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Michael Cornell

Bio: Michael Cornell is an academic researcher from University of Manchester. The author has contributed to research in topics: Genome & Notch signaling pathway. The author has an hindex of 12, co-authored 12 publications receiving 4490 citations. Previous affiliations of Michael Cornell include University of Maryland, Baltimore.

Papers
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Journal ArticleDOI
23 May 2002-Nature
TL;DR: Comprehensive protein–protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function and are compared with each other and with a reference set of previously reported protein interactions.
Abstract: Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.

2,432 citations

Journal ArticleDOI
Herman Jan Pel1, Johannes H. de Winde2, Johannes H. de Winde1, David B. Archer3, Paul S. Dyer3, Gerald Hofmann4, Peter J. Schaap5, Geoffrey Turner6, Ronald P. de Vries7, Richard Albang8, Kaj Albermann8, Mikael Rørdam Andersen4, Jannick Dyrløv Bendtsen9, Jacques A.E. Benen5, Marco A. van den Berg1, Stefaan Breestraat1, Mark X. Caddick10, Roland Contreras11, Michael Cornell12, Pedro M. Coutinho13, Etienne Danchin13, Alfons J. M. Debets5, Peter J. T. Dekker1, Piet W.M. van Dijck1, Alard Van Dijk1, Lubbert Dijkhuizen14, Arnold J. M. Driessen14, Christophe d'Enfert15, Steven Geysens11, Coenie Goosen14, Gert S.P. Groot1, Piet W. J. de Groot16, Thomas Guillemette17, Bernard Henrissat13, Marga Herweijer1, Johannes Petrus Theodorus Wilhelmus Van Den Hombergh1, Cees A. M. J. J. van den Hondel18, René T. J. M. van der Heijden19, Rachel M. van der Kaaij14, Frans M. Klis16, Harrie J. Kools5, Christian P. Kubicek, Patricia Ann van Kuyk18, Jürgen Lauber, Xin Lu, Marc J. E. C. van der Maarel, Rogier Meulenberg1, Hildegard Henna Menke1, Martin Mortimer10, Jens Nielsen4, Stephen G. Oliver12, Maurien M.A. Olsthoorn1, K. Pal5, K. Pal20, Noël Nicolaas Maria Elisabeth Van Peij1, Arthur F. J. Ram18, Ursula Rinas, Johannes Andries Roubos1, Cornelis Maria Jacobus Sagt1, Monika Schmoll, Jibin Sun, David W. Ussery4, János Varga20, Wouter Vervecken11, Peter J.J. Van De Vondervoort18, Holger Wedler, Han A. B. Wösten7, An-Ping Zeng, Albert J. J. van Ooyen1, Jaap Visser, Hein Stam1 
TL;DR: The filamentous fungus Aspergillus niger is widely exploited by the fermentation industry for the production of enzymes and organic acids, particularly citric acid, and the sequenced genome revealed a large number of major facilitator superfamily transporters and fungal zinc binuclear cluster transcription factors.
Abstract: The filamentous fungus Aspergillus niger is widely exploited by the fermentation industry for the production of enzymes and organic acids, particularly citric acid. We sequenced the 33.9-megabase genome of A. niger CBS 513.88, the ancestor of currently used enzyme production strains. A high level of synteny was observed with other aspergilli sequenced. Strong function predictions were made for 6,506 of the 14,165 open reading frames identified. A detailed description of the components of the protein secretion pathway was made and striking differences in the hydrolytic enzyme spectra of aspergilli were observed. A reconstructed metabolic network comprising 1,069 unique reactions illustrates the versatile metabolism of A. niger. Noteworthy is the large number of major facilitator superfamily transporters and fungal zinc binuclear cluster transcription factors, and the presence of putative gene clusters for fumonisin and ochratoxin A synthesis.

1,161 citations

Journal ArticleDOI
TL;DR: This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell and has direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eUKaryoticcell.
Abstract: Background: Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. Results: Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rateassociated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently

289 citations

Journal ArticleDOI
TL;DR: This work shows in Drosophila that Notch signaling is limited by the activity of two Nedd4 family HECT domain proteins, Suppressor of deltex [Su(dx)] and DNedd4, and proposes a model in which endocytic sorting of Notch mediates a decision between its activation and downregulation.

198 citations

Journal ArticleDOI
01 Jun 1999-Genetics
TL;DR: Overexpression of Su( dx) results in ectopic vein differentiation, wing margin loss, and wing growth phenotypes and enhances the phenotypes of loss-of-function mutations in Notch, evidence that supports the conclusion that Su(dx) has a role in the downregulation of Notch signaling.
Abstract: During development, the Notch receptor regulates many cell fate decisions by a signaling pathway that has been conserved during evolution. One positive regulator of Notch is Deltex, a cytoplasmic, zinc finger domain protein, which binds to the intracellular domain of Notch. Phenotypes resulting from mutations in deltex resemble loss-of-function Notch phenotypes and are suppressed by the mutation Suppressor of deltex [Su(dx)]. Homozygous Su(dx) mutations result in wing-vein phenotypes and interact genetically with Notch pathway genes. We have previously defined Su(dx) genetically as a negative regulator of Notch signaling. Here we present the molecular identification of the Su(dx) gene product. Su(dx) belongs to a family of E3 ubiquitin ligase proteins containing membrane-targeting C2 domains and WW domains that mediate protein-protein interactions through recognition of proline-rich peptide sequences. We have identified a seven-codon deletion in a Su(dx) mutant allele and we show that expression of Su(dx) cDNA rescues Su(dx) mutant phenotypes. Overexpression of Su(dx) also results in ectopic vein differentiation, wing margin loss, and wing growth phenotypes and enhances the phenotypes of loss-of-function mutations in Notch, evidence that supports the conclusion that Su(dx) has a role in the downregulation of Notch signaling.

151 citations


Cited by
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Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations

Journal ArticleDOI
13 Mar 2003-Nature
TL;DR: The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.
Abstract: Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

6,597 citations

Journal ArticleDOI
TL;DR: A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
Abstract: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE .

4,599 citations

Journal ArticleDOI
16 Oct 2003-Nature
TL;DR: The construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.
Abstract: A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

4,310 citations

Journal ArticleDOI
TL;DR: BioGRID is a freely accessible database of physical and genetic interactions that includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
Abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.

3,794 citations