• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.400-407
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• 2005

Reverse engineering is defined as the process where the internal structures and dynamics of a given system are inferred and analyzed from external observations and relevant knowledge. The first part of this paper surveys existing techniques for biosystem reverse engineering. Network structure inference techniques such as Correlation Matrix Construction (CMC), Boolean network and Bayesian network-based methods are explained. After the numeric and logical simulation techniques are briefly described, several representative working software tools were introduced. The second part presents our component-based software architecture for biosystem reverse engineering. After three design principles are established, a loosely coupled federation architecture consisting of 11 autonomous components is proposed along with their respective functions.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.3
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• pp.156-165
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• 2004

Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process of Saccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.

• Biotechnology and Bioprocess Engineering:BBE
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• v.6 no.1
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• pp.31-36
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• 2001

Optimal operation in chromatography is needed to save operation time and the solvent used in multiple chromatographic runs. To this end, many simulation studies of chromatography process have been performed. The relationship between the distribution coefficient and the ionic strength is important in gradient elution ion chromatography. Experimental runs and computer simulations were carried out under linear gradient elution condition in order to compare the experiments and the simulation. Experiments were performed with formic acid under isocratic conditions to determine the simulation equation parameters. Computer simulation was based on three equations which related distribution with ionic strength as follows; K=${\alpha}$I(sup)-${\beta}$, K=A+BI+Cl$^2$and K=y(sub)0+A$_1$$.$e(sup)(-I/m$_1$). The effects of gradient slope on the chromatograms are discussed, and good agreement between the experimental and the simulated results is shown.

• KSBB Journal
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• v.16 no.5
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• pp.452-458
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• 2001

Concentrations of ammonia in biological processes were controlled by PID controllers and also neural network based controllers (NN controllers). A flow injection analysis system has been to on-line monitor the concentrations of ammonia in a bioreactor. The effect of the analysis error and the residence time of samples on the control performance were studied. The optimal neural network structure was investigated by using computer simulation and found to be a 3(input layer)-2(hidden layer)-1(output layer). The NN controller is often time consuming, but it has advantage over the PID controller in sensitivity. The 3-2-1 NN controller has been applied to control the ammonia concentrations in a simulated bioprocess and also a real cultivation process of yeast. The good control performance showed that the 3-2-1 NN controller based on the FIA system can be used to control the concentration of substrates in biological processes very well.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.173-176
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• 2000

A neural network based controller (NN controller) was studied for the control of ammonia concentrations in biological processes. An ammonia FIA has been employed to on-line monitor the concentrations of ammonia in a bioreactor. The optimal neural network structure was investigated by computer simulation and found to be a 3(inputlayer)-2(hidden layer)-1(output layer). The NN controller had advantage over the PID controller, even though the former is more time consuming. The 3-2-1 NN controller has been used to control the ammonia concentrations in a simulated bioprocess and also in a real cultivation process of yeast, and its performance were investigated.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.1
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• pp.52-58
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• 2004

An unstructured mathematical model for lactic acid fermentation was developed. This model was able to predict the inhibition effects of lactic acid and glucose and was con-firmed to be valid with various initial concentrations of lactic acid and glucose. Simulation of energy production was made using this mathematical model, and the relationship between the kinetics of energy metabolism and lactic acid production was also analyzed.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.1
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• pp.23-31
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• 2003

A mathematical model was formulated to simulate the long-term performance of an anaerobic bioreactor designed to digest Korean food wastes. The system variables of various decomposition steps were built into the model, which predicts the temporal characters of Solid waste, and volatile fatty acid (VFA) in the reactor, and gas production in response to various input loadings and temperatures. The predicted values of VFA and gas production were found to be in good agreement with experimental observations in batch and repeated-input systems. Finally, long-term reactor performance was simulated with respect to the seasonal temperature changes from 5C in winter to 25C in Summer at different food waste input loadings. The simulation results provided us with information concerning the success or failure of a process during long-term operation .

• KSBB Journal
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• v.10 no.1
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• pp.97-104
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• 1995

A computer simulation of biochemical process was carried out using Macintosh-based BioDesignerTM developed at Bioprocess and Engineering Center(BPEC) of MIT. Based on the assumptions and flask culture experiments, a porcine growth hormone (PGH, Porcine Somatotropin) production process was simulated by a two-stage continuous culture. The economical and sensitivity analyses were evaluated for the scale-up production of PGH. A high return on investment (ROI, 104%/year) suggested that the process be profitable. However, sensitivity analysis indicated that ROI was dependent on the yield of PGH, selling price, dose and the achievement of projected market penetration.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.5
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• pp.362-368
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• 2004

A new one-column chromatography process, analogous to a four-zone simulated moving bed (SMB), was presented. The basic principle of the process was identical to that of a four-zone SMB. The process consisted of one chromatographic column and four tanks, instead of the four columns in the four-zone SMB (1-1-1-1), and has been used for the separation of two amino acids, phenylalanine and tryptophan, using an ion exchange resin. The operating parameters for the one-column process and four-zone SMB were obtained from equilibrium theory. Computer simulations were used to compare the performances of the new one column process to that of the general four-zone SMB, using Aspen $Chromatography^{TM}$ v 11.1. The differences between the one-column and SMB processes in terms of the purities and yields of phenylalanine and tryptophan were less than 4 and about $6\%$, respectively. The lower purities of the one-column process were due to the loss of the developed concentration profiles in the column when the liquid was stored in tanks. The one-column process gave great flexibility, and would be useful for reconstructing an existing conventional chromatography process to one of a SMB.