Computational methods in cancer gene networking

In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks

Network rewiring is an important mechanism of gene essentiality change.

Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality

The evolution of gene duplicates in angiosperms and the impact of protein-protein interactions and the mechanism of duplication

Gene duplicates, generated through either whole genome duplication (WGD) or small-scale duplication (SSD), are prominent in angiosperms and are believed to play an important role in adaptation and in generating evolutionary novelty. Previous studies reported contrasting evolutionary and functional dynamics of duplicate genes depending on the mechanism of origin, a behavior that is hypothesized to stem from constraints to maintain the relative dosage balance between the genes concerned and their interaction context. However, the mechanism ultimately influencing loss and retention of gene duplicates over evolutionary time are not yet fully elucidated. Here, by using a robust classification of gene duplicates in Arabidopsis thaliana, Solanumlycopersicum, and Zea mays, large RNAseq expression compendia and an extensive protein-protein interaction (PPI) network from Arabidopsis, we investigated the impact of PPIs on the differential evolutionary and functional fate ofWGD and SSD duplicates. In all three species, retained WGD duplicates show stronger constraints to diverge at the sequence and expression level than SSD ones, a pattern that is also observed for shared PPI partners between Arabidopsis duplicates. PPIs are preferentially distributed among WGD duplicates and specific functional categories. Furthermore, duplicates with PPIs tend to be under stronger constraints to evolve than their counterparts without PPIs regardless of their mechanism of origin. Our results support dosage balance constraint as a specific property of genes involved in biological interactions, including physical PPIs, and suggest that additional factors may be differently influencing the evolution of genes following duplication, depending on the species, time, and mechanism of origin

The EM Algorithm and the Rise of Computational Biology

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Endogenous Viral Etiology of Prion Diseases

Transmissible spongiform encephalopathies (TSEs), or prion diseases, are a group of incurable neurodegenerative disorders, including Kuru and Creutzfeldt-Jakob disease in humans, “mad cow” disease in cattle, and scrapie in sheep. This paper presents structural, genetic, and evolutionary evidence supporting an endogenous TSE virus model that integrates the three major traditional views on the nature of TSE pathogens, the conventional virus view, the prion hypothesis, and the virino concept, into a novel conceptual and evolutionary framework. According to this model, the TSE pathogens are symbiotic endogenous viruses that inadvertently produce transmissible viral particles that lack the viral genome and are composed primarily of the viral prion protein (PrP). Production of defective viral particles that contain a partial genome or lack the viral genome entirely is a relatively common event in the life cycle of many viruses. Similar to the normal viral particles, which contain a genome, these defective viral particles can be transmitted to new host cells. Obviously, in the absence of viral genome, these protein-only viral particles cannot establish a productive infection. However, if these viral particles enter a host cell that carries the parental or a related virus and induce the production of similar protein-only particles, then they would appear as self-replicating, protein-only infectious pathogens if mistakenly taken out from the context of the viral life cycle. This misconception, which is rooted into the current dogma of viruses as viral particles, led to the development of the prion theory. The endogenous TSE virus model is consistent with the TSE data and offers solutions to many enigmatic features associated with TSE, including the function of PrP that, despite more than two decades of TSE research conducted primarily within the framework of the prion hypothesis, is still not known. According to the TSE endogenous virus model, PrP is the protein of an endogenous virus that has co-evolved with their vertebrate hosts by providing a protective function against pathogenic viruses. The evidence for the endogenous TSE virus model and for the antiviral protective function of PrP is strong, and they are fully open to additional experimental testing. The endogenous virus model opens the TSE research field to new interpretations and directions, both in basic research and in associated biomedical and public health fields, and could lead to development of new diagnostic and therapeutic approaches

Probabilistic methods in the analysis of protein interaction networks

Imperial Users onl