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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
Soft-sensor for on-line estimation of parameters of knowledge-based hybrid model of recombinant E. coli fed-batch cultivation Golzar Eydi, Valiollah Babaeipour *, Ahmad Reza Vali Biochemical Engineering Group, Biotechnology Research Center, Malek-Ashtar University, P.O. Box 19395-1949, Tehran, Iran; And; Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, P.O. Box 14395-1374, Tehran, Iran
[email protected]
Abstract Acetate The biomass concentration, the same as concentrations of substrates or products, a very important state variable of almost every bioprocess, can’t often be measured on-line, due to the lack of reliable and cheap sensors, which makes this set of limited for control purposes. Inference algorithms rely either on variables phenomenological or on empirical models. Recently, hybrid models that combine these two approaches have received great attention. In this work, a novel softsensor was developed that was consisted of an Artificial Neural Network and the optimized physical model. A feed-forward neural network (FNN) was coupled with the optimized mass balance equations. This model for the first time was used for on-line estimation of state variables of fed-batch cultivation of E. coli BL21 (DE3) [pET3a-ifnγ] that has most reported production for any recombinant protein. The fairly good results obtained encourage further studies to use this approach in the development of process control algorithms. Keywords: Fed-batch, recombinant E. coli, Hybrid Model, Soft-sensor, Artificial Neural Network
Introduction Nowadays many proteins are produced by genetically modified microorganisms. The bacterium Escherichia coli are the most generic host microorganisms used for the production of recombinant proteins. The successful and economical run of recombinant protein production is quite dependant on achieving the maximum performance of the protein production Fed-batch processes are the most current and appropriate way of increasing the production [2,3]. The successful run of this process is ensured by appropriate control of the feeding rate [1,4]. Consequently most researches reported in this area are devoted to surveying of the feeding control approaches. Control of bioprocesses often is a delicate task due to the scarcity of on-line measurements of the component concentrations (essential substrates, biomass and products of interest) [5]
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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
To overcome this problem, indirect estimation techniques have been studied during the last decade. These estimation algorithms often rely on models. There are tree strategy for modeling white box, black box and gray box. White box modeling has usually imprecise results for biochemical process [10]. And black box great disadvantage is their incapacity for extrapolations [11]. The gray box modeling strategy that is called hybrid modeling can diminish problems of white and black box modeling. It combines a simple first-principles model with an ANN that serves as an estimator of unknown parameters. In this research, the first a hybrid ANN model was developed for the inference of state variables of E. coli BL21 (DE3) [pET3a-ifnγ] fed-batch cultivation that was reported with the highest amount of recombinant protein. The ANN is feed-forward neural network (FNN) that estimates the specific growth rate from selected on-line measurements and initial conditions. The output of network was included into the mass balances that have been optimized by genetic algorithm to estimate biomass, substrate and product concentrations.
Experimental Fed-batch processes have two working stages. The first stage is called batch in which biomass matures by means of the substrate (carbon source) in the medium and no external feed is added to the bioreactor. Then after exhausting the carbon source, which causes sudden increment of the oxygen concentration as well as sudden decrement of specific growth rate, the batch stage is finished and the next stage is commenced which is called fed-batch cultivation and during which glucose feed for nourishing the cells is added to the culture via control strategies. For setting up a model, data of previous runs was used which had the following characteristics: batch culture was initially established by the addition of 100 ml of an overnight-incubated seed culture (OD600 = 0.7–1) to the bioreactor containing 900 ml of M9 modified medium. The pH was maintained at 7 by the addition of 25% (w/v) NH4OH or 3 M H3PO4 solutions. Dissolved Oxygen was controlled at 30 ± 10% (v/v) of air saturation by controlling both the inlet air (which was enriched with pure oxygen) and agitation rate. Foam was controlled by the addition of siliconantifoaming reagent. After depletion of initial glucose in the medium, as indicated by a rapid increase in the dissolved oxygen concentration, feeding strategy was initiated. Feeding rate was increased stepwise based on the exponential feeding strategy with maximum attainable specific growth rate during fed-batch cultivation. By using this method the final cell density reached the value of 145 g/L DCW after 17 h for the cultures without induction. Plasmid stability was maintained higher than 95% and by-product concentrations (acetate and lactate) were under inhibitory levels and main components of medium were kept in permissible ranges. By induction at cell density of 65 g/L DCW (OD600=150), maximum cell density and rhIFN-γ concentration after 17 h were 115 g/L DCW and 42.5 g/L respectively. This is higher than the latest reported values (Choi et al., 2006). Process model The mass balance equations of a fed-batch bioreactor are [5] dC X F C X C X dt V dC S F 1 C X (C S ,in C S ) dt YS / X V dC A F 1 C X C A dt YA / X V dV F dt
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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
In which X is biomass concentration [g/L]; S-glucose concentration [g/L]; F-feed rate [L/h]; Vbioreactor volume [L];; Sin - glucose concentration of feed [g/L]; μ - specific growth rate of biomass [g g-1L-1]; YS/X - yield coefficient for biomass [g/g]; YA/X - yield coefficient for acetate [g/g];
Methodology This work operated a design, with two blocks running in series. The first block estimated the specific growth rate from carbon feed rate (F) and dissolved oxygen tension (DO). The output of network was included into the second block. This block consist mass balances that have been optimized by genetic algorithm to estimate biomass, substrate and product concentrations. Certainly, a more generic topology for our hybrid model could be proposed, that possibility will be explored in future works. Neural Network Multi layer perception (MLP) is a generally used neural-network structure. In the MLP neural network, the neurons are grouped into layers. Typically, an MLP neuron network consists of an input layer, one or more hidden layers, and an output layer, as shown in Fig.1.
