Euclidian space and grouping of biological objects

MOTIVATION: Biological objects tend to cluster into discrete groups. Objects within a group typically possess similar properties. It is important to have fast and efficient tools for grouping objects that result in biologically meaningful clusters. Protein sequences reflect biological diversity and offer an extraordinary variety of objects for polishing clustering strategies. Grouping of sequences should reflect their evolutionary history and their functional properties. Visualization of relationships between sequences is of no less importance. Tree-building methods are typically used for such visualization. An alternative concept to visualization is a multidimensional sequence space. In this space, proteins are defined as points and distances between the points reflect the relationships between the proteins. Such a space can also be a basis for model-based clustering strategies that typically produce results correlating better with biological properties of proteins. RESULTS: We developed an approach to classification of biological objects that combines evolutionary measures of their similarity with a model-based clustering procedure. We apply the methodology to amino acid sequences. On the first step, given a multiple sequence alignment, we estimate evolutionary distances between proteins measured in expected numbers of amino acid substitutions per site. These distances are additive and are suitable for evolutionary tree reconstruction. On the second step, we find the best fit approximation of the evolutionary distances by Euclidian distances and thus represent each protein by a point in a multidimensional space. The Euclidian space may be projected in two or three dimensions and the projections can be used to visualize relationships between proteins. On the third step, we find a non-parametric estimate of the probability density of the points and cluster the points that belong to the same local maximum of this density in a group. The number of groups is controlled by a sigma-parameter that determines the shape of the density estimate and the number of maxima in it. The grouping procedure outperforms commonly used methods such as UPGMA and single linkage clustering.     

Method of receiving differential images of objects autofluorescence in the process of photobleaching

The microscope with a high sensitive video camera and laser illumination was used to study autofluorescence changes of different structures in the photobleached region with a different speed. The work with images using the ImageJ program is described in application how to receive differential images of objects autofluorescence in the process of photobleaching.     

Laser Doppler spectroscopy of biological objects

Laser Doppler spectroscopy permits to investigate the velocity distribution of the biological objects either single cells or ensembles. The main principles of laser Doppler spectroscopy and its practical application connected with the spectral correlation dependence of biological objects have been studied.     

A simple method for estimation of phospholipids in biological objects

A simple method to estimate phospholipids is elaborated. This method is based on determination of optical density of phospholipid-molybdate complexes in chloroform. The method is used for the quantitative determination of phospholipids in biomembranes, liposomes and blood serum without their preliminary extraction, as well as in chloroform, methanol and chloroform-methanol solutions. It is also modified for the phospholipids determination in chromatographic fractions on silufol plates.     

Method of purifying glutaraldehyde for microscopic research on biological objects

A simple and reliable method of purification and determination of glutaraldehyde concentration for histochemical fixation is proposed. Purification of glutaraldehyde is provided by vacuum distillation with a rotational-filmy evaporator, and its concentration is determined using refractometer.     

Interactive visualization and exploration of relationships between biological objects

Genome sequencing and microarray technology produce ever-increasing amounts of complex data that need analysis. Visualization is an effective analytical technique that exploits the ability of the human brain to process large amounts of data. Here, we review traditional visualization methods based on clustering and tree representation, and also describe an alternative approach that involves projecting objects onto a Euclidean space in a way that reflects their structural or functional distances. Data are visualized without preclustering and can be dynamically explored by the user using ‘virtual-reality’. We illustrate this approach with two case studies from protein topology and gene expression.