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Current
Bioinformatics
ISSN: 1574-8936
Current Bioinformatics
Volume 3, Number 2, May 2008
Contents
An Integrated Rule-Set for Protein Localization in
Plasmodium falciparum Pp. 66-73
Aditya Rao, Sri Jyothsna Yeleswarapu, Rajgopal
Srinivasan and Gopalakrishnan Bulusu
[Abstract]
Machine Learning Techniques for Protein Secondary
Structure Prediction: An Overview and Evaluation
Pp. 74-86
Paul D. Yoo, Bing Bing Zhou and Albert
Y. Zomaya
[Abstract]
Genome Annotation in Plants and Fungi: EuGène
as a Model Platform Pp. 87-97
Sylvain Foissac, Jérôme Gouzy, Stephane
Rombauts, Catherine Mathé, Joëlle Amselem, Lieven
Sterck, Yves Van de Peer, Pierre Rouzé and
Thomas Schiex
[Abstract]
Integrating Bioinformatics and Computational Biology:
Perspectives and Possibilities for In Silico Network
Reconstruction in Molecular Systems Biology Pp. 98-129
Rui Alves, Ester Vilaprinyo and Albert
Sorribas
[Abstract]
Computational Modeling Approaches for Studying of
Synthetic Biological Networks Pp. 130-141
Elizabeth Pham, Isaac Li and Kevin Truong
[Abstract]
Abstracts
[Back to top]
An Integrated Rule-Set for Protein Localization in
Plasmodium falciparum
Aditya Rao, Sri Jyothsna Yeleswarapu, Rajgopal
Srinivasan and Gopalakrishnan Bulusu
Proteins localize to many intracellular and extracellular
organelles in the malarial parasite Plasmodium falciparum.
Although organellar localization in the parasite has been
studied in detail, there is a need for a comprehensive rule-set
that captures the different paths and mechanisms used by proteins
for localization. This review is an attempt to build such
a rule-set through a combination of rules gleaned from literature
reports. A prototype localization prediction tool has also
been implemented by incorporating certain sequence/composition
rules from the rule-set.
[Back to top]
Machine Learning Techniques for Protein Secondary
Structure Prediction: An Overview and Evaluation
Paul D. Yoo, Bing Bing Zhou and Albert
Y. Zomaya
The prediction of protein secondary structures is not
only of great importance for many biological applications
but also regarded as an important stepping stone for solving
the mystery of how amino acid sequences fold into tertiary
structures. Recent research on secondary structure prediction
is mainly based on widely known machine learning techniques,
such as Artificial Neural Networks and Support Vector Machines.
The most significant breakthroughs were the incorporation
of new biological information into an efficient prediction
model and the development of new models which can efficiently
exploit suitable information from its primary sequence. Hence
this paper reviews the theoretical and experimental literature
of these models with a focus on informational issues involving
evolutionary and long-range information of protein sequences.
Furthermore, we investigate several key issues in protein
data processing which involve dimensionality reduction and
encoding schemes.
[Back to top]
Genome Annotation in Plants and Fungi: EuGène
as a Model Platform
Sylvain Foissac, Jérôme Gouzy, Stephane
Rombauts, Catherine Mathé, Joëlle Amselem, Lieven
Sterck, Yves Van de Peer, Pierre Rouzé and
Thomas Schiex
In this era of whole genome sequencing, reliable genome
annotations (identification of functional regions) are the
cornerstones for many subsequent analyses. Not only is careful
annotation important for studying the gene and gene family
content of a genome and its host, but also for wide scale
transcriptome and proteome analyses attempting to describe
a certain biological process or to get a global picture of
a cell's behavior. Although the number of sequenced genomes
is increasing thanks to the application of new technologies,
genome wide analyses will critically depend on the quality
of the genome annotations. However, the annotation process
is more complicated in the plant field than in the animal
field because of the limited funding that leads to much fewer
experimental data and less annotation expertise. This situation
calls for highly automated annotation platforms that can make
the best use of all available data, experimental or not. We
discuss how the gene prediction (the process of predicting
protein gene structures in genomic sequences) research field
increasingly shifts from methods that typically exploited
one or two types of data to more integrative approaches that
simultaneously deal with various experimental, statistical,
or other in silico evidence. We illustrate the importance
of integrative approaches for producing high quality automatic
annotations of genomes of plants and algae as well as of fungi
that live in close association with plants using the platform
EuGène as an example.
[Back to top]
Integrating Bioinformatics and Computational Biology:
Perspectives and Possibilities for In Silico Network
Reconstruction in Molecular Systems Biology
Rui Alves, Ester Vilaprinyo and Albert
Sorribas
There is a flood of molecular data about many aspects
of cellular functioning. This data ranges from sequence and
structural data to gene and protein regulation data, including
time dependent changes in the concentration. Integration of
the different datasets through computational methods is required
to extract biological information that is relevant from a
systems biology perspective.
In this paper we discuss how different computational tools
and methods can be made to work together integrating different
types of data, mining these data for biological information,
and assisting in pathway reconstruction and biological hypotheses
generation. We review the recent body of literature where
such integrative approaches are used and discuss automation
of data integration and model building to generate testable
biological hypotheses. We analyze issues regarding the design
of such automated tools and discuss what limitations and pitfalls
can be foreseen for the automation and what solutions can
computer science and biologists provide to overcome them.
[Back to top]
Computational Modeling Approaches for Studying of
Synthetic Biological Networks
Elizabeth Pham, Isaac Li and Kevin Truong
Synthetic biology is an emerging field that strives to
build increasingly complex biological networks through the
integration of molecular biology and engineering. The growth
of the field has been supported by progress in the design
and construction of synthetic genetic and protein networks.
This has led to the possibility of assembling modular components
to attain novel biological functions and tools. In addition,
these synthetic networks give rise to insights that facilitate
the investigation of interactions and phenomena in naturally-occurring
networks. Integration of well-characterized biological components
into higher order networks requires computational modeling
approaches to rationally construct systems that are directed
towards a desired outcome. A computational approach would
improve the predictability of the underlying mechanisms that
would otherwise be difficult to deduce through experimentation
alone. The analysis and interpretation of both qualitative
and quantitative models also becomes increasingly important
towards taking a systems-level perspective on synthetic genetic
and protein networks. This review will first discuss the analogy
of synthetic networks to circuit engineering. It will then
look at computational modeling approaches that can be applied
to biological systems and how synthetic biology will help
to develop more accurate in silico representations
of these systems.
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