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      MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

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      Genome Biology
      BioMed Central

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          Abstract

          A new computational tool is presented that allows the integration of high-throughput experimental results with the probabilistic modeling of previously obtained information about cellular systems. The tool (MetaReg) is demonstrated on the leucine biosynthesis system in S.cerevisiae.

          Abstract

          MetaReg http://acgt.cs.tau.ac.il/metareg/application.html is a computational tool that models cellular networks and integrates experimental results with such models. MetaReg represents established knowledge about a biological system, available today mostly in informal form in the literature, as probabilistic network models with underlying combinatorial regulatory logic. MetaReg enables contrasting predictions with measurements, model improvements and studying what-if scenarios. By summarizing prior knowledge and providing visual and computational aids, it helps the expert explore and understand her system better.

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          Most cited references32

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Factor graphs and the sum-product algorithm

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              Genomic expression programs in the response of yeast cells to environmental changes.

              We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2008
                2 January 2008
                : 9
                : 1
                : R1
                Affiliations
                [1 ]School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
                [2 ]Computational Molecular Biology Department, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, D-14195 Berlin, Germany
                Article
                gb-2008-9-1-r1
                10.1186/gb-2008-9-1-r1
                2395235
                18171474
                695b9867-16cc-47d8-b72f-e850b637e624
                Copyright © 2008 Ulitsky et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 July 2007
                : 28 September 2007
                : 2 January 2008
                Categories
                Software

                Genetics
                Genetics

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