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Abstract: Introduction Biological mechanisms underlying the regulation of gene expression are not completely understood. It is known that they involve binding of transcription factors to regulatory elements on gene promoters. However, attempts to computationally predict such elements in DNA sequences of gene promoters typically yield an excess of false positives. Computational identification of CREs is currently based mainly on three different approaches: (1) identification of conserved motifs using interspecies sequence global alignments (Pennacchio 2001); (2) identification of conserved motifs in the promoters of coregulated genes (Hughes et al 2000, Sudarsanam et al 2002, Bussemaker et al 2001, Eskin et al 2002, Bailey et al 1994, Fujibuchi et al 2001, Palin et al 2002); (3) computational detection of known experimentally identified motifs in genes’ promoters for which binding factors are unknown (Kel et al 2003). The limitations of the first approach are caused by the high mutation, deletion and insertion rates in gene promoter regions (Ludwig 2002), that prevent a correct alignment of the promoter region. As experimental data is accumulating on known DNA binding elements, increasing amount of information can be used to search for similar elements in genes for which transcription factors are unknown. Our approach involves consensus pattern search of known regulatory elements in 5kb upstream of gene transcription start site against a background word distribution simulated by shuffling symbols in consensus, with the aim of minimizing false positives by using a background model of random matches of experimentally determined consensi, and integrating information from the promoters of ortholog genes.

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Abstract: A critical step in drug development is the optimization of the efficacy and specificity of candidate therapeutic compounds. Ideally, optimization is carried out using knowledge of the drug’s mode of action, i.e., the gene products with which a drug functionally interacts (drug targets). These drug targets may include genes that mediate the therapeutic effects of the drug, as well as genes that mediate undesirable side-effects. However, for many drug candidates the targets are unknown and difficult to identify among the thousands of genes in a typical genome. Previously, we developed an algorithm to identify drug targets in yeast using multiple perturbations to a cell and by measuring the response at steady-state (di Bernardo et al, Nature Biotechnology, in press). Here, we report a novel computational approach for rapidly identifying drug targets using time-course gene expression profiles. The approach filters expression profiles using a reverse-engineered gene-network model to distinguish the targets of compounds from the genes that exhibit only secondary responses. We tested this approach experimentally in E coli and show that it can overcome some of the experimental and computational limitations of existing chemogenetic approach for identifying a drug’s mode of action.

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