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Current
Bioinformatics
ISSN: 1574-8936
Current Bioinformatics
Volume 4, Number 2, May 2009
Contents
Biophysical Model of Sinoatrial Node’s Bioelectrical
Activity to Simulate Heart Rate Variability in Normal and
Diabetic Patients Pp. 88-100
Dhanjoo N. Ghista, Roustem Miftahof, Rajendra
U. Acharya and Kamlakar Desai
[Abstract] [Purchase
Issue/Articles]
The Next Step in Voice Assessment: High-Speed
Digital Endoscopy and Objective Evaluation Pp.
101-111
Michael Doellinger
[Abstract] [Purchase
Issue/Articles]
Current Trends in Pseudogene Detection
and Characterization Pp. 112-119
Eric Christian Rouchka and I.
Elizabeth Cha
[Abstract] [Purchase
Issue/Articles]
Feature Extraction Techniques for Protein
Subcellular Localization Prediction Pp.
120-128
Qing-Bin Gao, Zhi-Chao Jin, Cheng Wu, Ya-Lin
Sun, Jia He and Xiang He
[Abstract] [Purchase
Issue/Articles]
Computer-Assisted Automatic Classifications,
Storage, Queries and Functional Assignments of Orthologs and
In-Paralogs Proteins Pp. 129-140
David Thybert, Stéphane Avner, Céline
Lucchetti-Miganeh and Frédérique Barloy-Hubler
[Abstract] [Purchase
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miRNA Microarray Technology in miRNA
Profiling Pp. 141-148
Shu-Ting Wang, Cai Li and Lei
Liu
[Abstract] [Purchase
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Comprehensive Description of Signal Transduction
Networks by Quantitative Proteomics and Systems Biology
Pp. 149-153
Masaaki Oyama
[Abstract] [Purchase
Issue/Articles]
Bioinformatic Approaches Used in Modelling
Human Tremor Pp. 154-172
Manto Mario, Grimaldi Giuliana, Lorivel
Thomas, Farina Dario, Popovic Lana, Conforte Silvia, D’Alessio
Tommaso, Belda-Lois Juan-Manual, Pons Jose-Luis and Rocon
Eduardo
[Abstract] [Purchase
Issue/Articles]
Abstracts
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Biophysical Model of Sinoatrial Node’s Bioelectrical
Activity to Simulate Heart Rate Variability in Normal and
Diabetic Patients
Dhanjoo N. Ghista, Roustem Miftahof, Rajendra
U. Acharya and Kamlakar Desai
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 (Nα+)
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 anti-diabetic drug
therapy.
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The Next Step in Voice Assessment: High-Speed Digital Endoscopy
and Objective Evaluation
Michael Doellinger
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.
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Current Trends in Pseudogene Detection and Characterization
Eric Christian Rouchka and I.
Elizabeth Cha
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.
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Feature Extraction Techniques for Protein Subcellular Localization
Prediction
Qing-Bin Gao, Zhi-Chao Jin, Cheng Wu, Ya-Lin
Sun, Jia He and Xiang He
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.
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Computer-Assisted Automatic Classifications, Storage, Queries
and Functional Assignments of Orthologs and In-Paralogs Proteins
David Thybert, Stéphane Avner, Céline
Lucchetti-Miganeh and Frédérique Barloy-Hubler
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.
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miRNA Microarray Technology in miRNA Profiling
Shu-Ting Wang, Cai Li and Lei
Liu
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.
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Comprehensive Description of Signal Transduction Networks
by Quantitative Proteomics and Systems Biology
Masaaki Oyama
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 network-wide 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.
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Bioinformatic Approaches Used in Modelling Human Tremor
Manto Mario, Grimaldi Giuliana, Lorivel
Thomas, Farina Dario, Popovic Lana, Conforte Silvia, D’Alessio
Tommaso, Belda-Lois Juan-Manual, Pons Jose-Luis and Rocon
Eduardo
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.
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