http://smartech.gatech.edu/handle/1853/12167 Papers, Pre/Post-Prints, and Presentations by Faculty in the Department of Biomedical Engineering Search the Channel search http://smartech.gatech.edu/simple-search http://smartech.gatech.edu/handle/1853/13197 Title: A multivariate prediction model for microarray cross-hybridization <br/> <br/>Authors: Chen, Yian A.; Chou, Cheng-Chung; Lu, Xinghua; Slate, Elizabeth H.; Peck, Konan; Xu, Wenying; Voit, Eberhard O.; Almeida, Jonas S. <br/> <br/>Abstract: Background: Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. Results: We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used. Conclusion: A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments. <br/> <br/>Description: © 2006 Chen et al; licensee BioMed Central Ltd.; The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/7/101 http://smartech.gatech.edu/handle/1853/12288 Title: Yeast sphingolipid metabolism: clues and connections <br/> <br/>Authors: Sims, Kellie J.; Spassieva, Stefka D.; Voit, Eberhard O.; Obeid, Lina M. <br/> <br/>Abstract: This review of sphingolipid metabolism in the budding yeast Saccharomyces cerevisiae contains information on the enzymes and the genes that encode them, as well as connections to other metabolic pathways. Particular attention is given to yeast homologs, domains, and motifs in the sequence, cellular localization of enzymes, and possible protein–protein interactions. Also included are genetic interactions of special interest that provide clues to the cellular biological roles of particular sphingolipid metabolic pathways and specific sphingolipids. <br/> <br/>Description: ©2004 NRC Canada http://bcb.nrc.ca http://smartech.gatech.edu/handle/1853/12282 Title: The intricate side of systems biology <br/> <br/>Authors: Voit, Eberhard O.; Neves, Ana Rute; Santos, Helena <br/> <br/>Abstract: The combination of high-throughput methods of molecular biology with advanced mathematical and computational techniques has propelled the emergent field of systems biology into a position of prominence. Unthinkable a decade ago, it has become possible to screen and analyze the expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of a cell population. Although very important, the focus on comprehensive networks of biological components is only one side of systems biology. Complementing large-scale assessments, and sometimes at the risk of being forgotten, are more subtle analyses that rationalize the design and functioning of biological modules in exquisite detail. This intricate side of systems biology aims at identifying the specific roles of processes and signals in smaller, fully regulated systems by computing what would happen if these signals were lacking or organized in a different fashion. We exemplify this type of approach with a detailed analysis of the regulation of glucose utilization in Lactococcus lactis. This organism is exposed to alternating periods of glucose availability and starvation. During starvation, it accumulates an intermediate of glycolysis, which allows it to take up glucose immediately upon availability. This notable accumulation poses a nontrivial control task that is solved with an unusual, yet ingeniously designed and timed feedforward activation system. The elucidation of this control system required highprecision, dynamic in vivo metabolite data, combined with methods of nonlinear systems analysis, and may serve as a paradigm for multidisciplinary approaches to fine-scaled systems biology. <br/> <br/>Description: ©2006 by the National Academy of Sciences http://smartech.gatech.edu/handle/1853/12279 Title: Challenges for the identification of metabolic pathways from time series data <br/> <br/>Authors: Voit, Eberhard O.; Marino, Simeone; Lall, Raman <br/> <br/>Description: ©2004, Bioinformation Systems e.V.