The Brazilian Symposium on Bioinformatics (BSB) 2008 was held at Santo
Andr´e (S˜ao Paulo), Brazil, August 28–30, 2008. BSB 2008 was the third symposium
in the BSB series, although BSB was preceded by the Brazilian Workshop
on Bioinformatics (WOB). This previous event had three consecutive editions in
2002 (Gramado, Rio Grande do Sul), 2003 (Maca´e, Rio de Janeiro), and 2004
(Bras´ılia, Distrito Federal). The change from workshop to symposium reflects
the increasing quality and interest behind this meeting.
Bioinformatics ? Biology and Computers ? What do they have to do with each other?
I suppose that this question could have been raised even in 19th century when
technologies of computers and biology were just emerging. At one city in France the
great Louis Pasteur (1822-1895) was studying how fermentation of alcohol was
linked to the existence of a specific microorganism. In another city in England,
equally great Charles Babbage (1791-1871) was oiling his Analytical Engine in which
Ada Lovelace, a mathematician who understood Babbage's vision, was trying to
calculate the Bernoulli numbers.
Immediately after the first drafts of the human genome sequence were reported almost
a decade ago, the importance of genomics and functional genomics studies became
well recognized across the broad disciplines of biological sciences research.
Nowadays it is difficult to imagine an area of knowledge that can continue developing
without the use of computers and informatics. It is not different with biology, that has
seen an unpredictable growth in recent decades, with the rise of a new discipline,
bioinformatics, bringing together molecular biology, biotechnology and information
Computational biology is undergoing a revolution from a traditionally compute-intensive science conducted by individuals and small research groups to a high-throughput, datadriven science conducted by teams working in both academia and industry. It is this new biology as a data-driven science in the era of Grid Computing that is the subject of this chapter.
Bioinformatics is an interdisciplinary field which addresses biological problems using computational techniques, and makes the rapid organization and analysis of biological data possible. The field may also be referred to as computational biology, and can be defined as, "conceptualizing biology in terms of molecules and then applying informatics techniques to understand and organize the information associated with these molecules, on a large scale.
The handbook is written for mature readers with a great deal of rigorous
experience with either doing scientific research or writing scientific software.
However no specific background in computer science, statistics, or biology is
required to understand most of the chapters. In order to accommodate the
readers who wish to become professional computational biologists in the
future we also provide appendices that contain educationally sound glossaries
of terms and descriptions of major sequence analysis algorithms.
In this thesis we are concerned with constructing algorithms that address prob-
lems of biological relevance. This activity is part of a broader interdisciplinary
area called computational biology, or bioinformatics, that focuses on utiliz-
ing the capacities of computers to gain knowledge from biological data. The
majority of problems in computational biology relate to molecular or evolu-
tionary biology, and focus on analyzing and comparing the genetic material of
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Bioinformatics and Computational Biology, University of North Carolina...
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Universitá degli Studi di Milano, 20133 Milan, Italy. ‡Computational Biology Center, Memorial Sloan...
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health ...
As you worked your way through high school, or otherwise worked to prepare
yourself for college, you were probably unaware that an information
explosion was taking place in the field of biology. This explosion, brought on
by advances in biotechnology and communicated by faster, more powerful
computers, has allowed scientists to gather data more quickly and disseminate
data to colleagues in the global scientific community with the click of a
The public demand for the protection of human and environmental
health has led to the establishment of toxicology as
the science of the action of chemicals on biological systems.
Toxicological research is focused presently very much on the
elucidation of the cellular and molecular mechanisms of toxicity
and the application of this knowledge in safety evaluation
and risk assessment.
Computational chemistry and molecular modeling is a fast emerging area which is used for the modeling and simulation of small chemical and biological systems in order to understand and predict their behavior at the molecular level. It has a wide range of applications in various disciplines of engineering sciences, such as materials science, chemical engineering, biomedical engineering, etc.
Biological signal analysis1 encompasses several interdisciplinary topics that deal with analysing
signals generated by various physiological processes in the human body. These signals could be
electrical, chemical or acoustic in origin and an analysis of these signals are often useful in
explaining and/or identifying pathological conditions of the human body. However, these signals in
their rawest form do not provide much information and therefore, the motivation behind biological
signal analysis is to extract (i.e. to reveal) the relevant information.
This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data.