Despite the transformation in biological practice and theory brought about by discoveries in molecular biology, until recently philosophy of biology continued to focus on evolutionary biology. When the Human Genome Project got underway in the late 1980s and early 1990s, philosophers of biology—unlike historians and social scientists—had little to add to the debate.
This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.
Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information.
The advent of ever more sophisticated molecular manipulation techniques has made it clear that cellular systems are far more complex and dynamic than previously thought. At the same time, experimental techniques are providing an almost overwhelming amount of new data. It is increasingly apparent that linking molecular and cellular structure to function will require the use of new computational tools.
Scientific and technological advances now allow us to manipulate genomes directly at the level of single genes and their constituents, with a speed and precision that far exceed what natural evolution has been able to achieve over the past 3.5 billion years. We already have in vitro fertilization and animal cloning; in the future human cloning and the exploitation of embryonic stem cells, among other capabilities, may be routine.
As exciting as the new field of genomics is, it has not yet produced a basic conceptual change in biology. The fundamental problems remain: the origin of life, cell organization, the pathways of differentiation, aging, and the molecular and cellular capabilities of the brain. What has occurred is an explosion of molecular information obtained by genomic sequences, which will soon be followed by exhaustive catalogs of protein interactions and protein function. This wealth of information can be analyzed and manipulated only with the help of computers.
Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments.
In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple manner. This makes the material accessible to computer scientists without biological training, as well as to biologists with limited background in computer science.
What arouses an animal or human from an inactive, nonresponsive state to a condition of activity and responsiveness? What are the biological mechanisms for this change? In this book Donald W. Pfaff focuses on a reproductive behavior typical of many female animals. Sensory stimuli from the male trigger responses in a well-defined circuit of nerve cells. At the top of the circuit, certain nerve cells receive and retain sex hormones such as estrogens and progesterone.