Current Bioinformatics - Volume 4, Issue 2, 2009

Heart rate variability (HRV) is a reliable and powerful tool for the assessment of sympathetic and parasympathetic functions of the autonomic system. Hence HRV is widely used as tool to monitor post myocardial-infarction patients and also diabetes subjects, because as a chronic side effect diabetes affects peripheral and autonomous nervous system. In order to determine how this HRV decreases in diabetic patients, we have developed a biophysical model based on neuroanatomical data about electrophysico-chemical mechanisms of sinoatrial node's bioelectrical activity, involved in regulating heart-rate activity in healthy and diabetic subjects. In this biophysical model, the sinoatrial node is under the control of the sympathetic nervous system, represented by the adrenergic neuron. This neuron modulates the activities of sodium (Na + ) and (K + ) ionic channels, which are located on the membrane of sinoatrial cells. The model describes: a) the dynamics of propagation of the electric signal along the nerve pathway, b) the process of electrophysico-chemical coupling at the synaptic level, and c) changes in heart-rate as a result of decrease/increase in the frequency of discharges of the sinoatrial node. The model reproduces, quantitatively and qualitatively, the phenomenon of heart-rate variability in normal and diabetes subjects. Hence, our model is shown to provide representative simulation of the electrophysico-chemical mechanisms involved in hyperglycemia, that result in HRV decrease. The model can also be adapted to simulate the effects of antidiabetic drug therapy.

The data gained by digital high-speed endoscopy and its objective analysis provide many new possibilities to enhance the understanding and investigation of laryngeal dynamics and its pathologies. High-speed imaging overcomes disadvantages of the currently used technique of videostroboscopy. Additionally, objective evaluation of the dynamics finally enables the beginning of evidenced based diagnostics in endoscopic voice diagnostics. Purpose of Review: The purpose of this review is to describe the application and usefulness of endoscopic high-speed digital imaging in combination with objective analysis for clinical diagnostics and for understanding dynamics in the larynx. Recent Findings: High-speed digital endoscopy (2000 - 4000 fps) allows recording the oscillating vocal folds (100Hz - 300Hz) in real time during phonation (i.e. producing a single vowel). Therefore, it is especially useful to visualize and to quantify pathologies, which affect only the dynamic behavior of the vocal folds (i.e. hoarseness) but not the anatomical structure. The basis for objective laryngeal dynamics analysis are the recently developed solid image processing techniques enabling the segmentation of the vocal fold edges within the high-speed movies. For objective evaluation of laryngeal dynamics, several approaches have been suggested. The most common approaches are to evaluate the dynamics of single trajectories or the entire 2D-dynamics (Phonovibrography) directly by linear or non-linear analysis. Also, biomechanical models of the vocal folds are adapted or optimized towards extracted vocal fold movements for classification of voice pathologies. Acoustic recordings in combination with the corresponding high-speed sequences were applied to gain information about occurring dependencies. Using laser projection systems provided the quantification of vocal fold length and vocal fold vibrations in metric units during phonation. Due to high-speed recordings, relations between vocal fold vibrations and associated transglottal airflow could be associated. High-speed imaging was also performed for the investigation of the dynamics of the neoglottis in laryngectomees. It substantially enhanced the understanding of the vibrations of the neoglottis and provided more information than the commonly used videofluoroscopy.

Pseudogenes are homologous relatives of known genes that have lost their ability to function as a transcriptional unit. Three classes of pseudogenes are known to exist: duplicated pseudogenes; processed or retrotransposed pseudogenes; and unitary or disabled pseudogenes. Since pseudogenes may display a number of the characteristics of functional genes, they pose a unique set of problems for ab initio gene prediction. The ability to detect and differentiate pseudogenes from functional genes can be a difficult task. We present a comprehensive review of current approaches for pseudogene detection, highlighting difficulties in pseudogene differentiation.

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by large-scale genome sequencing projects. Prediction of protein subcellular localization is now a hot topic in bioinformatics community, which has been extensively studied in the past several years. Many computational methods have been proposed by the investigators, but they are still far from the final frontier. Among these methods, except for the prediction algorithms, the main factor influencing the prediction performance of various methods is the techniques used to extract features for characterizing proteins, i.e. the protein encoding schemes. To enhance the prediction performance of existing methods, many different approaches have been taken towards developing efficient and accurate methods for protein subcellular localization prediction, ranging from sorting signal based systems to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of their amino acid sequences. This review describes the inherent difficulties in developing a protein subcellular localization method and includes feature extraction techniques previously employed in this area. It is anticipated to serve as a guide for readers working in this field.

The automatic classification of proteins into groups is one of the major objectives for mining the increasing amount of data released by genomic and metagenomic sequencing projects. Ortholog and in-paralog accurate classification is motivated by the notion of descriptive biology. Facing the tremendous quantity of very complex protein datasets, one way to understand biological function, structure conservation as well as evolution history is to associate or group them into classes according to their sequence homology, function, folding motifs and structural features. In this review, we will explore and compare the different approaches and databases of automatic clustering and classification developed in the last years. We will also discuss the impact of hierarchies and clusters of proteins to protein function and phylogeny predictions.

miRNAs are a new class of non-coding small RNAs that exist in many species. They play important roles in many physiological and pathophysiological processes by inhibiting the expression of target RNAs. Recent advances in miRNAs are beginning to be predicted and identified using several technological approaches, such as miRNA cloning, hybridization with various probes, and PCR-based detection. In the past few years, miRNA microarray technology has become the reference technique for monitoring the miRNA expression. In this review, miRNAs will be introduced and the characteristics of normalization methods in miRNA research will be discussed. The technical operations of miRNA profiling with microarrays will also be described, with an emphasis on probe design and labelling. The applications of the miRNA microarray in both basic and applied research will be summarized.

Signal transduction systems are known to widely regulate complex biological events such as cell proliferation and differentiation. Although numerous biological analyses have revealed many of the key molecules and events involved in cell signaling, an integrative view of this complicated system cannot provide a fundamental theory on the regulation of the entire network without analyzing the dynamic behavior of these molecules and events at the system level. Recent technological advances in mass spectrometry-based proteomics and bioinformatics have enabled us to obtain a networkwide description of signaling dynamics through the large-scale identification and quantification of phosphorylated molecules. Accordingly, computational modeling on the basis of dynamic proteomics data has also been applied to the network analysis of representative signaling systems such as the epidermal growth factor receptor pathway. This review focuses on the current status of quantitative proteomics technology for temporal studies of signal transduction and on the application of comprehensive signaling dynamics data to mathematical analyses of regulatory networks. The perspective on proteomics data-driven systems biology is also discussed.

Bioinformatics is a field of information technology concerning the storage, retrieval, analysis, visualization, prediction and analysis of sets of data with biological or clinical significance. Tremor is a common movement disorder, for which pharmacological and neurophysiological models have been developed these last 3 decades, and which is at the frontier of biology, health sciences and computer technologies. Recently, new biomechanical modelling approaches of tremor have been proposed, based upon ambulatory systems and body area networks (BAN). Use of digital signal processing (DSP) techniques taking into account the non-linearity and non stationarity features of tremor time-series is reviewed in the present article. In particular, algorithms for instantaneous assessments of oscillations and direct online cancellations have been suggested. We discuss the advantages and drawbacks of the tremor detection algorithms, as well as prediction tools. In addition, promising models based upon neural networks, conductance studies and brain neurotransmitters are under development. These models will allow the accurate simulation of the behaviour of limbs. Their impact is outlined. The field of tremor research represents an excellent application of bioinformatics in medicine and rehabilitation.