A Tsunami of Data
When, in the early 1990s, the U.S. government-funded Human Genome Diversity Project (HGDP) drafted plans for a genetic database of some 4,000 to 8,000 distinct ethnic populations, it was met with a great deal of controversy and criticism. The stakes were raised even more when it was discovered that the HGDP had proposals for the patenting of the cell lines from several members of indigenous populations, all without those members' or communities' informed consent. Due to the interventions by such groups as the Rural Advancement Foundation International (RAFI), the HGDP was forced to drop three of its patents. In 1996 it provided a testimony to the U.S. National Research Council and has since drafted a document of "Model Ethical Protocols" for research, which emphasizes informed consent and cultural-ethical negotiation. Since that time, however, the HGDP has been conspicuously silent (it is now based at Stanford University, as the Morris Institute for Population Studies), and, despite the flurry of news items and press releases relating to the various genome mapping endeavors around the world--both government and corporate sponsored--there has been relatively no news or updates on the progress of the HGDP's original plans.
Much of this curious disappearing act has to do, certainly, with the bioethical conundrums in which the HGDP has been involved, as well as with the combination of vocal critics such as RAFI, and the HGDP's having been marked by the media and dubbed by its critics as "the vampire project." However, while the HGDP as an organization may have slipped from science headlines, the issues and problems associated with it have not. Another, parallel development within biotech and genetics has emerged, which has more or less taken up the "diversity problem" that the HGDP had dealt with in the 1990s: bioinformatics. Bioinformatics involves the use of computer and networking technologies in the organization of updated, networked, and interactive genomic databases being used by research institutions, the biotech industry, medical genetics, and the pharmaceutical industry. Bioinformatics signals an important development in the increasing computerization of "wet" biotech research, creating an abstract level where bioinformatics can form relationships between bioscientific approaches to diversity and the fluctuations of the biotech economy. A driving economic force is finance capital, bolstered from within by a wide range of "future promises" from biotech research (software-based gene discovery, data mining, genetic drugs, and so on). The emphasis we are witnessing now in "digital capitalism," to use Dan Schiller's term, is an intersection of economic systems with information technology. As Michael Dawson and John Bellamy Foster show, this trend leads to an emphasis on a "total marketing strategy" that is highly diversified: consumer profiling, individualized marketing, "narrowcasting," "push-media" and so on. Such trends are transforming biotech research as well. More often than not, the future of a research field within biotech can flourish or perish depending on the tides of stock values. In turn, those stock values are directly tied to the proclaimed successes or failures of clinical trials or research results. Most of the stock value of the biotech industry is an example of what Catherine Waldby calls "biovalue": either being able to produce valuable research results that can be transformed into products (such as genetic-based drugs or therapies), or the ability to take research and mobilize it within a product development pipeline (mostly within the domain of the pharmaceutical industry).
These trends are worth pointing out, because they draw our attention to the ways in which race, economics, and genomics are mediated by information technologies. Genomics--the technologically-assisted study of the total DNA, or genome, of organisms--currently commands a significant part of the biotech industry's attention. In economic as well as scientific terms, genomics has, for some years, promised to become the foundation upon which the possibility of a future medical genetics and pharmacogenomics would be based. As a way of providing a backdrop for Diane Ludin's project Harvesting the Net::MemoryFlesh, what I would like to do here is to outline some of the linkages between biotech as an increasingly corporate-managed field, and the emphasis within genomics programs on diversification. Such research programs, which highlight types of "genetic difference," demonstrate the extent to which culture and biology are often con-fused, as well as the extent to which both ethnicity and race are compelled to accommodate the structures of informatics.
