Abstract: The aim of the present work is to identify a connection network describing the expression profiles of the genes involved in the cellular cycle of the yeast. In order to derive such connections from the microarray data, a mathematical model is needed, whose states represent the expression levels of the genes. However, since the cellular dynamics exhibit a limit cycle behavior [1] (i.e. roughly speaking, a periodic response, which is robust to small perturbations), it is not possible to describe the whole phenomenon by means of only linear models. Therefore, we investigate the suitability of quadratic systems [2], for the description of the genetic network dynamics. A quadratic system is made up of a set of first order differential equations, with linear terms plus quadratic terms (i.e. multiplications between two state variables or between a state and an input variable).

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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.

Abstract: Recent developments in large-scale genomic technologies, such as DNA microarrays and mass spectroscopy have made the analysis of gene networks more feasible. However, it is not obvious how the data acquired through such method can be assembled into unambiguous and predictive models of these networks. In a recent study our group developed an algorithm (Network Identification by multiple regression – NIR) that used a series of steady state RNA expression measurements, following transcriptional perturbations, to construct a model of a 9 gene network that is a part of larger SOS network in E.Coli. Though the NIR method proved highly effective in inferring small microbial gene networks, its practical utility is limited because it requires: (i) prior knowledge of which genes are involved in the network of interest; (ii) the perturbation of all the genes in the network via the construction of appropriate episomal plasmids; (iii) the measurement of gene expressions at steady state (i.e., constant physiological conditions after the perturbation). This experimental setup is unpractical for large networks, it is not easily applied to higher organisms, and, most importantly, it is not applicable if there is no prior knowledge of the genes belonging to the network. Here we are proposing a new algorithm that can infer the network of gene-gene interactions to which a gene of interest belongs and identify its direct targets, using the perturbation of only one of the genes in the network. To this end, we need to measure gene expression profiles at multiple time points following perturbation of only the known gene, or genes, and without the need of the steady-state assumption.

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Abstract: Genome-wide gene expression profiles provide a means to discover the direct mediators of biologically active compounds. We have already shown that it is possible to infer a predictive model of a genetic network by overexpressing each gene of the network and measuring the resulting expression at steady state of all the genes in the network. This approach however requires the perturbation of each gene and the measurement of the perturbation magnitude. In this work we explored the possibility of inferring predictive models of large genetic networks without requiring the knowledge of which genes have been perturbed and by what amount. The network identification algorithm here described allows to infer a model of a genetic network from perturbation experiments for which the perturbed genes are not known. This model can be used to identify the target gene, or genes, of a given drug.

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