• Biotechnology and Bioprocess Engineering:BBE
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• 제7권5호
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• pp.275-280
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• 2002

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.

• Biotechnology and Bioprocess Engineering:BBE
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• 제1권1호
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• pp.46-50
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• 1996

In order to design industrial scale reactors and proceises for multi-phase biocatalytic reactions, it is essential to understand the mechanisms by which such systems operate. To il-lustrate how such mechanisms can be modeled, the hydrolysis of the primary ester groups of triglycerides to produce fatty acids and monoglycerides by lipased (glycerol-ester hydrolase) catalysis has been selected as an example of multiphase biocatalysis. Lipase is specific in its behavior such that it can act only on the hydrolyzed (or emulsified) part of the substrate. This follows because the active center of the enzyme is catalytically active only when the substrate contacts it in its hydrolyzed form. In other words, lipase acts only when it can shuttleback and forth between the emulsion phase and the water phase, presumably within an interphase or boundary layer between these two phases. In industrial applications lipase is employed as a fat splitting enzyme to remove fat stains from fabrics, in making cheese, to flavor milk products, and to degrade fats in waste products. Effective use of lipase in these processes requires a fundamental understanding of its kinetic behavior and interactions with substrates under various environmental conditions. Therefore, this study focuses on modeling and simulating the enzymatic activity of the lipase as a step towards the basic understanding of multi-phase biocatalysis processes.

• Biotechnology and Bioprocess Engineering:BBE
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• 제10권5호
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• Journal of Microbiology and Biotechnology
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• 제22권11호
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• pp.1494-1500
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• 2012

Fermentation with filamentous fungi in a bioreactor is a complex dynamic process that is affected by flow conditions and the evolution of the rheological properties of the medium. These properties are mainly affected by the biomass concentration and the morphology of the fungus. In this work, the rheological properties of a fermentation with the fungus Beauveria bassiana under different hydrodynamic conditions were studied and the rheological behavior of this broth was simulated through a mixture of carboxymethyl cellulose sodium and cellulose fibers (CMCNa-SF). The bioreactor was a 10 L CSTR tank operated at different stir velocities. Rheological results were similar at 100 and 300 rpm for both systems. However, there was a significant increase in the viscosity accompanied by a change in the consistence index, calculated according to the power law model, for both systems at 800 rpm. The systems exhibited shear-thinning behavior at all stir velocities, which was determined with the power law model. The mixing time was observed to increase as the cellulose content in the system increased and, consequently, the efficiency of mixing diminished. These results are thought to be due to the rheological and morphological similarities of the two fungal systems. These results will help in the optimization of scale-up production of these fungi.