“…Often the carbondioxide evolution rate is used for on-line estimation of biomass and growth rate. As a prerequisite for this application, however, the yield coecients must remain constant during the process (Estler, 1995;Gbewonyo et al, 1989;Montesinos et al, 1995;Petkov and Davis, 1996). Also, direct monitoring of recombinant protein production is only possible for proteins with special properties, e.g.,¯uorescence (Albano et al, 1998;DeLisa et al, 1999).…”
Human basic fibroblast growth factor (hFGF-2) was produced in high-cell density cultures of recombinant Escherichia coli using a temperature-inducible expression system. The synthesis rates of proteins were followed by two-dimensional gel electrophoresis of the (35)S-methionine-labeled proteom. After temperature induction of hFGF-2 synthesis, the rate of total protein synthesis per biomass increased by a factor of three, mainly as a result of the additional synthesis of hFGF-2 and heat-shock proteins. The synthesis rates of heat-shock proteins and constitutive plasmid-encoded proteins increased after the temperature upshift also in the control strain without hFGF-2 gene but followed time profiles different from the producing strain. The energy demand for the extra synthesis of plasmid-encoded and heat-shock proteins resulted in an elevated respiratory activity and, consequently, in a reduction of the growth rate and the biomass yield. A procedure was developed to relate the energy demand for the additional synthesis of these proteins to the generation of energy in the respiratory pathway. Specific energy production was estimated based on on-line measurable rates of oxygen consumption, or carbondioxide evolution and growth, respectively. In this way, the metabolic burden resulting from the synthesis of plasmid-encoded and heat-shock proteins was quantified from on-line accessible data.
“…Often the carbondioxide evolution rate is used for on-line estimation of biomass and growth rate. As a prerequisite for this application, however, the yield coecients must remain constant during the process (Estler, 1995;Gbewonyo et al, 1989;Montesinos et al, 1995;Petkov and Davis, 1996). Also, direct monitoring of recombinant protein production is only possible for proteins with special properties, e.g.,¯uorescence (Albano et al, 1998;DeLisa et al, 1999).…”
Human basic fibroblast growth factor (hFGF-2) was produced in high-cell density cultures of recombinant Escherichia coli using a temperature-inducible expression system. The synthesis rates of proteins were followed by two-dimensional gel electrophoresis of the (35)S-methionine-labeled proteom. After temperature induction of hFGF-2 synthesis, the rate of total protein synthesis per biomass increased by a factor of three, mainly as a result of the additional synthesis of hFGF-2 and heat-shock proteins. The synthesis rates of heat-shock proteins and constitutive plasmid-encoded proteins increased after the temperature upshift also in the control strain without hFGF-2 gene but followed time profiles different from the producing strain. The energy demand for the extra synthesis of plasmid-encoded and heat-shock proteins resulted in an elevated respiratory activity and, consequently, in a reduction of the growth rate and the biomass yield. A procedure was developed to relate the energy demand for the additional synthesis of these proteins to the generation of energy in the respiratory pathway. Specific energy production was estimated based on on-line measurable rates of oxygen consumption, or carbondioxide evolution and growth, respectively. In this way, the metabolic burden resulting from the synthesis of plasmid-encoded and heat-shock proteins was quantified from on-line accessible data.
“…The focus lays on the implementation, which can be not only used in scientific R&D laboratories, but also on the industrial plants level.Fig. 1Comparison of biomass estimators: a Lübbert [21], b Achle [25], c Simutis [22], d biomass estimation of this text, e Davis [26], f Barrigon [27]…”
Section: Comparative Analysis Of Biomass Estimatorsmentioning
BackgroundThe focus of this study is online estimation of biomass concentration in fed-batch cultures. It describes a bioengineering software solution, which is explored for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures. The experimental investigation of both cultures presents experimental validation results since the start of the bioprocess, i.e. since the injection of inoculant solution into bioreactor. In total, four strains were analyzed, and 21 experiments were performed under varying bioprocess conditions, out of which 7 experiments were carried out with dosed substrate feeding. Development of the microorganisms’ culture invariant generic estimator of biomass concentration was the main goal of this research.ResultsThe results show that stoichiometric parameters provide acceptable knowledge on the state of biomass concentrations during the whole cultivation process, including the exponential growth phase of both E. coli and S. cerevisiae cultures. The cell culture stoichiometric parameters are estimated by a procedure based on the Luedeking/Piret-model and maximization of entropy. The main input signal of the approach is cumulative oxygen uptake rate at fed-batch cultivation processes. The developed noninvasive biomass estimation procedure was intentionally made to not depend on the selection of corresponding bioprocess/bioreactor parameters.ConclusionsThe precision errors, since the bioprocess start, when inoculant was injected to a bioreactor, confirmed that the approach is relevant for online biomass state estimation. This included the lag and exponential growth phases for both E. coli and S. cerevisiae. The suggested estimation procedure is identical for both cultures. This approach improves the precision achieved by other authors without compromising the simplicity of the implementation. Moreover, the suggested approach is a candidate method to be the microorganisms’ culture invariant approach. It does not depend on any numeric initial optimization conditions, it does not require any of bioreactor parameters. No numeric stability issues of convergence occurred during multiple performance tests. All this makes this approach a potential candidate for industrial tasks with adaptive feeding control or automatic inoculations when substrate feeding profile and bioreactor parameters are not provided.
“…In order for the original signal to be cumulative, this study employs an integral approach, which is a good noise filter [25]. After integration, the improved Luedeking-Piret model in Equation (7) becomes more resistant to state variable noise [26]:…”
“…In order for the original signal to be cumulative, this study employs an integral approach, which is a good noise filter [25]. After integration, the improved Luedeking-Piret model in Equation (7) becomes more resistant to state variable noise [26]: stoichiometric parameters during the whole cultivation process. The convex estimation of coefficients k β2 , k β1 , k β0 , manifested in this study, shows that all of those coefficients obtain zero values in this parsimonious model.…”
The purpose of this study is to introduce an improved Luedeking–Piret model that represents a structurally simple biomass concentration approach. The developed routine provides acceptable accuracy when fitting experimental data that incorporate the target protein concentration of Escherichia coli culture BL21 (DE3) pET28a in fed-batch processes. This paper presents system identification, biomass, and product parameter fitting routines, starting from their roots of origin to the entropy-related development, characterized by robustness and simplicity. A single tuning coefficient allows for the selection of an optimization criterion that serves equally well for higher and lower biomass concentrations. The idea of the paper is to demonstrate that the use of fundamental knowledge can make the general model more common for technological use compared to a sophisticated artificial neural network. Experimental validation of the proposed model involved data analysis of six cultivation experiments compared to 19 experiments used for model fitting and parameter estimation.
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