Abstract not found

High-throughput molecular data are revolutionizing biology by providing massive amounts of information about gene expression and regulation. Such information is applicable both to furthering our understanding of fundamental biology and to developing new diagnostic and treatment approaches for diseases. However, novel mathematical methods are needed for extracting biological knowledge from highdimensional, complex and noisy data sources. In this thesis, I develop and apply three novel computational approaches for this task. The common theme of these approaches is that they seek to discover meaningful groups of genes, which confer robustness to noise and compress complex information into interpretable models. I first present the GRAM algorithm, which fuses information from genome-wide expression and in vivo transcription factor-DNA binding data to discover regulatory networks of

A cell’s behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large number of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theoretical descriptions of the relationships between different cellular components. Recent

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells. Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes controlling common functions, such as the formation of a transcriptional complex or the availability of a signaling pathway. Understanding this organization is crucial to elucidate normal cell physiology as well as to dissect complex pathologic phenotypes. Studies in lower organisms indicate that the structure of both protein-protein interaction and metabolic networks is of a hierarchical scale-free nature 1,2 , characterized by an inverse relationship between the number of nodes and their connectivity (scale-free) and by a preferential interaction among highly connected genes, called hubs (hierarchical). Although scale-free networks may represent a common blueprint for all cellular constituents, evidence of scale-free topology in higher-order eukaryotic cells is currently limited to coexpression networks 3,4 , which tend to identify entire subpathways rather than individual interactions. Identifying the organizational network of eukaryotic cells is still a key goal in understanding cell physiology and disease. Genome-wide clustering of gene-expression profiles has provided an initial step towards the elucidation of cellular networks. But the organization of gene-expression profile data into functionally meaningful genetic information has proven difficult and so far has fallen short of uncovering the intricate structure of cellular interactions. This challenge, called network reverse engineering or deconvolution, has led to an entirely new class of methods aimed at producing high-fidelity representations of cellular networks as graphs, where nodes represent genes and edges between them represent interactions, either between the encoded proteins or between the encoded proteins and the genes (we use 'genetic interaction' to refer to both types of mechanisms). Available methods fall into four broad categories: optimization methods 5-7 , which maximize a scoring function over alternative network models; regression techniques Here we present the successful reverse engineering of geneexpression profile data from human B cells. Our study is based on ARACNe (algorithm for the reconstruction of accurate cellular networks), a new approach for the reverse engineering of cellular networks from microarray expression profiles. ARACNe first identifies statistically significant gene-gene coregulation by mutual information, an information-theoretic measure of relatedness. It then eliminates indirect relationships, in which two genes are coregulated through one or more intermediaries, by applying a well-known staple of data

associations, integrated and transferred across organisms

Research article

Abstract not found

Over the past few years, due to the popularisation of high-throughput methodologies such as DNA microarrays, the possibility of obtaining experimental data has increased significantly. Nevertheless, the interpretation of the results, which involves translating these data into useful biological knowledge, still remains a challenge. The methods and strategies used for this interpretation are in continuous evolution and new proposals are constantly arising. Initially, a two-step approach was used in which genes of interest were initially selected, based on thresholds that consider only experimental values, and then in a second, independent step the enrichment of these genes in biologically relevant terms, was analysed. For different reasons, these methods are relatively poor in terms of performance and a new generation of procedures, which draw inspiration from systems biology criteria, are currently under development. Such procedures, aim to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes.

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson’s correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat. Key words: gene coexpression; Pearson’s correlation coefficient; GeneChip summarization; Arabidopsis 1.

seven model animals