SynBioEcoli: a comprehensive metabolism network of engineered E. coli in three dimensional visualization

Weizhong Tu , Shaozhen Ding , Ling Wu , Zhe Deng , Hui Zhu , Xiaotong Xu , Chen Lin , Chaonan Ye , Minlu Han , Mengna Zhao , Juan Liu , Zixin Deng , Junni Chen , Dong-Qing Wei , Qian-Nan Hu

Quant. Biol. ›› 2017, Vol. 5 ›› Issue (1) : 99 -104.

PDF (2018KB)
Quant. Biol. ›› 2017, Vol. 5 ›› Issue (1) : 99 -104. DOI: 10.1007/s40484-017-0098-1
RESEARCH ARTICLE
RESEARCH ARTICLE

SynBioEcoli: a comprehensive metabolism network of engineered E. coli in three dimensional visualization

Author information +
History +
PDF (2018KB)

Abstract

Background: A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. Many tools focus on two-dimensional space to display pathways in metabolic network. However, the usage of three-dimensional visualization may help to understand better the intricate topology of metabolic and regulatory networks.

Methods: We manually curated large amount of experimental data (including pathways, reactions and metabolites) from literature related with different types of engineered E. coli and then utilized a novel technology of three dimensional visualization to develop a comprehensive metabolic network named SynBioEcoli.

Results: SynBioEoli contains 740 biosynthetic pathways, 3,889 metabolic reactions, 2,255 chemical compoundsmanually curated from about 11,000 metabolism publications related with different types of engineered E. coli. Furthermore, SynBioEcoli integrates with various informatics techniques.

Conclusions: SynBioEcoli could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology. It could be accessed via web browsers (such as Google Chrome) supporting WebGL, at http://www.rxnfinder.org/synbioecoli/.

Graphical abstract

Keywords

engineered E. coli / three dimensional metabolic network / biosynthetic ability

Cite this article

Download citation ▾
Weizhong Tu, Shaozhen Ding, Ling Wu, Zhe Deng, Hui Zhu, Xiaotong Xu, Chen Lin, Chaonan Ye, Minlu Han, Mengna Zhao, Juan Liu, Zixin Deng, Junni Chen, Dong-Qing Wei, Qian-Nan Hu. SynBioEcoli: a comprehensive metabolism network of engineered E. coli in three dimensional visualization. Quant. Biol., 2017, 5(1): 99-104 DOI:10.1007/s40484-017-0098-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

McCloskey, D., Palsson, B. O. and Feist, A. M. (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol. Syst. Biol., 9, 661–676

[2]

Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Hatzimanikatis, V. and Palsson, B. O. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol., 3, 121–139

[3]

Feist, A. M., Herrgard, M. J., Thiele, I., Reed, J. L. and Palsson, B. O. (2009) Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol., 7, 129–143

[4]

Feist, A. M. and Palsson, B. O. (2008) The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat. Biotechnol., 26, 659–667

[5]

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. and Barabási, A. L. (2000) The large-scale organization of metabolic networks. Nature, 407, 651–654

[6]

Ma, H. and Zeng, A. P. (2003) Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics, 19, 270–277

[7]

Okuda, S., Yamada, T., Hamajima, M., Itoh, M., Katayama, T., Bork, P., Goto, S. and Kanehisa, M. (2008) KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res., 36, W423–W426

[8]

Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., (2010) Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief. Bioinform., 11, 40–79

[9]

Csermely, P., Korcsmaros, T., Kiss, H. J., London, G. and Nussinov, R. (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery. Pharmacol. Ther., 138, 333–408

[10]

Rojdestvenski, I. and Cottam, M. (2002) Visualizing metabolic networks in VRML. In Proceedings. Sixth International Conference on Information Visualisation, pp. 175–180

[11]

Pavlopoulos, G. A., O’Donoghue, S. I., Satagopam, V. P., Soldatos, T. G., Pafilis, E. and Schneider, R. (2008) Arena3D: visualization of biological networks in 3D. BMC Syst. Biol., 2, 104–111

[12]

Jia, M., Choi, S. Y., Reiners, D., Wurtele, E. S. and Dickerson, J. A. (2010) MetNetGE: interactive views of biological networks and ontologies. BMC Bioinformatics, 11, 469–485

[13]

Karp, P. D. and Riley, M. (1996) EcoCyc: an encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res., 24, 32–39

[14]

Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res., 42, D199–D205

[15]

Chang, A., Schomburg, I., Placzek, S., Jeske, L., Ulbrich, M., Xiao, M., Sensen, C. W. and Schomburg, D. (2015) BRENDA in 2015: exciting developments in its 25th year of existence. Nucleic Acids Res., 43, D439–D446

[16]

Lang, M., Stelzer, M. and Schomburg, D. (2011) BKM-react, an integrated biochemical reaction database. BMC Biochem., 12, 42

[17]

Fruchterman, T. M. J. and Reingold, E. M. (1991) Graph drawing by force-directed placement. Softw. Pract. Exper., 21, 1129–1164

[18]

Hu, Q. N., Deng, Z., Tu, W., Yang, X., Meng, Z. B., Deng, Z. X. and Liu, J. (2014) VNP: interactive visual network pharmacology of diseases, targets, and drugs. CPT Pharmacometrics Syst. Pharmacol., 3, e105

[19]

Tu, W., Zhang, H., Liu, J. and Hu, Q. -N. (2015) BioSynther: a customized biosynthetic potential explorer. Bioinformatics, 32, 472–473

[20]

Hu, Q. N., Deng, Z., Hu, H., Cao, D. S. and Liang, Y. Z. (2011) RxnFinder: biochemical reaction search engines using molecular structures, molecular fragments and reaction similarity. Bioinformatics, 27, 2465–2467

[21]

Smith, T. F. and Waterman, M. S. (1981) Identification of common molecular subsequences. J. Mol. Biol., 147, 195–197

[22]

Lipman, D. J. and Pearson, W. R. (1985) Rapid and sensitive protein similarity searches. Science, 227, 1435–1441.

[23]

Pearson, W. R. and Lipman, D. J. (1988) Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA, 85, 2444–2448

[24]

Danchilla, B. (2012) Three.js Framework. In Beginning WebGL for HTML5, 173–203. Berkeley: Apress

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg

AI Summary AI Mindmap
PDF (2018KB)

1556

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/