Wasyl Malyj

The case for strategic international alliances to harness nutritional genomics for public and personal health

Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene-nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient-genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countries.

Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap, a nonlinear algorithm

BackgroundLife processes are determined by the organisms genetic profile and multiple environmental variables. However the interaction between these factors is inherently non-linear [1]. Microarray data is one representation of the nonlinear interactions among genes and genes and environmental factors. Still most microarray studies use linear methods for the interpretation of nonlinear data. In this study, we apply Isomap, a nonlinear method of dimensionality reduction, to analyze three independent large Affymetrix high-density oligonucleotide microarray data sets.ResultsIsomap discovered low-dimensional structures embedded in the Affymetrix microarray data sets. These structures correspond to and help to interpret biological phenomena present in the data. This analysis provides examples of temporal, spatial, and functional processes revealed by the Isomap algorithm. In a spinal cord injury data set, Isomap discovers the three main modalities of the experiment – location and severity of the injury and the time elapsed after the injury. In a multiple tissue data set, Isomap discovers a low-dimensional structure that corresponds to anatomical locations of the source tissues. This model is capable of describing low- and high-resolution differences in the same model, such as kidney-vs.-brain and differences between the nuclei of the amygdala, respectively. In a high-throughput drug screening data set, Isomap discovers the monocytic and granulocytic differentiation of myeloid cells and maps several chemical compounds on the two-dimensional model.ConclusionVisualization of Isomap models provides useful tools for exploratory analysis of microarray data sets. In most instances, Isomap models explain more of the variance present in the microarray data than PCA or MDS. Finally, Isomap is a promising new algorithm for class discovery and class prediction in high-density oligonucleotide data sets.

It's a dog's life

Abstract Second Molecular Genetics and Canine Genetic Health Conference, St Louis, MO, USA, 31 October–1 November 1997.

DNA Testing in the Equine

This article discusses the concepts and methods underlying the present-day state of production-robust nucleic acid-based identity, parentage, and diagnostic tests in the equine. Given the rapid pace of progress, continuing developments are expected in equine nucleic acid-based diagnostic testing for infectious agents and for a small but significant number of genetic traits and diseases, primarily those that exhibit single-gene Mendelian inheritance. As marker discovery accelerates and testing and analysis methods become faster, more accurate, and less expensive, whole genome scanning will become economically feasible. Subsequent adoption of high-volume whole genome scanning of equines and other animal species would initiate a new revolution in veterinary medicine, a revolution that would greatly benefit both animals and humans.

New directions in scientific computing: Impact of advances in microprocessor architecture and system design

The new generation of microcomputers has brought computing power previously restricted to mainframe and supermini computers within the reach of individual scientific laboratories. Microcomputers can now provide computing speeds rivaling mainframes and computational accuracies exceeding those available in most computer centers. Inexpensive memory makes possible the transfer to microcomputers of software packages developed for mainframes and tested by years of experience. Combinations of high level languages and assembler subroutines permit the efficient design of specialized applications programs. Microprocessor architecture is approaching that of superminis, with coprocessors providing major contributions to computing power. The combined result of these developments is a major and perhaps revolutionary increase in the computing power now available to scientists.

Computer simulation of rapidly responding forced exponential systems

Two common problems in computer simulations are the decisions to ignore or include a particular element of a system under study in a model and the choice of an appropriate integration algorithm. To examine aspects of these problems, a simple exponential system is considered in which a large simulation error is induced by a rather small truncation error. The effect of computational precision, step size and hardware selection on this error is examined at standard and extended precisions over a range of step sizes and on a variety of computers. For this model, simulation accuracy is an exponential function of the number of bits in the mantissa of the computer word. Optimal step size is a function of accuracy required and precision used; a trade-off between truncation and round-off errors becomes important as accuracy requirements increase. Machine selection is important primarily in economic terms if the required precision is available. We conclude that the effect on a simulation of small terms such as truncation errors can be unexpectedly large, that solutions should always be checked, and that high precision and wide dynamic range are important to the successful computer simulation of models such as that examined.