Fractal landscapes and molecular evolution: modeling the myosin heavy chain gene family.

Mapping nucleotide sequences onto a "DNA walk" produces a novel representation of DNA that can then be studied quantitatively using techniques derived from fractal landscape analysis. We used this method to analyze 11 complete genomic and cDNA myosin heavy chain (MHC) sequences belonging to 8 different species. Our analysis suggests an increase in fractal complexity for MHC genes with evolution with vertebrate > invertebrate > yeast. The increase in complexity is measured by the presence of long-range power-law correlations, which are quantified by the scaling exponent alpha. We develop a simple iterative model, based on known properties of polymeric sequences, that generates long-range nucleotide correlations from an initially noncorrelated coding region. This new model-as well as the DNA walk analysis-both support the intron-late theory of gene evolution.     

Mathematical characterization of three-dimensional gene expression patterns

MOTIVATION: The importance of a systematic methodology for the mathematical characterization of three-dimensional gene expression patterns in embryonic development. METHODS: By combining lacunarity and multiscale fractal dimension analyses with computer-based methods of three-dimensional reconstruction, it becomes possible to extract new information from in situ hybridization studies. Lacunarity and fractality are appropriate measures for the cloud-like gene activation signals in embryonic tissues. The newly introduced multiscale method provides a natural extension of the fractal dimension concept, being capable of characterizing the fractality of geometrical patterns in terms of spatial scale. This tool can be systematically applied to three-dimensional patterns of gene expression. RESULTS: Applications are illustrated using the three-dimensional expression patterns of the myogenic marker gene Myf5 in a series of differentiating somites of a mouse embryo.     

Histomorphometric parameters and fractal complexity of the equine placenta from healthy and sick foals

Computer-based digital image analysis of tissue samples shows promise both to reduce the subjectivity of traditional manual tissue assessments and potentially to shorten the time required to analyze each sample. The present study used digital image analysis to investigate the histomorphometric parameters and fractal complexity of the equine placenta from healthy and sick foals. We hypothesized that the placentas of sick foals could have a different growth pattern and complexity that could be objectively estimated by their fractal dimension (FD). Fourteen placentas from 30 mares were selected in the 2013 breeding season and divided into two groups: seven mares with normal pregnancy, eutocic delivery, and healthy foals (group 1) and seven mares with normal or high-risk pregnancy, eutocic delivery and sick foals (group 2). Four mares in group 2 were classified as having a high-risk pregnancy on the basis of anamnesis and/or ultrasound findings. Clinical diagnosis of group 2 foals included perinatal asphyxia syndrome (n = 4), prematurity/dysmaturity (n = 2), and both diagnoses (n = 1). Seven out of fourteen placentas showed diffuse gross abnormalities. Grossly abnormal placentas were observed in one out of seven (14.28%) animals in group 1 and in six out of seven (85.72%) animals in group 2. Digital image analysis proved to be reliable and efficient in segmentation, calculation, outline extraction of villi as also resulted in sampled test images. The placentas of group 1 foals displayed a uniform and homogeneous villi development, as revealed by geometric parameters and FD. These results can be interpreted as a harmonic growth pattern of microcotyledons throughout the placenta in healthy foals. By contrast, the placentas of group 2 foals showed a nonuniform growth pattern and complexity with more villi and more developed villi in pregnant horn (PH) and nonpregnant horn (NPH) compared with body (B) and higher FD in NPH than in the other areas. This finding can be interpreted as a compensatory growth with increased complexity. Our results show that morphometric analysis, particularly FD measurement, can be proposed as an ancillary histological tool for equine placenta evaluation. Chorionic villi tend to have greater branching and complexity in sick than in healthy foals, particularly in the NPH. This could represent an attempt to increase the exchange area between fetal and maternal compartments of the equine placenta, and merits further investigation.     

Lacunarity as a novel measure of cancer cells behavior

An important goal in many branches of science, especially in molecular biology and medicine is the quantitative analysis of the structures and their morphology. The morphology can be analyzed in many ways, in particular by the fractal analysis. Apart from the fractal dimension, an important part of the fractal analysis is the lacunarity measurement which, roughly speaking, characterizes the distribution of gaps in the fractal: a fractal with high lacunarity has large gaps. In this paper, we present an extension of the lacunarity measure to objects with nonregular shapes that enables us to provide a successful discrimination of cancer cell lines. The cell lines differ in the shape of vacuole (the gaps in their body) which is perfectly suited for the lacunarity analysis.     

