Interaction network containing conserved and essential protein complexes in Escherichia coli

Butland, Gareth; Peregrín-Alvarez, José M.; Li, Joyce; Yang, Wehong; Yang, Xiaochun; Canadien, Veronica; Starostine, Andrei; Richards, Dawn P.; Beattie, Bryan K.; Krogan, Nevan J.; Davey, Michael; Parkinson, John; Greenblatt, Jack; Emili, Andrew

doi:10.1038/nature03239

Cited by 1,091 publications

(1,112 citation statements)

References 29 publications

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“…Over the past decade, one goal of systems biology is to understand how different molecules work together to maintain cellular functionalities [1,2]. It is now a common belief that many complex diseases including cancer are due to systems impairments caused by not only single genetic mutations but also disruption of molecular interactions under different situations, which have been conjectured to be the probable sources of disease heterogeneity as well as treatment response heterogeneity [3-5].…”

Section: Introductionmentioning

confidence: 99%

“…It is now a common belief that many complex diseases including cancer are due to systems impairments caused by not only single genetic mutations but also disruption of molecular interactions under different situations, which have been conjectured to be the probable sources of disease heterogeneity as well as treatment response heterogeneity [3-5]. Hence, by analyzing large-scale gene expression profiles and protein-protein interaction (PPI) data, computational methods may help us to have a better understanding of biological pathways and cellular organization and thereafter their relationships to diseases as well as potential drug responses [1,2]. One way to investigate these large-scale data is to analyze them in the framework of network analysis [2].…”

Section: Introductionmentioning

confidence: 99%

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Joint clustering of protein interaction networks through Markov random walk

Wang

Qian

2014

BMC Systems Biology

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Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint clustering of protein interaction networks through Markov random walk

Wang

Qian

2014

BMC Systems Biology

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“…For membrane proteins, twodimensional blue native/SDS gel electrophoresis (2D BN/SDS-PAGE) [6] has been the method of choice for intact proteins and protein complexes. Similarly, PAGE is an important step for further separating polypeptides after "affinity tagged" purification [7][8][9]. However, a major limitation of all these forms of PAGE, compared with many liquid chromatography techniques, is that they are not readily interfaced with mass spectrometry because it is inconvenient to extract separated protein bands from the gel for further processing.…”

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confidence: 99%

“…To harvest separated proteins, one must either physically cut them out as gel slices or elute them from the bottom of gels. Large scale "gel-cutting" has been used effectively in proteomics and protein interaction projects [7][8][9][10][11], but it is generally labor intensive. Gel-cutting robots-for instance, the "Ettan Spot Picker" from GE Healthcare's Life Sciences (Piscataway, NJ)-exist, but their accessibility is limited.…”

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confidence: 99%

“…Like strategies used previously to identify protein complexes, some of the high throughput pipelines we are developing as part of PCAP use SDS or native PAGE for protein separation and purification, followed by protein identification by mass spectrometry [7][8][9]12]. To meet the GTL program's needs, it will be necessary to develop methods that can separate and elute thousands of protein samples by gel electrophoresis as rapidly as possible.…”

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confidence: 99%

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A Multichannel Gel Electrophoresis and Continuous Fraction Collection Apparatus for High-Throughput Protein Separation and Characterization

Dong

Choi

Biggin

et al. 2012

Methods in Molecular Biology

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To facilitate a direct interface between protein separation by polyacrylamide gel electrophoresis (PAGE) and protein identification by mass spectrometry, we developed a multi-channel system that continuously collects fractions as protein bands migrate off the bottom of gel electrophoresis columns. The device was constructed using several short linear gel columns, each of a different percent acrylamide, to achieve a separation power similar to that of a long gradient gel. A "Counter Free-Flow" elution technique then allows continuous and simultaneous fraction collection from multiple channels at low cost. We demonstrate that rapid, high resolution separation of a complex protein mixture can be achieved on this system using SDS-PAGE. In a 2.5-hour electrophoresis run, for example, each sample was separated and eluted into 48 to 96 fractions over a mass range of ~10 to 150 kD; sample recovery rates were 50% or higher; each channel was loaded with up to 0.3 mg of protein in 0.4 ml; and a purified band was eluted in 2-3 fractions (200 µl/fraction). Similar results were obtained when running native gel electrophoresis, but protein aggregation limited the loading capacity to about 50 µg per channel and reduced resolution.

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Topology of protein interaction networks and cell physiology

Marsolier‐Kergoat

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Interaction network containing conserved and essential protein complexes in Escherichia coli

Cited by 1,091 publications

References 29 publications

Joint clustering of protein interaction networks through Markov random walk

Joint clustering of protein interaction networks through Markov random walk

A Multichannel Gel Electrophoresis and Continuous Fraction Collection Apparatus for High-Throughput Protein Separation and Characterization

Topology of protein interaction networks and cell physiology

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