- Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example
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Junhyeok Jeon , Hyun Uk Kim
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J. Microbiol. 2020;58(3):227-234. Published online February 27, 2020
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DOI: https://doi.org/10.1007/s12275-020-9516-6
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 Abstract
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Computational analysis of biological data is becoming increasingly
important, especially in this era of big data. Computational
analysis of biological data allows efficiently deriving
biological insights for given data, and sometimes even
counterintuitive ones that may challenge the existing knowledge.
Among experimental researchers without any prior exposure
to computer programming, computational analysis
of biological data has often been considered to be a task reserved
for computational biologists. However, thanks to the
increasing availability of user-friendly computational resources,
experimental researchers can now easily access computational
resources, including a scientific computing environment
and packages necessary for data analysis. In this regard,
we here describe the process of accessing Jupyter Notebook,
the most popular Python coding environment, to conduct
computational biology. Python is currently a mainstream programming
language for biology and biotechnology. In particular,
Anaconda and Google Colaboratory are introduced as
two representative options to easily launch Jupyter Notebook.
Finally, a Python package COBRApy is demonstrated as an
example to simulate 1) specific growth rate of Escherichia coli
as well as compounds consumed or generated under a minimal
medium with glucose as a sole carbon source, and 2)
theoretical production yield of succinic acid, an industrially
important chemical, using E. coli. This protocol should serve
as a guide for further extended computational analyses of biological
data for experimental researchers without computational
background.
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Citations
Citations to this article as recorded by  - The Application of Web‐Based Scientific Computing System in Innovation and Entrepreneurship
Tingli Cheng, Lele Qin
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Sheng Wang, Xiantao Jiang
Mathematical Problems in Engineering.2022; 2022: 1. CrossRef - Omics-based microbiome analysis in microbial ecology: from sequences to information
Jang-Cheon Cho
Journal of Microbiology.2021; 59(3): 229. CrossRef - Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data
Anurag Passi, Juan D. Tibocha-Bonilla, Manish Kumar, Diego Tec-Campos, Karsten Zengler, Cristal Zuniga
Metabolites.2021; 12(1): 14. CrossRef - User guides for biologists to learn computational methods
Dokyun Na
Journal of Microbiology.2020; 58(3): 173. CrossRef
- Isolation and Functional Characterization of a Delta 6-Desaturase Gene from the Pike Eel (Muraenesox cinereus)
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Sun Hee Kim , Kyung Hee Roh , Jung-Bong Kim , Kwang-Soo Kim , Nam Shin Kim , Hyun Uk Kim , Kyeong-Ryeol Lee , Jong-Sug Park , Jong-Bum Kim
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J. Microbiol. 2013;51(6):807-813. Published online October 5, 2013
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DOI: https://doi.org/10.1007/s12275-013-3144-3
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928
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Abstract
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Stearidonic acid (STA; 18:4n-3) and γ-linolenic acid (GLA;
18:3n-6) are significant intermediates in the biosynthetic pathway
for the very-long-chain polyunsaturated fatty acids of
eicosapentaenoic acid (EPA; 20:5n-3) and arachidonic acid
(ARA; 20:4n-6), respectively. To develop a sustainable system
for the production of dietary polyunsaturated fatty acids,
we focused on the action of the enzyme delta 6-desaturase
(D6DES) on the essential acids, linoleic acid (LA; 18:2n-6)
and α-linolenic acid (ALA; 18:3n-3). A 1,335-bp full-length
cDNA encoding D6DES (McD6DES) was cloned from Muraenesox
cinereus using degenerate PCR and RACE-PCR
methods
. To investigate the enzymatic activity of McD6DES
in the production of n-6 and n-3 fatty acids, a recombinant
plasmid expressing McD6DES (pYES-McD6DES) was transformed
into and expressed in Saccharomyces cerevisiae. The
exogenously expressed McD6DES produced GLA and STA
at conversion rates of 14.2% and 45.9%, respectively, from
the exogenous LA and ALA substrates. These results indicate
that McD6DES is essentially a delta 6-desaturase involved
in very-long-chain polyunsaturated fatty acid synthesis.
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Citations
Citations to this article as recorded by - Regulation of long-chain polyunsaturated fatty acid biosynthesis in teleost fish
Dizhi Xie, Cuiying Chen, Yewei Dong, Cuihong You, Shuqi Wang, Óscar Monroig, Douglas R. Tocher, Yuanyou Li
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Crisalejandra Rivera-Pérez, Fausto Valenzuela-Quiñonez, Javier Caraveo-Patiño
Comparative Biochemistry and Physiology Part D: Genomics and Proteomics.2020; 35: 100704. CrossRef - Δ6 fatty acid desaturases in polyunsaturated fatty acid biosynthesis: insights into the evolution, function with substrate specificities and biotechnological use
Jie Cui, Haiqin Chen, Xin Tang, Jianxin Zhao, Hao Zhang, Yong Q. Chen, Wei Chen
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Suvra Roy, Hirak jyoti Chakraborty, Vikash Kumar, B K Behera, R S Rana, Gireesh Babu
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Qinghao Zhang, Marty K. S. Wong, Yiqi Li, Yuanyou Li, Yoshio Takei
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Sun Hee Kim, Kyung Hee Roh, Kyeong-Ryeol Lee, Han-Chul Kang, Hyun Uk Kim, Jong Bum Kim
Plant Biotechnology Reports.2016; 10(6): 475. CrossRef - Heterologous Reconstitution of Omega-3 Polyunsaturated Fatty Acids inArabidopsis
Sun Hee Kim, Kyung Hee Roh, Jong-Sug Park, Kwang-Soo Kim, Hyun Uk Kim, Kyeong-Ryeol Lee, Han-Chul Kang, Jong-Bum Kim
BioMed Research International.2015; 2015: 1. CrossRef - Coexpression of multiple genes reconstitutes two pathways of very long-chain polyunsaturated fatty acid biosynthesis in Pichia pastoris
Sun Hee Kim, Kyung Hee Roh, Kwang-Soo Kim, Hyun Uk Kim, Kyeong-Ryeol Lee, Han-Chul Kang, Jong-Bum Kim
Biotechnology Letters.2014; 36(9): 1843. CrossRef
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