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Imanishi, Toshiaki,Hanai, Taizo,Aoyagi, Ichiro,Uemura, Jun,Araki, Katsuhiro,Yoshimoto, Hiroshi,Harima, Takeshi,Honda , Hiroyuki,Kobayashi, Takeshi The Korean Society for Biotechnology and Bioengine 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
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.
Takeshi Kobayashi,Toshiaki Imanishi,Taizo Hanai,Ichiro Aoyagi,Jun Uemura,Katsuhiro Araki,Hiroshi Yoshimoto,Takeshi Harima,Hiroyuki Honda 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
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.
Yoshinori Iba,Koji Yokoi,Itsuka Eitoku,Masaki Goto,Seiko Koizumi,Fumihito Sugihara,Hiroshi Oyama,Tadashi Yoshimoto 한국식품영양과학회 2016 Journal of medicinal food Vol.19 No.9
The aim of this study was to evaluate the antidiabetic properties of collagen hydrolysates (CHs). CHs exhibited dipeptidyl peptidase-IV inhibitory activity and stimulated glucagon-like-peptide-1 (GLP-1) secretion in vitro. We also determined whether CHs improve glucose tolerance in normal mice. Oral administration of CHs suppressed the glycemic response during the oral and intraperitoneal glucose tolerance tests (OGTT and IPGTT), but the effects were weaker in IPGTT than in OGTT. CHs had no effect on the gastric emptying rate. A pretreatment with the GLP-1 receptor antagonist, exendin 9–39 (Ex9), partially reversed the glucose-lowering effects of CHs, but only when coadministered with glucose. CHs administered 45 min before the glucose load potentiated the glucose-stimulated insulin secretion. This potentiating effect on insulin secretion was not reversed by the pretreatment with Ex9, it appeared to be enhanced. These results suggest that CHs improve glucose tolerance by inhibiting intestinal glucose uptake and enhancing insulin secretion, and also demonstrated that GLP-1 was partially involved in the inhibition of glucose uptake, but not essential for the enhancement of insulin secretion.