Identification of Unstructured model for Subtilin production through Bacillus subtilis using Hybrid Genetic Algorithm

Singh, R and Miriyala, S S and Giri, Lopamudra and Mitra, Kishalay and Kareenhalli, V V (2017) Identification of Unstructured model for Subtilin production through Bacillus subtilis using Hybrid Genetic Algorithm. Process Biochemistry, 60. pp. 1-12. ISSN 1359-5113

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Abstract

Subtilin production is favorable when Bacillus subtilis 168 is subjected to stress condition such as nutrient scarcity. A mathematical model underlying such growth process has immense applicability in determining the optimal operating conditions at industrial scale. We present this work with multiple objectives of a) selection of a substrate for creating the minimal nutrient media for B. subtilis thereby enhancing subtilin production, b) experimental study of the growth along with morphological characteristics of B. subtilis and product profile in nutrient scarcity condition and c) identification of an optimal unstructured model for subtilin production using a computational framework. First, we show that subtilin can be produced while B. subtilis is grown using galactose and B. subtilis undergoes morphological changes and takes filamentous shape. We then constructed a series of plausible models and used a hybrid method combining Genetic Algorithm and gradient based search methodologies, for model selection. The estimated kinetic parameters and the stoichiometric analysis indicate that the B. subtilis growth/death, product profile and respiratory mechanism undergo specific modifications in galactose as an adaptive response. Current study provides an inexpensive platform to produce subtilin and the predictive framework presented here has potential applications for large scale production of subtilin.

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IITH Creators:
IITH CreatorsORCiD
Giri, Lopamudrahttp://orcid.org/0000-0002-2352-7919
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Uncontrolled Keywords: Bacillus subtilis; Subtilin production; Morphogenesis; Modeling and identification; Parameter estimation; Model validation; Hybrid optimizer and Non-dominated Sorting Genetic Algorithms II
Subjects: Others > Biochemistry
Others > Biotechnology
Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 22 Jun 2017 05:02
Last Modified: 07 Nov 2017 04:39
URI: http://raiithold.iith.ac.in/id/eprint/3278
Publisher URL: https://doi.org/10.1016/j.procbio.2017.06.005
OA policy: http://www.sherpa.ac.uk/romeo/issn/1359-5113/
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