After studying this chapter, you should be able to:
Describe the distinguishing features of genomics, proteomics, and bioinformatics.
Recognize the potential and challenges presented by genome-guided personalized medicine.
Summarize the principal features and medical relevance of the Encode project.
Describe the functions served by HapMap, Entrez Gene, and the dbGAP databases.
Explain how BLAST and deciphering of the folding code assist scientists in the elucidation of the form and function of unknown or hypothetical proteins.
Describe the major features of computer-aided drug design and discovery.
Describe possible future applications of computational models of individual pathways and pathway networks.
Outline the possible medical utility of “virtual cells.”
The first scientific models of pathogenesis, such as Louis Pasteur’s seminal germ theory of disease, were binary in nature: each disease possessed a single, definable causal agent. Malaria was caused by amoeba of the genus Plasmodium, tuberculosis by the bacterium Mycobacterium tuberculosis, sickle cell disease by a mutation in a gene encoding one of the subunits of hemoglobin, poliomyelitis by poliovirus, and scurvy by a deficiency in ascorbic acid. The strategy for treating or preventing disease thus could be reduced to a straightforward process of tracing the causal agent, and then devising some means of eliminating it, neutralizing its effects, or blocking its route of transmission.
While simple models proved effective for understanding and treating a wide range of nutritional, infectious, and genetic diseases, efforts to identify discrete causal agents for diseases such as cancer, heart disease, obesity, type II diabetes, and Alzheimer’s disease have proved unavailing. The origins and progression of these latter diseases are multifactorial in nature, the product of the complex interplay between each individual’s genetic makeup, other inherited or epigenetic factors, and environmental factors such as diet, lifestyle, toxins, viruses, or bacteria. Unraveling these multidimensionally complex and subtly amorphous biomedical puzzles demands the acquisition and analysis of data on a scale that lies beyond the ability of human beings to collect, organize, and review unaided.
The term bioinformatics refers to the application of computer and robotics technology to automate the collection, retrieval, and analysis of scientific data on a mass scale. A major objective of many bioinformaticists is to develop algorithms capable of reliably predicting the three-dimensional structures and functional properties of the roughly one-third of all genetically-encoded proteins currently categorized as “unknown” or “hypothetical.” Another is to use information technology to increase the rapidity and effectiveness with which doctors can diagnose and treat patients by providing physicians with immediate access to critical information such as medical histories and drug interaction data. The goal of computational biology is to allow researchers to perform experiments in silico on digital virtual models of molecules, cells, organs, and organisms. These virtual models hold great promise for enhancing the pace and extending the scope of biomedical research by freeing scientists from the inherent material, economic, labor, temporal, and ethical constraints ...