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Biology for Students: Bioinformatics

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Biology for Students: Bioinformatics Biology for Students: Bioinformatics The Human Genome Project (HGP) is an international effort to determine the biochemical code for the entire human genome – the base sequences of the 100,000 or so genes and their regulatory elements that serve as the blueprint of human biology. The HGP has generated massive data on genome sequences, disease genes, protein 3-dimensional structures and functions, protein-protein interactions, and gene regulation. Bioinformatics is a new field centered on the development and application of computational methods to organize, integrate and analyze these data. It is closely tied to two other nascent fields – genomics (identification and functional characterization of genes in a massively parallel and high throughput fashion) and proteomics (analysis of the biological functions of gene products and their interactions), which have also resulted from the HGP. The HGP will have major impacts on understanding evolution and developmental biology, and our ability to diagnose and treat diseases. Its impact is also being felt in areas outside those of traditional biology, such as anthropology and forensic medicine. In 1985, a group of visionary scientists led by Charles DeLisi, who was then the director of the office of health and environmental research at the U.S. Department of Energy (DOE), realized that having the entire human genome in hand would provide the foundation for a revolution in biology and medicine (DeLisi, 1988, p.488). As a result, the 1988 presidential budget submission to Congress requested funds to start the Human Genome Project. Momentum built quickly and by 1990 DOE and the U.S. National Institutes of Health (NIH) had coordinated their plans for a fifteen-year project (Collins and Gallas, 1993, p.43). In 1995 an article by Craig Venter, Nobel Prize winner Hamilton Smith, and thirty-eight other researchers, described the completion of the entire genome of Haemophilus influenzae, a bacterium that causes ear infections and meningitis (Fleischmann et al, 1995, p. 496). For the first time, the genetic secrets of a free-living organism became an open book. Today, five years later, the genomes of more than 30 organisms have been completely sequenced, with another 100 or so in progress (see http://www.tigr.org for a list). A rough draft of the human genome will be announced on June 26, 2000. We are the first generation bestowed with the “parts list” of life, as well as the daunting task of making sense out of it (Begley, 2000, p. 50). Knowing the sequence of the billions of bases in the human genome does not tell us where the genes are (less than 10% of the human genome encode for genes), what the genes do, how genes are regulated, how gene products form a cell, how cells form organs, which mutations underlie genetic diseases, why we age, and how to develop drugs. Bioinformatics, genomics, and proteomics try to answer these questions using technologies that take advantage of as much gene sequence information as possible. In particular, Bioinformatics focuses on computational approaches. One aspect of Bioinformatics involves the development of databases and computational algorithms for the storage, dissemination and rapid retrieval of genomic data. Biological data are complex and in large quantity. For example, the U.S. National Center for Biotechnology Information (NCBI), a division of NIH, houses central databases for gene sequence (GenBank), disease association (OMIM), protein structure (MMDB), and published biomedical articles (PubMed). The best way to get a feeling for the magnitude and variety of the data is to access the homepage of NCBI via the world-wide-web (http://ncbi.nlm.nih.gov). A whole Bioinformatics team at NCBI works on the design of the databases and the development of efficient algorithms for retrieving data. Another aspect of Bioinformatics covers the design of genomics and proteomics experiments and subsequent analysis of the results. Disease tissues (such as those from cancer patients) express different sets of proteins than their normal counterparts. Therefore protein abundance can be used to diagnose diseases. Moreover, proteins that are highly (or uniquely) expressed in disease tissues are potential drug targets. Genomics and Proteomics generate protein abundance data using different approaches. Genomics determine gene abundance (which is often a good indicator for protein abundance) using “gene chips”, which are high-density arrays of “gene fragments” each recognizing a particular gene. By hybridizing a tissue sample to a gene chip, one can determine the activities of all genes in a single experiment. The design of gene chips, i.e., which “gene fragments” to use in order to achieve maximum sensitivity and specificity, as well as how to interpret the results of gene chip experiments, are difficult problems in Bioinformatics. Proteomics measures protein abundance directly using mass spectroscopy, which is a way to measure the mass of a protein. Since mass is not unique enough for identifying a protein, one usually cuts the protein with enzymes (which cut at specific places according to the protein sequence) and measures the masses of the resulting fragments using mass spectroscopy. Such “mass distributions” for all proteins with known sequences can be generated and stored using computers. By comparing the mass distribution of an unknown protein sample to those of known proteins, one can quickly reveal the identity of the sample. Such comparison requires sophisticated computational algorithms, especially when the sample is a mixture of proteins. Although not as efficient as gene chips, mass spectroscopy can make direct measurement of protein abundance. In fact, spectrometric identification of protein identity has been the one of the most significant breakthroughs in proteomics. One of the most exciting aspects of Bioinformatics includes hypothesis-driven research to make new discoveries. When the cystic fibrosis gene (CF) was first identified in 1989, researchers compared its sequence computationally to all sequences known at that time. The comparison revealed striking homology to a large family of proteins involved in active transport across cell membranes. Indeed, the CF gene codes for a membrane spanning chloride ion channel (Collins, 1992, p.774). The identification of gene function by searching for sequence homology is a widely used Bioinformatics method. When no homology is found, one may still be able to tell if a gene codes for membrane spanning channels using computational tools. Membranes are layers of lipid molecules, which are water insoluble. An ion channel typically has regions outside the membrane (water soluble) and regions inside the membrane (water insoluble) arranged in a certain pattern. Computer algorithms have been developed to capture such patterns in a gene sequence. By thinking boldly and by setting ambitious goals, the Human Genome Project has brought about a new era in biological and biomedical research. Many revolutionarily new technologies are being developed, most of which have significant computational components. The avalanche of genomic data also enables model-based reasoning. The bright future of Bioinformatics calls for individuals who can think quantitatively and in the meantime love biology, a rather unique combination (Marshall, 1996, p. 1730; DeLisi, 1997, p. 819). Word Count: 1121 Reference: Begley, Sharon. “The Race to Decode the Human Body.” News Week, April 10, 2000, 50-57. Collins, Francis. “Cystic Fibrosis: Molecular Biology and Therapeutic Implications.” Science 256 (1992): 774-779. Collins, Francis and Gallas, David. “A New Five-Year Plan for the U.S. Human Genome Project.” Science 262 (1993): 43-46. DeLisi, Charles. “The Human Genome Project.” American Scientist 76 (1988): 488-493. DeLisi, Charles, Cantor, Charles and Weng, Zhiping. “Hedgehogs, foxes, and a new science.” Nature Biotechnology 15(1997): 819. Marshall, Eliot. “Bioinformatics: Hot Property: Biologists Who Compute.” Science 272 (1996): 1730- 1732. (in News & Comment). Fleischmann, R. D. et al. “Whole-genome random sequencing and assembly of Haemophilus influenzae Rd Genome.” Science 269(1995): 496-512. Information on the Author: Zhiping Weng Department of Biomedical Engineering Boston University Boston, MA 02215 Email: [email protected] .
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