During the past two decades, reductionist biological science has generated new
empirical data on the molecular foundations of biological structure and function
at an accelerating rate. The list of organisms whose complete genomes have been
sequenced is growing by the week. Annotations of these sequences are becoming
more comprehensive, and databases of protein structure are growing at impressive,
indeed formerly unimaginable rates.
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.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation
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.
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.
Harrison's Internal Medicine Chapter 66. Stem Cell Biology
Stem Cell Biology: Introduction
Stem cell biology is a relatively new field that explores the characteristics and possible clinical applications of the different types of pluripotential cells that serve as the progenitors of more differentiated cell types. In addition to potential therapeutic applications (Chap. 67), patient-derived stem cells can also provide disease models and a means to test drug effectiveness.
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.
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
Understanding and quantitative describing of marine ecosystems requires an integration
of physics, chemistry and biology. The coupling between physics, which
regulates for example nutrient availability and the physical position of many organisms
is particularly important and thus cannot be described by biology alone.
Therefore the appropriate basis for theoretical investigations of marine systems are
coupled models, which integrate physical, chemical and biological interactions.
This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine.
In the past 15 years, molecular biologists and geneticists have uncovered
some of the most basic mechanisms by means of which normal stem cells
in a certain organ or tissue develop into cancerous tumors. This biological
knowledge serves as a basis for various models of carcinogenesis.
Physicists pretend not only to know everything, but also to know everything bet-
ter. This applies in particular to computational statistical physicists like US. Thus
many of our colleagues have applied their computer simulation techniques to
ﬁelds outside of physics, and have published sometimes in biological, economic
or sociological journals, and publication ﬂow in the opposite direction has also
This book presents a full spectrum of views on current approaches to modeling cell mechanics. The authors of this book come from the biophysics, bioengineering, and physical chemistry communities and each joins the discussion with a unique perspective on biological systems. Consequently, the approaches range from ﬁnite element methods commonly used in continuum mechanics to models of the cytoskeleton as a cross-linked polymer network to models of glassy materials and gels.
Mathematical Modeling I – preliminary is designed for undergraduate students. Two other followup
books, Mathematical Modeling II – advanced and Mathematical Modeling III – case studies in biology,
will be published. II and III will be designed for both graduate students and undergraduate students.
All the three books are independent and useful for study and application of mathematical modeling in
What makes populations stabilize? What makes them fluctuate? Are populations in complex ecosystems more stable than populations in simple ecosystems? In 1973, Robert May addressed these questions in this classic book. May investigated the mathematical roots of population dynamics and argued-counter to most current biological thinking-that complex ecosystems in themselves do not lead to population stability.
This book is about how to construct and use computational models of specific
parts of the nervous system, such as a neuron, a part of a neuron or a
network of neurons. It is designed to be read by people from a wide range of
backgrounds from the biological, physical and computational sciences. The
word ‘model’ can mean different things in different disciplines, and even researchers
in the same field may disagree on the nuances of its meaning.
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.
Systems biology has received an ever increasing interest during the last
decade. A large amount of third-party funding is spent on this topic, which
involves quantitative experimentation integrated with computational
modeling. Industrial companies are also starting to use this approach more
and more often, especially in pharmaceutical research and biotechnology.
This book, with its 16 chapters, documents the present state of knowledge of the
adenosine A3 receptor. It covers a wide range of information, including data from
studies of theoretical, molecular and cellular pharmacology, signal transduction,
integrative physiology, new drug discoveries and clinical applications. It fills an
important gap in the literature since no alternative source of such information is