Literature DB >> 15137083

Fed-batch optimization of alpha-amylase and protease-producing Bacillus subtilis using Markov chain methods.

Wanwisa Skolpap1, J M Scharer, P L Douglas, M Moo-Young.   

Abstract

A stoichiometry-based model for the fed-batch culture of the recombinant bacterium Bacillus subtilis ATCC 6051a, producing extracellular alpha-amylase as a desirable product and proteases as undesirable products, was developed and verified. The model was then used for optimizing the feeding schedule in fed-batch culture. To handle higher-order model equations (14 state variables), an optimization methodology for the dual-enzyme system is proposed by integrating Pontryagin's optimum principle with fermentation measurements. Markov chain Monte Carlo (MCMC) procedures were appropriate for model parameter and decision variable estimation by using a priori parameter distributions reflecting the experimental results. Using a simplified Metropolis-Hastings algorithm, the specific productivity of alpha-amylase was maximized and the optimum path was confirmed by experimentation. The optimization process predicted a further 14% improvement of alpha-amylase productivity that could not be realized because of the onset of sporulation. Among the decision variables, the switching time from batch to fed-batch operation (t(s)) was the most sensitive decision variable. Copyright 2004 Wiley Periodicals, Inc.

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Year:  2004        PMID: 15137083     DOI: 10.1002/bit.20079

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  2 in total

1.  Complete Genome Sequence of Bacillus subtilis Strain ATCC 6051a, a Potential Host for High-Level Secretion of Industrial Enzymes.

Authors:  Haeyoung Jeong; Young Mi Sim; Seung-Hwan Park; Soo-Keun Choi
Journal:  Genome Announc       Date:  2015-05-21

2.  Multigene disruption in undomesticated Bacillus subtilis ATCC 6051a using the CRISPR/Cas9 system.

Authors:  Kang Zhang; Xuguo Duan; Jing Wu
Journal:  Sci Rep       Date:  2016-06-16       Impact factor: 4.379

  2 in total

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