Biomedical subjects

Eric de Silva

Publications and source records attributed to Eric de Silva.

5 recordsLinked to original sources

PubMed2006

The effects of incomplete protein interaction data on structural and evolutionary inferences.

BACKGROUND: Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis. RESULTS: Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. CONCLUSION: Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

Evolution, Molecular↗

PubMed2005

Complex networks and simple models in biology.

The analysis of molecular networks, such as transcriptional, metabolic and protein interaction networks, has progressed substantially because of the power of models from statistical physics. Increasingly, the data are becoming so detailed--though not always complete or correct--that the simple models are reaching the limits of their usefulness. Here, we will discuss how network information can be described and to some extent quantified. In particular statistics offers a range of tools, such as model selection, which have not yet been widely applied in the analysis of biological networks. We will also outline a number of present challenges posed by biological network data in systems biology, and the extent to which these can be addressed by new developments in statistics, physics and applied mathematics.

Animals↗

PubMed2005

Recombination hotspots as a point process.

The variation of the recombination rate along chromosomal DNA is one of the important determinants of the patterns of linkage disequilibrium. A number of inferential methods have been developed which estimate the recombination rate and its variation from population genetic data. The majority of these methods are based on modelling the genealogical process underlying a sample of DNA sequences and thus explicitly include a model of the demographic process. Here we propose a different inferential procedure based on a previously introduced framework where recombination is modelled as a point process along a DNA sequence. The approach infers regions containing putative hotspots based on the inferred minimum number of recombination events; it thus depends only indirectly on the underlying population demography. A Poisson point process model with local rates is then used to infer patterns of recombination rate estimation in a fully Bayesian framework. We illustrate this new approach by applying it to several population genetic datasets, including a region with an experimentally confirmed recombination hotspot.

Bayes Theorem↗

PubMed2004

HIV and the CCR5-Delta32 resistance allele.

The combination of molecular biology, epidemiology, virology, evolutionary and population genetics has enabled us to understand the delicate interplay between HIV and the CCR5-Delta32 HIV resistance allele. We here review and collect from the different approaches to show how they can be combined to elucidate the interaction between host and pathogen genetics in this system. We will present an overview of the normal role of CCR5, its involvement in HIV, the molecular biology of the CCR5-Delta32 allele and its probable origins. By focusing on this well-documented and important system we hope to demonstrate the power that such a "holistic" approach might offer in the study of infectious diseases.

Alleles↗

PubMed2004

The extent and importance of intragenic recombination.

We have studied the recombination rate behaviour of a set of 140 genes which were investigated for their potential importance in inflammatory disease. Each gene was extensively sequenced in 24 individuals of African descent and 23 individuals of European descent, and the recombination process was studied separately in the two population samples. The results obtained from the two populations were highly correlated, suggesting that demographic bias does not affect our population genetic estimation procedure. We found evidence that levels of recombination correlate with levels of nucleotide diversity. High marker density allowed us to study recombination rate variation on a very fine spatial scale. We found that about 40 per cent of genes showed evidence of uniform recombination, while approximately 12 per cent of genes carried distinct signatures of recombination hotspots. On studying the locations of these hotspots, we found that they are not always confined to introns but can also stretch across exons. An investigation of the protein products of these genes suggested that recombination hotspots can sometimes separate exons belonging to different protein domains; however, this occurs much less frequently than might be expected based on evolutionary studies into the origins of recombination. This suggests that evolutionary analysis of the recombination process is greatly aided by considering nucleotide sequences and protein products jointly.

Africa↗