Modelling applicability of fractal analysis to efficiency of soil exploration by roots

BACKGROUND AND AIMS: Fractal analysis allows calculation of fractal dimension, fractal abundance and lacunarity. Fractal analysis of plant roots has revealed correlations of fractal dimension with age, topology or genotypic variation, while fractal abundance has been associated with root length. Lacunarity is associated with heterogeneity of distribution, and has yet to be utilized in analysis of roots. In this study, fractal analysis was applied to the study of root architecture and acquisition of diffusion-limited nutrients. The hypothesis that soil depletion and root competition are more closely correlated with a combination of fractal parameters than by any one alone was tested. MODEL: The geometric simulation model SimRoot was used to dynamically model roots of various architectures growing for up to 16 d in three soil types with contrasting nutrient mobility. Fractal parameters were calculated for whole roots, projections of roots and vertical slices of roots taken at 0, 2.5 and 5 cm from the root origin. Nutrient depletion volumes, competition volumes, and relative competition were regressed against fractal parameters and root length. KEY RESULTS: Root length was correlated with depletion volume, competition volume and relative competition at all times. In analysis of three-dimensional, projected roots and 0 cm slices, log(fractal abundance) was highly correlated with log(depletion volume) when times were pooled. Other than this, multiple regression yielded better correlations than regression with single fractal parameters. Correlations decreased with age of roots and distance of vertical slices from the root origin. Field data were also examined to see if fractal dimension, fractal abundance and lacunarity can be used to distinguish common bean genotypes in field situations. There were significant differences in fractal dimension and fractal abundance, but not in lacunarity. CONCLUSIONS: These results suggest that applying fractal analysis to research of soil exploration by root systems should include fractal abundance, and possibly lacunarity, along with fractal dimension.     

Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma

The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, there is a growing interest among clinicians regarding the potential diagnostic utility of computerised image analysis. Recognising that there exist significant shortcomings in currently available algorithms, we are motivated to investigate the utility of lacunarity, a simple statistical measure previously used in geology and other fields for the analysis of fractal and multi-scaled images, in the automated assessment of melanocytic naevi and melanoma. Digitised dermoscopic images of 111 benign melanocytic naevi, 99 dysplastic naevi and 102 melanomas were obtained over the period 2003 to 2008, and subject to lacunarity analysis. We found the lacunarity algorithm could accurately distinguish melanoma from benign melanocytic naevi or non-melanoma without introducing many of the limitations associated with other previously reported diagnostic algorithms. Lacunarity analysis suggests an ordering of irregularity in melanocytic lesions, and we suggest the clinical application of this ordering may have utility in the naked-eye dermoscopic diagnosis of early melanoma.     

Nonlinearity and fractality in the variability of cardiac period in the lizard, Gallotia galloti: effects of autonomic blockade

Both nonlinear and fractal properties of beat-to-beat R-R interval variability signal (RRV) of freely moving lizards (Gallotia galloti) were studied in baseline and under autonomic nervous system blockade. Nonlinear techniques allowed us to study the complexity, chaotic behavior, nonlinearity, stationarity, and regularity over time of RRV. Scaling behavior of RRV was studied by means of fractal techniques. The autonomic nervous system blockers used were atropine, propranolol, prazosin, and yohimbine. The nature of RRV was linear in baseline and under beta-, alpha(1)- and alpha(2)-adrenoceptor blockades. Atropine changed the linear nature of RRV to nonlinear and increased its stationarity, regularity and fractality. Propranolol increased the complexity and chaotic behavior, and decreased the stationarity, regularity, and fractality of RRV. Both prazosin and yohimbine did not change any of the nonlinear and fractal properties of RRV. It is suggested that 1) the use of both nonlinear and fractal analysis is an appropriate approach for studying cardiac period variability in reptiles; 2) the cholinergic activity, which seems to make the alpha(1)-, alpha(2)- and beta-adrenergic activity interaction unnecessary, determines the linear behavior in basal RRV; 3) fractality, as well as both RRV regularity and stationarity over time, may result from the balance between cholinergic and beta-adrenergic activities opposing actions; 4) beta-adrenergic activity may buffer both the complexity and chaotic behavior of RRV, and 5) neither the alpha(1)- nor the alpha(2)-adrenergic activity seem to be involved in the mediation of either nonlinear or fractal components of RRV.     

