreactors [8][9][10][11][12][13][14]. Biomass growth, for example, has been estimated by soft sensors based on base titration [15,16], absorbance measurement [11], and exhaust gas analysis [12] using mechanistic models.…”

“…During the past ten years, the soft sensors have also extensively been discussed in biotechnology, in particular for the monitoring and control of bioreactors [8][9][10][11][12][13][14]. Biomass growth, for example, has been estimated by soft sensors based on base titration [15,16], absorbance measurement [11], and exhaust gas analysis [12] using mechanistic models. Other soft sensors have used regression models, multivariate models, and neural networks [17] in combination with on-line fluorimetry [18] and multisensor arrays [19].…”

“…In microbial processes, examples include feedback control of specific growth rate from gas mass balance [11,12] and monitoring of oxygen and carbon dioxide gases for estimating biomass on-line [20][21][22]. In other studies the soft sensors have controlled the nutrient feeding to the fed-batch by monitoring key metabolites as acetate by on-line HPLC [11]. These results show that feedback control could be used successfully to effectively adjust the feed profile, thereby maximizing the feeding rate and minimizing the culture time as well as stabilizing the product yield.…”

“…As host-vector has been chosen the expression of the Green Fluorescent Protein (GFP) in E. coli, since it represents a typical biopharmaceutical protein 3 production system [24]. A previous study of this system by us [11] is here extended to include control of glucose overflow and MAF metabolites [25][26][27]. In order to simplify the control only one variable is controlled, namely the sum of the detectable metabolites.…”