Relationship between gene expression and observed intensities in DNA microarrays--a modeling study

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.     

PanGEA: Identification of allele specific gene expression using the 454 technology

Background  

Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression.     

Cutting-edge technology. I. Global gene expression profiling using DNA microarrays

Having the complete human genomic sequence poses a new challenge: to use genomic structural information to display and analyze biological processes on a genome-wide scale to assign gene function. DNA microarrays are a miniaturized, ordered arrangement of nucleic acid fragments from individual genes located at defined positions on a solid support, enabling the analysis of thousands of genes in parallel by specific hybridization. This review describes technical aspects, discusses relevant applications, and suggests factors affecting the use of this technology and how it fits in the grand scheme of meeting the needs of the postgenomic era.     

Profiling gene expression using onto-express

Gene expression profiles obtained through microarray or data mining analyses often exist as vast data strings. To interpret the biology of these genetic profiles, investigators must analyze this data in the context of other information such as the biological, biochemical, or molecular function of the translated proteins. This is particularly challenging for a human analyst because large quantities of less than relevant data often bury such information. To address this need we implemented an automated routine, called Onto-Express (http://vortex.cs.wayne.edu:8080), to systematically translate genetic fingerprints into functional profiles. Using strings of accession or cluster identification numbers, Onto-Express searches the public databases and returns tables that correlate expression profiles with the cytogenetic locations, biochemical and molecular functions, biological processes, cellular components, and cellular roles of the translated proteins. The profiles created by Onto-Express fundamentally increase the value of gene expression analyses by facilitating the translation of quantitative value sets to records that contain biological implications.     

Identifying time-lagged gene clusters using gene expression data

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.     

Clustering gene expression data using graph separators

Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.     

Inducible product gene expression technology tailored to bioprocess engineering

Bioprocess engineering has developed as a discipline to design optimal culture conditions and bioreactor operation protocols for production cell lines engineered for constitutive expression of desired protein pharmaceuticals. With the advent of heterologous gene regulation systems it has become possible to fine-tune expression of difficult-to-produce protein pharmaceuticals to optimal levels and to conditionally engineer cell metabolism for the best production performance. However, most of the small-molecules used to trigger expression of product or metabolic engineering product genes are incompatible with downstream processing regulations or process economics. Recent progress in product gene control design has resulted in the development of bioprocess-compatible regulation systems, which are responsive to physical parameters such as temperature or physiologic trigger molecules that are either an inherent part of host cell metabolism or intrinsic components of licensed protein-free cell culture media, such as redox status, vitamin H and gaseous acetaldehyde. While all of these systems have been shown to fine-tune product gene expression independent of the host cell metabolism some of them can be plugged into metabolic networks to capture critical physiologic parameters and convert them into an optimal production response. Assembly of individual product gene control modalities into synthetic networks has recently enabled construction of autonomously regulated time-delay or cell density-sensitive gene circuits, which trigger population-wide induction of product gene expression at a predefined time or culture density. We provide a comprehensive overview on the latest developments in the design of bioprocess-compatible product gene control systems.     

Combinatorial gene expression using multiple episomal vectors

Episomal vectors offer a powerful alternative to integrative recombination for transgene expression in mammalian cells. In this study, various combinations of G protein-coupled receptors (GPCRs) and the G protein subunit G(i2)alpha, were stably expressed from separate episomal vectors in 293-EBNA (293E) cells. Each episome did not adversely affect the others, as gauged by episomal copy number, steady-state mRNA levels and the presence of functional receptors and G protein. Cell lines expressing genes from multiple autonomously replicating vectors were stable just two weeks after transfection, and remained stable in continuous culture for at least 5months. Co-expression of supplementary G(i2)alpha with receptor amplifies the magnitude of signal transduction thereby permitting the development of more sensitive high throughput functional assays. Given these results, combinatorial transfection is the strategy of choice for generating stable cell lines expressing multiple genes for the study of signal-transduction pathways or the evaluation of receptor ligands.     

Invited review: sex-based differences in gene expression.

Certain diseases are more prevalent among women than men. The reasons for this increased prevalence are unknown, but there could be a genetic basis. Increased expression of X-linked genes in females, protective effects of Y-linked genes in males, or sex-limited gene expression that is developmentally or hormonally regulated could all account for these differences. Analysis of individuals with and without genetic sex reversal provides a means for distinguishing between genetic and hormonal causes. This can be complemented by genetic linkage and gene expression profiling to aid in the identification of candidate genes.