TY - JOUR
T1 - Database and tools for metabolic network analysis
AU - Jing, Lu Shi
AU - Shah, Farah Fathiah Muzaffar
AU - Mohamad, Mohd Saberi
AU - Hamran, Nur Laily
AU - Salleh, Abdul Hakim Mohamed
AU - Deris, Safaai
AU - Alashwal, Hany
N1 - Funding Information:
We would like to thank the Malaysian Ministry of Science, Technology and Innovation for supporting this research by an e-science research grant (Grant number: 01-01-06-SF1234). This research is also funded by an Exploratory Research Grant Scheme (Grant number: R.J130000.7807. 4L096) and a Fundamental Research Grant Scheme (Grant number: R.J130000.7807.4F190) from the Malaysian Ministry of Higher Education.
Publisher Copyright:
© 2014 The Korean Society for Biotechnology and Bioengineering and Springer-Verlag Berlin Heidelberg.
PY - 2014/7/1
Y1 - 2014/7/1
N2 - Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.
AB - Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.
KW - Kyoto Encyclopedia of Genes and Genomes (KEGG)
KW - MetaFluxNet
KW - OptFlux
KW - flux balance analysis (FBA)
KW - metabolic network analysis tools
KW - metabolic network reconstruction
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U2 - 10.1007/s12257-014-0172-8
DO - 10.1007/s12257-014-0172-8
M3 - Article
AN - SCOPUS:84907229971
SN - 1226-8372
VL - 19
SP - 568
EP - 585
JO - Biotechnology and Bioprocess Engineering
JF - Biotechnology and Bioprocess Engineering
IS - 4
ER -