The recent rise in genomics projects, especially those geared towards unique gene pools and genetic markers, has impelled the hybridization of a new practice of statistics and medicine, combining studies in population genetics with new techniques in genomic mapping. This application of sociobiological studies of populations--what I'll be calling "population genomics"--brings together a lengthy tradition in the study of populations, hereditary patterns, and inherited characteristics, with the contemporary development of large-scale genomic sequencing and analysis for clinical medicine. For instance, biotech companies such as deCODE, Myriad Genetics, and Oxford Biosciences Inc., are focusing on the genomes of populations with histories of low migration and a low frequency of genetic mixing (Icelandic, Mormon, and Newfoundland communities, respectively). Other companies, such as DNA Sciences Inc., are focusing on building a volunteer-based genetic health database to aid in the fight against disease. DNA Science's Gene Trust uses the GenBank model to archive medical, genetic, and health-related data (GenBank holds the public consortium's human genome data). Still other companies and research labs are focusing on the minute genetic sequence differences between individuals--polymorphisms, SNPs, and haplotypes--that may be the keys to individual genetic susceptibility to disease, and, by extension, the key to the development of genetic drugs.
What all of these databases promise to provide is an extensive, computer-driven analysis of the genetic basis of disease, as well as assisting in the developments of treatments, cures, and preventive practices. In these projects, the database--both local and online--is a key technology. A database such as deCODE's Icelandic Healthcare Database (IHD) is a good example, in that it brings together three types of genetic-medical data: (i) phenotypic (observable) health data and health-care information, (ii) genotypic data (genomic sequence), and (iii) genealogical and hereditary data (gene pool and statistical information). The IHD is both highly specified in its object (a genetically-isolated population) and widespread in its coverage (containing national health records, genealogical records, and genetic data). In addition, the IHD, as part of deCODE, is a business endeavor as well as a health-care one, and deCODE uses this product--here the product is information or database access--to forge productive relationships with governments (Icelandic national genealogical records) as well as with other businesses (especially pharmaceutical corporations). The scope and ambition of projects such as Celera's or deCODE's are, of course, made possible by advances in computing technologies, most of which are exorbitantly expensive, inaccessible to non-specialists, and which have a high learning curve. This, combined with the illegibility of genetic sequence data by non-specialists, makes the potentials of genomics extensively out of the reach of the general healthcar public, and it makes any informed critique or public debate challenging as well. For instance, in articles written by Kari Steffanson, CEO of deCODE, the potentials of informatics technologies transforms science research from a linear, hypothesis-driven approach, to a semi-automated data mining agent that completes computations far beyond what was possible prior to the use of parallel processing computers and data mining algorithms. Both deCODE and other companies such as Human Genome Sciences have stated that they are in the business of information and discovery. The intersection of business approaches to genomics and the use of informatics-based tools means that science research is based in combinatorial techniques; the best pattern-recognition combinations will be of the highest value, the greatest assets.
In considering this complex of life-science business models, new computer tools, and a genomics-based approach to populations and disease, we can actually differentiate several types of strategies for databasing the body within contemporary genomics.
Universal Reference: Generalized human genome projects, such as that undertaken and first completed by Celera, emphasize their universality as models for the study of disease, treatment, and a greater understanding of life at the molecular level. They also highlight the backdrop against which all genetic difference and/or deviations from a norm will be assessed. Indeed, part of the reason genomic projects by Celera, Incyte, or the public consortium have received so much attention is that these projects are in the process of establishing the very norms of genetic medicine. Their practices and techniques themselves are the processes of establishing what will or will not exist within the domain of consideration for genetic medicine, and will or will not be identified as anomalous or central to genetic knowledge and genetic drug design. A company like Celera, though it assembles its sequences from a number of anonymous individuals, constructs one single, universal human genome database. That database becomes the model for all sorts of research emerging from genomics--proteomics, functional genomics, DNA diagnostics, and so forth.
