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      Chemical combination effects predict connectivity in biological systems

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          Abstract

          Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

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          Most cited references55

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          Transcriptional regulatory networks in Saccharomyces cerevisiae.

          We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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            Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

            The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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              Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.

              We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we compared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively global protein expression in cells and tissues.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                1744-4292
                2007
                27 February 2007
                : 3
                : 80
                Affiliations
                [1 ]CombinatoRx, Incorporated, Cambridge, MA, USA
                [2 ]Bioinformatics and Biomedical Engineering, Boston University, Boston, MA, USA
                [3 ]Department of Biological Sciences and Department of Chemistry, Fairchild Center, Columbia University, New York, NY, USA
                Author notes
                [a ]CombinatoRx, Incorporated, 245 First St, Cambridge, MA 02142, USA. Tel.: +1 617 301 7151; Fax: +1 617 301 7110; jlehar@ 123456combinatorx.com or jlehar@ 123456alum.mit.edu
                Article
                msb4100116
                10.1038/msb4100116
                1828746
                17332758
                23e97e4b-8b12-4155-b260-884c3e05ac1e
                Copyright © 2007, EMBO and Nature Publishing Group
                History
                : 13 July 2006
                : 23 November 2006
                Page count
                Pages: 1
                Categories
                Article

                Quantitative & Systems biology
                metabolic and regulatory networks,combinations and synergy,simulation and data analysis,chemical genetics

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