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      Microbial interactions for enhancement of α-amylase production by Bacillus amyloliquefaciens 04BBA15 and Lactobacillus fermentum 04BBA19

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          Abstract

          Interactions occurring between Saccharomyces cerevisiae and two thermostable α-amylase producing strains ( Bacillus amyloliquefaciens 04BBA15 and Lactobacillus fermentum 04BBA19) were analyzed by comparing their growth patterns obtained in isolation with those obtained in mixture. The difference between the patterns was assessed using analysis of variance (ANOVA) in order to measure how much the growth of an organism was affected by other. The results showed two types of interactions in mixed culture; commensalism between S. cerevisiae and B. amyloliquefaciens 04BBA15 and mutualism between S. cerevisiae and L. fermentum 04BBA19. In mixed culture, the α-amylase production increased significantly compared to that observed in monoculture ( P < 0.05). Response surface optimization of fermentation parameters in mixed cultures (initial yeast to bacteria ratio 1.125, temperature 33.5 °C, pH 5.5) resulted in about 1.8 fold higher enzyme production than that observed in the unoptimized fermentation.

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          A dynamic approach to predicting bacterial growth in food.

          J. Baranyi, T A Roberts (1994)
          A new member of the family of growth models described by Baranyi et al. (1993a) is introduced in which the physiological state of the cells is represented by a single variable. The duration of lag is determined by the value of that variable at inoculation and by the post-inoculation environment. When the subculturing procedure is standardized, as occurs in laboratory experiments leading to models, the physiological state of the inoculum is relatively constant and independent of subsequent growth conditions. It is shown that, with cells with the same pre-inoculation history, the product of the lag parameter and the maximum specific growth rate is a simple transformation of the initial physiological state. An important consequence is that it is sufficient to estimate this constant product and to determine how the environmental factors define the specific growth rate without modelling the environment dependence of the lag separately. Assuming that the specific growth rate follows the environmental changes instantaneously, the new model can also describe the bacterial growth in an environment where the factors, such as temperature, pH and aw, change with time.
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            Some New Three Level Designs for the Study of Quantitative Variables

            G. BOX, D. Behnken (1960)
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              Response surface methodology for the optimization of alpha amylase production by Bacillus amyloliquefaciens.

              Dhanya Gangadharan, Swetha Sivaramakrishnan, K Nampoothiri … (2008)
              The aim of this work was to optimize the cultural and production parameters through the statistical approach for the synthesis of alpha amylase by Bacillus amyloliquefaciens in submerged fermentation (SmF) using a combination of wheat bran and groundnut oil cake (1:1) as the substrate. The process parameters influencing the enzyme production were identified using Plackett-Burman design. Among the various variables screened, the substrate concentration, incubation period and CaCl2 concentration were most significant. The optimum levels of these significant parameters were determined employing the response surface Box-Behnken design, which revealed these as follows: substrate concentration (12.5%), incubation period (42 h) and CaCl2 (0.0275 M).
              Article 0 comments Cited 40 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Bertrand Tatsinkou Fossi
                Journal
                Journal ID (nlm-ta): Biotechnol Rep (Amst)
                Journal ID (iso-abbrev): Biotechnol Rep (Amst)
                Title: Biotechnology Reports
                Publisher: Elsevier
                ISSN (Electronic): 2215-017X
                Publication date PMC-release: 18 September 2014
                Publication date Collection: December 2014
                Publication date (Electronic): 18 September 2014
                Volume: 4
                Pages: 99-106
                Affiliations
                [a ]Department of Microbiology and Parasitology, Faculty of Science, University of Buea, P.O. Box 63, Buea, Cameroon
                [b ]Department of Biochemistry, Faculty of Science, University of Douala, Cameroon
                [c ]Department of Agronomic and Applied Molecular Sciences, University of Buea, Cameroon
                [d ]Department of Food Science and Nutrition, National School of Agro-Industrial Sciences, University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon
                Author notes
                [* ]Corresponding author. Tel.: +237 99386079 tatsinkoufossi@ 123456yahoo.fr tatsinkou.fossi@ 123456ubuea.cm
                Article
                Publisher ID: S2215-017X(14)00038-1
                DOI: 10.1016/j.btre.2014.09.004
                PMC ID: 5466130
                PubMed ID: 28626668
                SO-VID: e76658f4-49a9-42f6-b88b-d2c1cb5a83d7
                Copyright © © 2014 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                Date received : 8 July 2014
                Date revision received : 10 September 2014
                Date accepted : 11 September 2014
                Categories
                Subject: Article

                Keywords: microbial interactions,thermostable amylases,statistical optimization
                Data availability:
                Keywords: microbial interactions, thermostable amylases, statistical optimization

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