Fig. 1. MLP neural-network structure. A popular method of network training is the error back in this work a feed-forward neural network with three layers was employed. The hidden layer had seven neurons. The combined algorithm was used to train the neural network -propagation (BP) algorithm [3].
Database The database consisted of one experimental runs. The assays were carried on in a conventional bioreactor operated in fed-batch modes. The data-sampling interval was 10s. For training purposes, time intervals encompassed 10 minutes. The database used to train the ANN included this run. Experimental off-line data were determined using standard methods and since specific growth rate and product not directly measurable, they must be calculated from these data. This can be done after differentiating cell concentration with respect to time. There are two possible solutions for this problem. One approach relies on a phenomenological model to calculate the "experimental" rates from the empirical concentrations. In this work, this approach was applied. Results and Discussion This page was created using Nitro PDF trial software. To purchase, go to http://www.nitropdf.com/
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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
To validate the model, a set of data was employed that not used during the training phase. Fig.2 depict these results. An excellent fitting to the "experimental" values of specific growth rate can be noted. It can be observed that the FNN results fitted fairly well to the "experimental" specific growth rates. The simulation results derived from the hybrid ANN model are plotted in Figs 3,4,5 together with the experimental results for biomass, glucose and acetate and hIFN-γ. As it can be observed, the accuracy of the model is satisfactory.
Figure 2. the measured and estimated biomass
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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
Figure 3. measured and estimated glucose
Figure 4. measured and estimated acetate Conclusion The hybrid model was successful in capturing the complex dynamics of this system. One very important feature of this approach is that differences in the lag phase duration could be accommodated without effort. This would be an overwhelming task for a phenomenological model: differences in adaptation times are due to innumerous causes, making it almost impossible to predict the beginning of the exponential growth beforehand. This delay must be informed to the "white box" model, as an extra empirical parameter. The algorithm presented in this work by-passed this difficulty.
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The 8 International Chemical Engineering Congress& Exhibition (IChEC 2014)
Kish, Iran, 24-27February, 2014
Figure 5. measured and estimated Interferon REFERENCES [1] Åkesson, M., Probing Control of Glucose Feeding in E. coli Cultivation, PhD thesis, Lund, 1999. [2] Babaeipour, V, Shojaosadati, S. and et al., Over-production of human interferon-γ by HCDC of recombinant E. coli, Process Biochem., 2007, 42: 112-117. [3] Babaeipour, V, Shojaosadati, and et al., A proposed feeding strategy for overproduction of recombinant proteins by E. coli, Biotechnol. Appl. Biochem., Article in Press, 2008. [4] De Mare, L., Hagander, P., Parameter Estimation of a Model Describing the Oxygen Dynamics in Fed-batch E. coli Cultivation, Biotechnol. Lett. 27:14, 2006: 983-990. [5] Renard, F., Vande Wouwer, A., Valentinotti, S., A practical robust control scheme for yeast fed-batch culturesAn experimental validation. J of Process Control, [6] Acuña, G., Latrille, E., Béal, C., and Corrieu, G., Static and Dynamic Neural Models for Estimating Biomass Concentration during Thermophilic Lactic Acid Bacteria Batch Cultures, J. Ferment. Bioeng., 85(6), 615-622 (1998). [7] Araujo, M.L.G.C., Oliveira, R.P., Giordano, R.C., Hokka, C.O., Comparative Studies on Cephalosporin C Production Process with Free and Immobilized Cells of Cephalosporium acremonium ATCC 48272, Chemical Engineering Science, 51(11), 2835-2840 (1996). [8] Bhat, N., McAvoy, T.J., Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems, Computers and Chemical Engineering, 14(4/5), 573-583 (1990). [10] Cruz, A.J.G., Araujo, M.L.G.C., Giordano, R.C., Hokka, C.O., Phenomenological and Neural-Network Modeling of Cephalosporin C Production Bioprocess, Appl. Bioch. Biotechnol., 70-72, 579-592 (1998) [11] Di Massimo, C., Montague, G.A., Willis, M.J., Tham, M.T., Morris, A.J., Towards Improved Penicillin Fermentation via Artificial Neural Network, Computers and Chemical Engineering, 16(4), 283-291 (1992). [12] Pollard, J.F., Broussard, M.R., Garrison, D.B., San, K.Y., Process Identification Using Neural Networks, Computers & Chemical Engin., 16(4):253-270 (1992).
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