Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
Genomic stability is critical for the clinical use of human embryonic and induced pluripotent stem cells. We performed high resolution SNP (single nucleotide polymorphism) analysis on 186 pluripotent and 119 non-pluripotent samples. We report a higher frequency of subchromosomal copy number variations in pluripotent samples compared to non-pluripotent samples, with variations enriched in specific genomic regions. The distribution of these variations differed between hESCs and hiPSCs, characterized by large numbers of duplications found in a few hESC samples and moderate numbers of deletions distributed across many hiPSC samples. For hiPSCs, the reprogramming process was associated with deletions of tumor suppressor genes, while time in culture was associated with duplications of oncogenic genes. We also observed duplications that arose during a differentiation protocol. Our results illustrate the dynamic nature of genomic abnormalities in pluripotent stem cells and the need for frequent genomic monitoring to assure phenotypic stability and clinical safety.
A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the halflife of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.degradation ͉ proteomics ͉ cycloheximide ͉ epitope-tagged T he availability of whole-genome sequences and the advent of microarray technology have made global analyses of mRNA expression mainstream. However, most biological processes are mediated by proteins, which are subject to posttranscriptional regulation that is generally not observable at mRNA levels. A complete understanding of biological systems requires knowledge of protein properties, which is ultimately the goal of proteomics.Despite tremendous technical advances and effort in proteomics, the chemical heterogeneity of proteins and the large dynamic range of protein abundance make it challenging to establish global proteomic assays. This obstacle has been circumvented in the yeast Saccharomyces cerevisiae with the availability of two collections of yeast strains expressing epitope-tagged fusion proteins, one by using the tandem affinity purification (TAP) tag and a second employing the GFP (1, 2). In an initial study, we analyzed the TAP-tagged strain collection by Western blotting to quantify steady-state levels of protein abundance in actively dividing yeast cells. These data augmented previous efforts to quantify protein abundance by using mass spectrometry and 2D gel electrophoresis and provided a more comprehensive estimate of protein levels in a eukaryotic cell (3, 4).The availability of high-throughput protein abundance data has facilitated analysis of the relationship between protein abundance and mRNA levels. Although a statistically significant correlation is observed between these parameters, individual genes with similar mRNA levels can produce proteins with very different abundances. This complication makes it difficult to extrapolate from mRNA levels and microarray experiments to protein abundance. Three potential...
In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).
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