Showing papers by "Dennis Vitkup published in 2013"
TL;DR: Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues, and many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes, which are potential targets for anticancer therapy.
Abstract: Reprogramming of cellular metabolism is an emerging hallmark of neoplastic transformation. However, it is not known how metabolic gene expression in tumors differs from that in normal tissues, or whether different tumor types exhibit similar metabolic changes. Here we compare expression patterns of metabolic genes across 22 diverse types of human tumors. Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues. Although expression changes of some metabolic pathways (e.g., up-regulation of nucleotide biosynthesis and glycolysis) are frequently observed across tumors, expression changes of other pathways (e.g., oxidative phosphorylation and the tricarboxylic acid (TCA) cycle) are very heterogeneous. Our analysis also suggests that the expression changes of major metabolic processes across tumors can be rationalized in terms of several principal components. On the level of individual biochemical reactions, many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes. These isoenzymes are potential targets for anticancer therapy.
343 citations
New England Biolabs1, Boston University2, Emmanuel College (Massachusetts)3, Harvard University4, Wayne State University5, University of California, Berkeley6, University of Virginia7, University of Florida8, Miami University9, Georgia State University10, Discovery Institute11, University of Miami12, Yale University13, Georgia Institute of Technology14, University of Toledo15, Pennsylvania State University16, University of Texas at Arlington17, Spanish National Research Council18, National Institutes of Health19, University of Applied Sciences Western Switzerland20, Swiss Institute of Bioinformatics21, J. Craig Venter Institute22, Columbia University23, SRI International24, University of California, Los Angeles25, University of California, San Diego26, Indiana University27, Wellcome Trust Sanger Institute28, University of Toronto29, University of Alberta30, Tel Aviv University31, Johns Hopkins University32
TL;DR: This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.
Abstract: Experimental data exists for only a vanishingly small fraction of sequenced microbial genes. This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.
149 citations
TL;DR: This work uses Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters for mass‐action models of receptor‐mediated cell death and illustrates how Bayesian approaches to model calibration and discrimination combined with single‐cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.
Abstract: Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., bytreating parameters as a simple list of valuesand variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (B20-fold) for competing ‘direct’ and ‘indirect’ apoptosis models having different numbers of parameters.Our results illustratehowBayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty. Molecular Systems Biology 9: 644; published online 5 February 2013; doi:10.1038/msb.2012.69 Subject Categories: computational methods; differentiation & death
100 citations
01 Apr 2013
TL;DR: In this article, the expression patterns of metabolic genes across 22 diverse types of human tumors were compared and it was shown that the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues.
Abstract: Reprogramming of cellular metabolism is an emerging hallmark of neoplastic transformation. However, it is not known how metabolic gene expression in tumors differs from that in normal tissues, or whether different tumor types exhibit similar metabolic changes. Here we compare expression patterns of metabolic genes across 22 diverse types of human tumors. Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues. Although expression changes of some metabolic pathways (e.g., up-regulation of nucleotide biosynthesis and glycolysis) are frequently observed across tumors, expression changes of other pathways (e.g., oxidative phosphorylation and the tricarboxylic acid (TCA) cycle) are very heterogeneous. Our analysis also suggests that the expression changes of major metabolic processes across tumors can be rationalized in terms of several principal components. On the level of individual biochemical reactions, many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes. These isoenzymes are potential targets for anticancer therapy.
44 citations