A hypergraph model for the yeast protein complex network

Ramadan, Emad; Tarafdar, Arijit; Pothen, Alex

doi:10.1109/ipdps.2004.1303205

Cited by 47 publications

(39 citation statements)

References 8 publications

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“…For instance, it has been found that the proteomic network of yeast forms a hypergraph with the proteins corresponding to vertices and the protein-complexes corresponding to hyperedges [13]. Other large metabolic networks like the one of E. coli have also been found to possess a small-world structure and as most of the reactions of metabolism are multimolecular they can be represented by hypergraphs [14].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics of spin systems on small-world hypergraphs

2006

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We study the thermodynamic properties of spin systems on small-world hypergraphs, obtained by superimposing sparse Poisson random graphs with p-spin interactions onto a one-dimensional Ising chain with nearest-neighbor interactions. We use replica-symmetric transfer-matrix techniques to derive a set of fixed-point equations describing the relevant order parameters and free energy, and solve them employing population dynamics. In the special case where the number of connections per site is of the order of the system size we are able to solve the model analytically. In the more general case where the number of connections is finite we determine the static and dynamic ferromagneticparamagnetic transitions using population dynamics. The results are tested against Monte-Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Thermodynamics of spin systems on small-world hypergraphs

2006

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“…Other HCPIN proteins that are not included in the KEGG pathways but interact with these pathway proteins are called interaction proteins. The representation of protein complexes using a binary protein-protein interaction graph remains a challenge because without detailed structural studies it is often not possible to distinguish direct physical interactions from interactions mediated through the complex (49,50). We used triangular pseudonodes, which link proteins involved in the same complex, to represent multiprotein complexes (50).…”

Section: Human Cancer Pathway Protein Interaction Networkmentioning

confidence: 99%

Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics

Huang

Hang

et al. 2008

Molecular & Cellular Proteomics

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Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10 ؊6 ), only ϳ20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10 ؊6 and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structurefunction database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

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“…We have extended the concept of a k-core to a hypergraph in earlier work [19]. k-cores have been used earlier for clustering proteomic networks as a way of identifying highly connected subnetworks and for removing proteins belonging to low shell values [4].…”

Section: K-cores and K-shells In Graphsmentioning

confidence: 99%

The Architecture of a Proteomic Network in the Yeast

Ramadan

Osgood

Pothen

2005

Lecture Notes in Computer Science

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Abstract.We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.

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A hypergraph model for the yeast protein complex network

Cited by 47 publications

References 8 publications

Thermodynamics of spin systems on small-world hypergraphs

Thermodynamics of spin systems on small-world hypergraphs

Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics

The Architecture of a Proteomic Network in the Yeast

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