Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli-Reference-Cited by-同舟云学术

Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli

Published:2020-03-01 Issue:1 Volume:17 Page:
ISSN:1613-4516
Container-title:Journal of Integrative Bioinformatics
language:en
Short-container-title:

Author:

Lee Mee K.¹,Mohamad Mohd Saberi²³,Choon Yee Wen¹,Mohd Daud Kauthar¹,Nasarudin Nurul Athirah¹,Ismail Mohd Arfian⁴,Ibrahim Zuwairie⁵,Napis Suhaimi⁶,Sinnott Richard O.⁷

Affiliation:

1. Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia , 81310 Skudai Johor , Malaysia

2. Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan , City Campus, Pengkalan Chepa , 16100 Kota Bharu Kelantan , Malaysia

3. Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan , Jeli Campus, Lock Bag 100 , 17600 Jeli Kelantan , Malaysia

4. Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang , 26300 Kuantan, Pahang , Malaysia

5. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang , Pekan, Pahang , Malaysia

6. Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia , 43400 UPM Serdang , Selangor , Malaysia

7. School of Computing and Information Systems, The University of Melbourne , Melbourne , VIC 3052 Australia

Abstract

Abstract The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Link

https://www.degruyter.com/document/doi/10.1515/jib-2019-0073/pdf

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