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tists and technology-savvy entrepreneurs that they produce. According to the National Science Board, the academic research and development infra structure in the United States in 2002 included over 700 institutions of higher education, over 200 federal laboratories, and hundreds of nonpro t research institutions. This chapter continues the discussion of the infrastructure that is en abling the evolution of the biotech industry, with emphasis on the infra structure in the United States (the infrastructure issues associated with other countries are explored in 7, Regional Outlook ). It con siders issues such as the availability of patent protection for pharmaceu ticals and the tools used to develop them, the effect of migration of expertise from education centers to potentially more lucrative areas in developing economies, and the effect of public perception on the growth of the biotech market.
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INFORMATION TECHNOLOGY INFRASTRUCTURE
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For many information technology (IT) professionals, biotech is viewed as their life preserver in a market that crashed in 2000. For several years fol lowing the crash, virtually all programmers and other IT professionals in California s Silicon Valley faced a year or more delay in nding employ ment. Even outspoken leaders in the computer industry, such as Oracle CEO Larry Ellison, contend that the IT market in the United States has matured, and that growth seen in the late 1990s lies in elds such as biotech. In support of this prediction, many of the larger computer compa nies based in the United States and Europe are rushing into the Asian mar ket before it too becomes saturated with computers and computer technology. Smaller companies are hiring lower-paid offshore workers to compete in an industry with increasingly thin pro t margins. Whether the worldwide information technology industry ever regains the position it achieved just prior to the bubble of 2000, the gains the in dustry has made in standards and computing power in the 1990s, and the penetration of computing methods into higher education, the sciences, and engineering have had a enabling effect on business worldwide. The appli cation of information technology in the biotech industry is helping to rede ne the pharmaceutical research and development process through IT s knowledge management practices and its validation tools for better rsttime compliance with FDA rules and regulations. The discussion here illus trates how an information technology infrastructure enables modern knowledge management practices and how some challenges to its potential productive power still remains.
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An information technology infrastructure supports the biotech knowledge management process by providing computation, communications, control, and data storage. These provisions can be appreciated in the context of the knowledge management process associated with the pharmacogenomic lab oratory depicted in Figure 5.1. The gure shows data-collection, databases, and analysis tools, all networked to support the knowledge management process. The data serve as the basis of online publication in applications from drug discovery to genetic engineering. The amount of data owing through the lab is astronomical. There are numerous data sources, including the patients, clinical studies, ge nomic studies, and public and private online databases. Data of various types are acquired from a variety of sources, incorporated into databases, manipulated, transformed, and archived for future use. In addition, there are a variety of applications that can be brought to bear on genomic and
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FIGURE 5.1 The information technology (IT) infrastructure in an ideal
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pharmacogenomic laboratory environment, showing clinical and laboratory datacollection, databases, and analysis tools, all networked to support the knowledge management process.
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clinical data, and the biomedical literature, which help researchers gener ate more data. Computational tasks enabled by the IT infrastructure range from searching for nucleotide sequences and visualizing protein-folding pat terns to simulating complex protein-protein interactions. These tasks are undertaken for applications ranging from drug discovery to biomaterials research and development. Furthermore, embedded computer controllers operate sequencing machines, fermentation tanks, bioreactors, and the robotic arms that perform repetitive tasks in the lab. Celera Genomics was able to sequence the human genome in part because of the computerdirected robots that automated the tedious gene-sequencing process that generated a quarter trillion genomic sequences every hour. As a communications medium, modern computer networks enable re searchers to collaborate directly with each other and to publish their re search online and later in print. Most biotech researchers consult the numerous public databases on the Web or one of the value-added com mercial databases for the latest information on biotech research. Not only are articles in print typically considered historical documents, but if a printed journal article describing the research isn t referenced by one of the online electronic reference databases (such as the U.S. National Li brary of Medicine s PubMed), then the chances of the printed article ever being read are minimal. Computation, communications, and control all revolve around data maintained in computer databases, including the hundreds of public biotech databases accessible through the Internet. International genomic sequence databases include GENBANK, which is supported by the Na tional Center for Biological Information (NCBI), the DNA DataBank of Japan (DDBJ), and the European Molecular Biology Laboratory (EMBL). PubMed, which is maintained by the U.S. National Library of Medicine, is a key resource for international biomedical literature. In addition to the public databases, there are a rapidly increasing number of private data bases created and maintained by for-pro t companies such as Incyte Ge nomics, Inc. and Celera Genomics that offer databases designed to help researchers identify and prioritize potential drug targets. Whether biotech databases are private or public, they are characterized by the enormity of their contents. To the delight of the sagging post-eCommerce information technology industry, the data-handling requirements asso ciated with even modest biological databases often necessitate considerable investment in computers, storage devices, network, and other components of an information technology infrastructure. Consider that as of mid-2003, GenBank, the repository of nucleotide sequences for a variety of species that forms the basis for much bioinformatics research, contained data on over 17 billion base pairs stored in over 15 million sequence records. Many compa
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