A novel reverse micellar purification strategy for histidine tagged human interferon gamma (hIFN-γ) protein from Pichia pastoris

Prabhu, Ashish A.; Purkayastha, Anwesha; Mandal, Bapi; Kumar, Jadi Praveen; Mandal, Biman B.; Veeranki, Venkata Dasu

doi:10.1016/j.ijbiomac.2017.10.130

Cited by 24 publications

(15 citation statements)

References 45 publications

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“…This result was somehow expected as it was reported that for small datasets, Bayesian regularization provides better generalization performance as it does not need separate validation dataset, and it uses all data . To the best of authors’ knowledge, only one study implemented Bayesian regularization training algorithm in bioprocess systems , and many previous studies mainly used Levenberg–Marquardt function . During training and testing of these neural networks using Matlab toolbox, the feed‐forward ANN having five nodes in the hidden layer has shown the best performance in terms of accuracy and precision (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, online software sensors have gained significant attention by the introduction of quality‐by‐design in pharmaceutical process design with its integration with process analytical tools and feedback control approaches . Software sensor such as artificial neural network (ANN) is a monitoring system that finds a nonlinear correlation between the critical variables that are challenging to be measured with accessible variables . ANN is a biologically inspired network that is based on a machine learning method and composed of highly interconnected process elements called neurons .…”

Section: Introductionmentioning

confidence: 99%

“…Software sensor such as artificial neural network (ANN) is a monitoring system that finds a nonlinear correlation between the critical variables that are challenging to be measured with accessible variables . ANN is a biologically inspired network that is based on a machine learning method and composed of highly interconnected process elements called neurons . This computational model does not need prior knowledge about the process, and instead, it learns from mismatches and mistakes when tries to find a pattern between the given inputs and outputs .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

Hosseini

Javidanbardan

Khatami

2019

Biotech and App Biochem

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In the current work, the attempt was made to apply best-fitted artificial neural network (ANN) architecture and the respective training process for predicting final titer of hepatitis B surface antigen (HBsAg), produced intracellularly by recombinant Pichia pastoris Mut + in the commercial scale. For this purpose, in large-scale fed-batch fermentation, using methanol for HBsAg induction and cell growth, three parameters of average specific growth rate, biomass yield, and dry biomass concentration-in the definite integral form with respect to fermentation time-were selected as input vectors; the final concentration of HBsAg was selected for the ANN output. Used dataset consists of 38 runs from previous batches; feed-forward ANN 3:5:1 with training algorithm of backpropagation based on a Bayesian regularization was trained and tested with a high degree of accuracy. Implementing the verified ANN for predicting the HBsAg titer of the five new fermentation runs, excluded from the dataset, in the full-scale production, the coefficient of regression and root-mean-square error were found to be 0.969299 and 2.716774, respectively. These results suggest that this verified soft sensor could be an excellent alternative for the current relatively expensive and time-intensive analytical techniques such as enzyme-linked immunosorbent assay in the biopharmaceutical industry.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

Hosseini

Javidanbardan

Khatami

2019

Biotech and App Biochem

View full text Add to dashboard Cite

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“…According to the ANOVA, the regression model for the xylitol production showed high significance with the F value of 22.41. The model with high Fisher's F value clearly explains most of the variations present in the experimental 6 design [16]. Further the F-value for lack of fit was 0.92 and implies that the response (xylitol) is not significant relative to the pure error.…”

Section: Central Composite Design (Ccd)mentioning

confidence: 88%

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

Prabhu

Thomas

Amaro

et al. 2020

Preprint

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Background: Xylitol is a commercially important chemical with multiple applications in food and pharmaceutical industries. According to the US Department of Energy, xylitol is among the twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however expensive and energy intensive. In contrast, the biological route involving microbial cell factories offers a potential cost-effective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts which can be sourced from renewable carbon coming from a variety of cheap feedstocks classified as wastes. Result: In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica an oleaginous yeast grown on glycerol/glucose screening of primary carbon source, media optimisation in shake flask, scale up in bioreactor and downstream processing of xylitol were carried out. With the two step medium optimization involving central composite design and artificial neural network coupled with genetic algorithm, the yeast amassed 53.2 g/L of xylitol using glycerol and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when pure glycerol was substituted with crude glycerol from biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from lignocellulosic hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from fermented broth using pure glycerol/xylose and crude glycerol /xylose with a recovery of 35 and 39.45%, respectively. Conclusion: To the best of the author’s knowledge, this is the first study demonstrating the potential of Y. lipolytica as microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competitive with other xylitol producing organisms.

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“…The error term, which indicates that the amount of variation in the response data, is very low. According to the ANOVA, the regression model for the xylitol production showed high signi cance with a Fisher's F value of 22.41 and explains most of the variations present in the experimental design [21]. The "p" value of 0.92 for lack of t indicated that the response for xylitol concentration was not signi cant relative to the pure error.…”

Section: Central Composite Design (Ccd)mentioning

confidence: 99%

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

Prabhu

Thomas

Amaro

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Xylitol is a commercially important chemical with multiple applications in the food and pharmaceutical industries. According to the US Department of Energy, xylitol is one of the top twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however expensive and energy intensive. In contrast, the biological route using microbial cell factories offers a potential cost-effective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts and biomass which can be sourced from renewable carbon originating from a variety of cheap waste feedstocks. Result: In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica an oleaginous yeast which was firstly grown on a glycerol/glucose for screening of co-substrate, followed by media optimisation in shake flask, scale up in bioreactor and downstream processing of xylitol. A two-step medium optimization was employed using central composite design and artificial neural network coupled with genetic algorithm. The yeast amassed a concentration of 53.2 g/L xylitol using pure glycerol (PG) and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when PG was substituted with crude glycerol (CG) from the biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from sugarcane bagasse hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from PG/xylose and CG /xylose fermentation broths with a recovery of 39.5 and 35.3%, respectively. Conclusion: To the best of the author’s knowledge, this study demonstrates for the first time the potential of using Y. lipolytica as a microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competitive with other xylitol producing organisms.

show abstract

A novel reverse micellar purification strategy for histidine tagged human interferon gamma (hIFN-γ) protein from Pichia pastoris

Cited by 24 publications

References 45 publications

Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

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