Wavelet packet fractal analysis of neuronal morphology

An image analysis method called two-dimensional wavelet packet analysis (2D WPA) is introduced to quantify branching complexity of neurons. Both binary silhouettes and contour profiles of neurons were analyzed to determine accuracy and precision of the fractal dimension in cell classification tasks. Two-dimensional WPA plotted the slope of decay for a sorted list of discrete wavelet packet coefficients belonging to the adapted wavelet best basis to obtain the fractal dimension for test images and binary representations of neurons. Two-dimensional WPA was compared with box counting and mass-radius algorithms. The results for 2D WPA showed that it could differentiate between neural branching complexity in cells of different type in agreement with accepted methods. The importance of the 2D WPA method is that it performs multiresolution decomposition in the horizontal, vertical, and diagonal orientations.     

Down syndrome’s brain dynamics: analysis of fractality in resting state

To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions.     

Fractal genome sequences

The existence of fractal sets of DNA sequences have long been suspected on the basis of statistical analyses of genome data. In this article we identify for the first time explicitly the GA-sequences as a class of fractal genomic sequences that are easy to recognize and to extract, and are scattered densely throughout the chromosomes of a large number of genomes from different species and kingdoms including the human genome. Their existence and their fractality may have significant consequences for our understanding of the origin and evolution of genomes. Furthermore, as universal and natural markers they may be used to chart and explore the non-coding regions.     

Self-similar mitochondrial DNA

We show that repeated sequences, like palindromes (local repetitions) and homologies between two different nucleotide sequences (motifs along the genome), compose a self-similar (fractal) pattern in mitochondrial DNA. This self-similarity comes from the looplike structures distributed along the genome. The looplike structures generate scaling laws in a pseudorandom DNA walk constructed from the sequence, called a Lévy flight. We measure the scaling laws from the generalized fractal dimension and singularity spectrum for mitochondrial DNA walks for 35 different species. In particular, we report characteristic loop distributions for mammal mitochondrial genomes.     

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length of DNA sequences, which is practical even when long sequences, such as whole genome sequences, are compared. We tested five distance measures for the estimation of sequence similarities, and found that the histogram intersection and Manhattan distance are the most appropriate ones for phylogenetic analyses.     

Dynamics of protein evolution

Protein sequences of the SWISS-PROT data bank were analysed by fractal techniques and harmonic analysis. In both cases, the results show the presence of self-affinity, a kind of self-similarity, in the sequences. Self-similarity is a sign of fractality and fractality is a consequence of a chaotic dynamical process. The evolution of the protein sequences is modelled as a dynamical system. The abundance of the fractal form in biology and creation of fractal forms as a result of “chaos” is already established. It may be noted that the word “chaos” here implies that most predictable processes can also become unpredictable under certain conditions, and that the most unpredictable processes are not as unpredictable as they are expected to be. In evolutionary dynamics, this allows scope for mutations and variations in otherwise predictable situations, potentially leading to increased diversity. Part of this work was presented at the National Symposium on Evolution of Life.     

Foliage randomness and light interception in 3-D digitized trees: an analysis from multiscale discretization of the canopy

Light models for vegetation canopies based on the turbid medium analogy are usually limited by the basic assumption of random foliage dispersion in the canopy space. The objective of this paper was to assess the effect of three possible sources of non-randomness in tree canopies on light interception properties. For this purpose, four three-dimensional (3-D) digitized trees and four theoretical canopies – one random and three built from fractal rules – were used to compute canopy structure parameters and light interception, namely the sky-vault averaged STAR (Silhouette to Total Area Ratio). STAR values were computed from (1) images of the 3-D plants, and (2) from a 3-D turbid medium model using space discretization at different scales. For all trees, departure from randomness was mainly due to the spatial variations in leaf area density within the canopy volume. Indeed STAR estimations, based on turbid medium assumption, using the finest space discretization were very close to STAR values computed from the plant images. At this finest scale, foliage dispersion was slightly clumped, except one theoretical fractal canopy, which showed a marked regular dispersion. Taking into account a non-infinitely small leaf size, whose effect is theoretically to shorten self-shading, had a minor effect on STAR computations. STAR values computed from the 3-D turbid medium were very sensitive to plant lacunarity, a parameter introduced in the context of fractal studies to characterize the distribution of gaps in porous media at different scales. This study shows that 3-D turbid medium models based on space discretization are able to give correct estimation of light interception by 3-D isolated trees, provided that the 3-D grid is properly defined, that is, discretization maximizes plant lacunarity.     

