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Cited by 73Enzyme Production and CharacterizationMicrobial Metabolic Engineering and BioproductionEnzyme Catalysis and Immobilization

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Optimization of cultural conditions using response surface methodology versus artificial neural network and modeling of l-glutaminase production by Bacillus cereus MTCC 1305

Singh, Priyanka, Shera, Shailendra Singh, Banik, Jaba, Banik, Rathindra Mohan·2013·Bioresource Technology
DOI 10.1016/j.biortech.2013.03.086
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Abstract

Response surface methodology and artificial neural network were used to optimize cultural conditions of L-glutaminase production from Bacillus cereus MTCC 1305. ANN model was superior to RSM model with higher value of coefficient of determination (99.97ANN>97.78RSM), predicted distribution coefficient (0.9992ANN>0.896RSM) and lower value of absolute average deviation (1.17%ANN<18.47%RSM). Optimum cultural conditions predicted by ANN were pH (7.5), fermentation time (40 h), temperature (34°C), inoculum size (2%), inoculum age (10 h) and agitation speed (175 rpm) with a maximum predicted production of L-glutaminase 666.97 U/l which was close to experimental production of L-glutaminase 667.23 U/l at simulated optimum cultural condition. The production of L-glutaminase was enhanced by 1.58-fold after optimization of cultural conditions. Simple kinetic models were developed using Logistic equation for cell growth, Luedeking Piret equation for L-glutaminase production and modified Luedeking Piret equation for glucose utilization indicating that L-glutaminase fermentation is non growth associated process.

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Crossref · 10.1016/j.biortech.2013.03.086

Cites

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Citations

Cites

Show all 32 references

Research works printed but not linked: 3

  1. Pal, 1992, Antineoplastic activities of purified bacterial glutaminase on transplanted tumour system, Indian J. Cancer Chemother., 13, 73
  2. Prasanthi, 2008, Optimization of the fermentation media using statistical approach and artificial neural networks for the production of an alkaline protease from Bacillus subtilis, IJNES, 2, 51
  3. Sabu, 2000, Biopotential of microbial glutaminases, Chem. Today, 18, 21

Cited by

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Related

Metadata

Authors
Singh, P., Shera, S. S., Banik, J., & Banik, R. M.
Affiliations
  • Indian Institute of Technology BHU (IN)
  • University of Engineering & Management (IN)
Corresponding author
  • Banik, Jaba
  • Banik, Rathindra Mohan
ORCID
Venue
Bioresource Technology
Publisher
Elsevier BV
Published
2013-06
Volume
137
Pages
261-269
ISSN
0960-8524 (ISSN-L)
DOI
10.1016/j.biortech.2013.03.086
OpenAlex
W2107038117
Cited by
73
References
32
Citations by year
2026: 2 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 5 · 2021: 6 · 2020: 4 · 2019: 6 · 2018: 6 · 2017: 9 · 2016: 9 · 2015: 8 · 2014: 6 · 2013: 1
FWCI
7.00
Type
journal article
Language
en
Open access
closed
Locations
  • Bioresource Technology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Enzyme Production and Characterization
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
Keywords
Bacillus cereus, fermentation optimization, artificial neural networks, response surface methodology, logistic growth model, Luedeking-Piret model, culture medium optimization
MeSH terms
Bacillus cereus, Fermentation, Glutaminase, Kinetics, Models, Biological, Neural Networks, Computer, Biomass, Cell Culture Techniques

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.