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Current
Bioinformatics
ISSN: 1574-8936
Current Bioinformatics
Volume 3, Number 3, September 2008
Contents
On the Potential for Integrating Gene Expression and
Metabolic Flux Data Pp. 142-148
Meric A. Ovacik and Ioannis P. Androulakis
[Abstract] [Full
Text Article]
Computational Methods for the Prediction of the
Structure and Interactions of Coiled-Coil Peptides Pp.
149-161
Neha S. Gandhi and Ricardo L. Mancera
[Abstract] [Full
Text Article]
Computational Prediction of RNA Editing Sites:
Successes and Challenges Ahead Pp. 162-177
Shuba Gopal
[Abstract]
[Full Text Article]
Beyond the HapMap Genotypic Data: Prospects of
Deep Resequencing Projects Pp. 178-182
Wei Zhang and M. Eileen Dolan
[Abstract] [Full
Text Article]
Sequence-Based Methods for Real Value Predictions of Protein
Structure Pp. 183-196
Lukasz Kurgan, Krzysztof Cios, Hua Zhang, Tuo Zhang, Ke
Chen, Shiyi Shen and Jishou Ruan
[Abstract]
[Full Text Article]
A Review of Systems Biology Perspective on Genetic
Regulatory Networks with Examples Pp. 197-225
Don Kulasiri, Lan K. Nguyen, Sandhya Samarasinghe and
Zhi Xie
[Abstract] [Full
Text Article]
Abstracts
[Back to top]
[Full Text Article]
On the Potential for Integrating Gene Expression and
Metabolic Flux Data
Meric A. Ovacik and Ioannis P. Androulakis
Computational strategies, that integrate genomic-level
information and metabolic flux data, have improved both prediction
of metabolic fluxes and metabolic network identification.
Due to the tight interplay between hierarchical (transcriptional)
and metabolic control it is not clear how changes in gene
expression drive changes in cellular phenotypes manifested
through changes in metabolic fluxes. This raises the questions
to what extent a change in a metabolic flux should be attributed
to changes in gene expression and/or changes in metabolite
concentrations and what kind of conclusions can be drawn by
comparing gene expression profiles with the associated metabolic
fluxes. This review addresses issues related to modeling approaches
that attempt to integrate gene expression and metabolic flux
data.
[Back to top] [Full
Text Article]
Computational Methods for the Prediction of the Structure
and Interactions of Coiled-Coil Peptides
Neha S. Gandhi and Ricardo L. Mancera
The past several years have seen significant advances
in the development of computational methods for the prediction
of the structure and interactions of coiled-coil peptides.
These methods are generally based on pairwise correlations
of amino acids, helical propensity, thermal melts and the
energetics of sidechain interactions, as well as statistical
patterns based on Hidden Markov Model (HMM) and Support Vector
Machine (SVM) techniques. These methods are complemented by
a number of public databases that contain sequences, motifs,
domains and other details of coiled-coil structures identified
by various algorithms. Some of these computational methods
have been developed to make predictions of coiled-coil structure
on the basis of sequence information; however, structural
predictions of the oligomerisation state of these peptides
still remains largely an open question due to the dynamic
behaviour of these molecules. This review focuses on existing
in silico methods for the prediction of coiled-coil
peptides of functional importance using sequence and/or three-dimensional
structural data.
[Back to top] [Full
Text Article]
Computational Prediction of RNA Editing Sites: Successes and
Challenges Ahead
Shuba Gopal
RNA editing is an unusual phenomenon in which RNA transcripts
are modified so that they no longer match their original,
genomic template. The process occurs in a wide variety of
eukaryotes from plant mitochondria to humans. Recent advances
in computational and experimental methods have made it possible
to survey entire genomes for candidate edit sites. Such efforts
have yielded many promising results while raising tantalizing
new questions. Key features of extant methods and the open
questions and issues that have been raised by these efforts
are highlighted in this review.
[Back to top] [Full
Text Article]
Beyond the HapMap Genotypic Data: Prospects of Deep Resequencing
Projects
Wei Zhang and M. Eileen Dolan
The International HapMap Project provides a key resource
of genotypic data on human samples including lymphoblastoid
cell lines derived from individuals of four major world populations
of African, European, Japanese and Chinese ancestry. Researchers
have utilized this resource to identify genetic elements that
correlate with various phenotypes such as risks of common
diseases, individual drug response and gene expression variation.
However, recent comparative studies have suggested that the
currently available HapMap genotypic data may not capture
a substantial proportion of rare or untyped SNPs in these
populations, implying that the HapMap SNPs may not be sufficient
for comprehensive association studies. In this paper, three
large-scale deep resequencing projects covering the HapMap
samples: ENCODE (Encyclopedia of DNA Elements), SeattleSNPs
and NIEHS (National Institute of Environmental Health Sciences)
Environmental Genome Project are discussed. Prospectively,
once integrated with the HapMap resource, these efforts will
greatly benefit the next wave of association studies and data
mining using these cell lines.
[Back to top] [Full
Text Article]
Sequence-Based Methods for Real Value Predictions of Protein
Structure
Lukasz Kurgan, Krzysztof Cios, Hua Zhang, Tuo Zhang, Ke
Chen, Shiyi Shen and Jishou Ruan
Recent years observed a growing interest in computational
methods that predict and characterize protein structure due
to the increasing sequence-structure gap. This includes a
spike in development of sequence-based in-silico methods that
address prediction of several newly formulated real-value
descriptors of protein structure. These descriptors include
B-factor, backbone torsion angles, solvent accessibility,
residue depth, contact number, residue-wise contact order,
secondary structure content, and folding rates. Although they
address different structural aspects, such as exposure to
the solvent, spatial position and packing of the residues,
their flexibility, amount of secondary structures in the protein,
and folding time, the methods that are built to address them
share similarities that could be exploited to improve future
designs. To date, no comprehensive overview that summarizes
and contrasts solutions developed for these tasks was published.
To address this we compare different designs of real-value
predictors based on information concerning input data encoding
and prediction algorithms used. We also investigate evaluation
standards, which include benchmark datasets, test criteria,
and test procedures used in these predictive tasks. Finally,
we summarize application areas and problems that use the above-mentioned
predictions. We believe that the breath and number of these
applications justify further development of more accurate
and integrated real-value prediction methods.
[Back to top]
[Full
Text Article]
A Review of Systems Biology Perspective on Genetic Regulatory
Networks with Examples
Don Kulasiri, Lan K. Nguyen, Sandhya Samarasinghe and
Zhi Xie
We present an extensive review of genetic regulatory
networks (GRNs) from a system biology perspective, and discuss
pertinent research issues related to GRNs such as roles of
feedback loops, and internal and external noise. A succinct
review of mathematical modelling is also given as mathematical
modelling is central to understand the complex molecular living
systems. We discuss tryptophan production system in Escherichia
coli bacteria, as an example, to illustrate how feedback
loops originate, and finally we present a development of a
mathematical model of circadian rhythms of Drosophila
(fruit fly) as a comprehensive illustration of building a
system model from components. We attempt to integrate experimentally
acquired molecular biology knowledge in the model developments
and discussions to emphasise the importance of being true
to the data and insights in the experimental molecular biology.
The experimental knowledge and insights should drive the efforts
in development of mathematical and computational models of
living systems, not the other way around, and this is a challenging
task.
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