Characterization of melanophore morphology by fractal dimension analysis

Fractal or focal dimension (FD) analysis is a valuable tool to identify physiologic stimuli at the cellular and tissue levels that allows for quantification of cell perimeter complexity. The FD analysis was determined on fluorescence images of caffeine- or epinephrine-treated (or untreated control) killifish Fundulus heteroclitus (Linneaus) melanophores in culture. Cell perimeters were indicated by rhodamine-phalloidin labeling of cortical microfilaments using box-counting FD analysis. Caffeine-treated melanophores displayed dispersed melanosomes in cells with less serrated edges and reduced FD and complexity. Complexity in epinephrine-treated cells was significantly higher than the caffeine-treated cells or in the control. Cytoarchitectural variability of the cell perimeter is expected because cells change shape when cued with agents. Epinephrine-treated melanophores demonstrated aggregated melanosomes in cells with more serrated edges, significantly higher FD and thus complexity. Melanophores not treated with caffeine or epinephrine produced variable distributions of melanosomes and resulted in cells with variably serrated edges and intermediate FD with a larger SE of the regression and greater range of complexity. Dispersion of melanosomes occurs with rearrangements of the cytoskeleton to accommodate centrifugal distribution of melanosomes throughout the cell and to the periphery. The loading of melanosomes onto cortical microfilaments may provide a less complex cell contour, with the even distribution of the cytoskeleton and melanosomes. Aggregation of melanosomes occurs with rearrangements of the cytoskeleton to accommodate centripetal distribution of melanosomes. The aggregation of melanosomes may contribute to centripetal retraction of the cytoskeleton and plasma membrane. The FD analysis is, therefore, a convenient method to measure contrasting morphologic changes within stimulated cells.     

Nano- and microscale mechanical properties of erythrocytes in hereditary spherocytosis

Hereditary spherocytosis (HS), an erythrocyte membranopathy, is a heterogeneous disease, even at the level of the erythrocyte population. The paper aims at studying the mechanical properties (the Young’s modulus, median and RMS roughness of friction force maps; fractal dimension, lacunarity and spatial distribution parameters of lateral force maps) of the cell surface layer of the erythrocytes of two different morphologies (discocytes and spherocytes) in HS using atomic force microscopy. The results of spatial-spectral and fractal analysis showed that the mechanical property maps of the HS spherocyte surface were more structurally homogeneous compared to the maps of HS discocytes. HS spherocytes also had a reduced RMS roughness and lacunarity of the mechanical property maps. The Young’s modulus and averaged friction forces over the microscale HS spherocyte surface regions were approximately 20% higher than that of HS discocytes. The revealed significant difference at the nano- and microscales in the structural and mechanical properties of main (discoidal and spheroidal) morphological types of HS erythrocytes can potentially cause blood flow disturbance in the vascular system in HS.     

Mean free energy topology for nucleotide sequences of varying composition based on secondary structure calculations.

The mean free energy generated from the secondary structure of RNA sequences of varying length and composition has been studied by way of probability theory. The expected boundaries or maximal and minimal values of a given distribution are explored and a method for estimating error as a function of the number of shuffled sequences is also examined. For typical nucleotide sequences found in biologically active organisms, the mean free energy, free energy distributions and errors appear to be scalable in terms of a fixed set of algorithm-dependent parameters and the nucleotide composition of the particular sequence under evaluation. In addition, a general semi-analytical formula for predicting the mean free energy is proposed which, at least to first-order approximation, can be used to rapidly predict the mean free energy of any sequence length and composition of RNA. The general methodology appears to be algorithm independent. The results are expected to provide a reference point for certain types of analysis related to structure of RNA or DNA sequences and to assist in measuring the somewhat related matter of complexity in algorithm development. Some related applications are discussed.     

SEGMENT: identifying compositional domains in DNA sequences

MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.