Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms

By Hongwu Ma, An-Ping Zeng. Bioinform., 2003

538 citations recorded by the source corpus.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/2e645deb09836d6a1276959a7f8abd70820e63d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Ernst Dieter Gilles
2. Saket Navlakha
3. Steffen Klamt
4. V. Schachter
5. Pierre-Yves Bourguignon
6. Maxime Durot
7. Hongwu Ma
8. Sudhir Kumar

In Pharmacology

1. Saket Navlakha
2. Hongwu Ma
3. An-Ping Zeng
4. Ruth Nussinov
5. Peter Csermely
6. Huba J. M. Kiss
7. Tamás Korcsmáros
8. Gabor London

Citations in the corpus

• Comparative Genomics in Eukaryotes (2005, background): “, 2000; Wagner, 2001), metabolic networks in which the nodes (metabolites) are connected when they are substrates or products in the same biochemical reaction (Fell and Wagner, 2000; Jeong et al., 2000; Ma and Zeng, 2003), genomic association networks in which the nodes (genes) are connected when th”
• Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Metabolic networks have a hierarchical modular structure (Jeong et al., 2001; Wagner & Fell, 2001; Ravasz et al., 2002; Ma & Zeng, 2003; Ma et al., 2004; Guimera & Amaral, 2005; Zhao et al., 2006).”
• Cortical High-Density Counterstream Architectures (2013, background): “A core-periphery structure has been observed in other self-organized information-processing architectures, both human-made such as theWorld Wide Web (113) and the Internet (114, 115), and in biological networks, such as in metabolism (116, 117), the immune system (118), and cell signaling (119–121).”
• The Edinburgh human metabolic network reconstruction and its functional analysis (2007, methodology): “This metabolite classification is quite similar with the bow-tie structure of metabolic networks discovered previously based on graph analysis (Ma and Zeng, 2003).”
• Genome-scale models of bacterial metabolism: reconstruction and applications (2008, background): “…the literature, reversibility is therefore often determined using simple thermodynamic considerations based on the reaction Gibbs energy, if it is known, or on basic rules depending on the energy equivalents (e.g. NADH or ATP) involved in the reactions (Ma & Zeng, 2003; K ¨ ummel et al ., 2006a).”
• Learning the Structural Vocabulary of a Network (2017, methodology): “To test this effect at the network level, we collected bacterial metabolic networks for each of 67 unique species (Ma & Zeng, 2003), each containing at least 100 nodes.”
• The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks (2003, methodology): “∗To whom correspondence should be addressed.”
• Minimal cut sets in biochemical reaction networks (2004, background): “Studies on the large-scale topology have yielded deeper insights into the global organization of metabolic networks (e.g. Fell and Wagner, 2000; Ma and Zeng, 2003a,b).”
• Using graph theory to analyze biological networks (2011, background): “Metabolic and biochemical networks are scale-free networks indicating a small-world structure considering the topology of the network based on its metabolites [166,167], where all of the nodes in such networks are connected through a short path to any other.”