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- Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example
- Junhyeok Jeon , Hyun Uk Kim
- J. Microbiol. 2020;58(3):227-234. Published online February 27, 2020
- DOI: https://doi.org/10.1007/s12275-020-9516-6
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Abstract
- 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
Discrete Dynamics in Nature and Society.2022;[Epub] CrossRef - Numerical Analysis and Scientific Calculation Considering the Management Mechanism of College Students’ Innovation and Entrepreneurship Education
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
- The Application of Web‐Based Scientific Computing System in Innovation and Entrepreneurship
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