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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received October 25, 2007
Accepted January 16, 2008

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fed-batch optimization of recombinant α-amylase production by Bacillus subtilis using a modified Markov chain Monte Carlo technique

Wanwisa Skolpap^† Somboon Nuchprayoon¹ Jeno M. Scharer² Nurak Grisdanurak Peter L. Douglas² Murray Moo-Young²

Department of Chemical Engineering, Thammasat University, Pathumthani, 12120, Thailand ¹Department of Electrical Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand ²Department of Chemical Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada

swanwisa@engr.tu.ac.th

Korean Journal of Chemical Engineering, July 2008, 25(4), 646-655(10), 10.1007/s11814-008-0107-1

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Abstract

A modified Markov Chain Monte Carlo (MCMC) searching procedure was developed to search for an optimal set of decision variables and optimal feed rate trajectories for recombinant α-amylase expression by Bacillus subtilis ATCC 6051a. The bacterium also synthesizes proteases as undesirable products in fed-batch culture that need to be minimized. To maximize α-amylase productivity, a 14th-order fed-batch model was optimized by integrating Pontryagin’s maximum principle with the Luedeking-Piret equation. The number of iterations and simulations of the proposed searching procedure were statistically examined for accuracy and acceptability of the results. It can be concluded that the proposed searching procedure increased the parameter selection opportunity near the tail ends of redefined triangular distribution. By applying a modified MCMC searching procedure with 1,500 iterations, the predicted α-amylase productivity was improved by 18% in comparison with near-optimum experimental results. This productivity was 3.5% higher than predicted by conventional MCMC optimization.

Keywords

Bacillus subtilis Fed-batch Culture Nonlinear Optimization Problem Markov Chain Monte Carlo

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