Difference and the Subject: At the opposite pole of Celera's universal model of the human genome is a field of research that deals with the minute, highly specific base pair changes that differ from one individual to another. Accounting for about .1 percent of the total genome (or roughly one million base pair variations per individual), these "single nucleotide polymorphisms" (SNPs) are thought to contribute to a range of phenotypic characteristics, from the physical markers that make one person different from another (hair color, etc.), to the susceptibility to single base pair mutation conditions (such as sickle cell or forms of diabetes). However, many SNPs are phenotypically non-expressive; that is, they are base pair changes that do not affect the organism in any way and are simply differences in code sequence. Specific projects, such as Genaissance Pharmaceutical's HAP series of haplotype database tools, as well as the Whitehead Institute's SNP database, focus exclusively on these minute base pair changes. These databases of individual point changes form linkages between variation within a gene pool and a flexible drug development industry that operates at the genetic level.
Stratifications: Between universal genome projects and individualized genetic medicine research, other genomics projects are focusing on collectivities within a universal gene pool. The projects from deCODE, Genaissance, Myriad, and others focus on genetically isolated populations. Often combining the usual genotypic data with demographic, statistics, genealogy, and health-care data, these projects are both new forms of health-care management as well as studies of the effects of disease within genetically homogenous groups. Projects such as deCODE's IHD promise to be able to perform large-scale computational analysis on entire genomes, but in this they also threaten to fully abstract genetic data from real, physical communities. The population genomics projects take a genetics-based or genotypic view of race, and make connections to the functioning of norms within medicine and health care. In doing this they establish an intermediary space between the universality of the human genome project (which claims a uniformity under the umbrella of a distinct species) and the high-specificity of SNP or haplotype databases (which claim individual difference within a general category). This intermediary space is precisely the space of racial (mis)identification and boundary-marking; it is the space where bioscience forms collectivities, composed of individuals and united under a common species categorization. With their primary aim as medical, these population genomics projects are involved in the re-articulation of race and ethnicity itself as biologically determined, and they do so through the lens of bioscience research and corporate biotechnology.
In this schematic of different types of genomics projects, we can see an approach toward biological information that is far from a simplistic monocultural model, in which difference is marginalized, silenced, or pushed out of the domain of serious consideration. In fact, everything in genomics moves toward an inclusion of differences, but differences that can be accounted for by both DNA and information. From a scientific perspective, of course, difference or diversity is the cornerstone of traditional evolutionary theory, be it random or directed environmental influence. Likewise, from a business perspective, diversity is not only the key to creating more custom-tailored products (as in genetic drug design), but diversity also enables a more thorough knowledge-production of the population. This is a kind of niche biomarketing, in which information is extracted from a heterogeneous population, then selectively organized, and re-routed into research and product development.
Projects like deCODE's IHD are prototypes for scientific-business practices that are indissociable from a consideration of race and ethnicity, themselves considered from a molecular and informatic perspective. They do two things: they work toward establishing new, more flexible sets of norms, both within biomedicine and from the point of view of business strategies, and in doing so they form new methods of population management and regulation.
The New Dotcom
In some ways, it is misleading when we talk about the biotech "industry"; in the most literal sense biotech rarely produces anything. Its specialty lies in modifications and recontextualizations of organic life. We can begin by outlining three main kinds of companies within the biotech industry:
First, there are the "pick-and-shovel" companies, mostly in the technology and laboratory supply sector, which provide the tools for research. Like the original pick-and-shovel businesses during the California gold rush, such companies implicitly believe that, while the actual genome may not yield any profits, the need for research technologies will. These companies are generally the lowest risk-takers, though radical new tools such as DNA chips are transforming the way in which research is carried out. An example is Affymetrix, which is one of the leading suppliers of microarrays, or DNA chips, for large-scale, efficient sequencing.
Second, there are the software and service companies, which operate mostly on the level of computer technology, software, and network applications. These companies often provide a counterpart to the pick-and-shovel companies by supplying the software tools necessary to complete the work done by the hardware. Such companies can offer software packages (such as Incyte's "LifeSeq" sequencing and analysis software), they can offer data analysis services (these are mostly software companies), or access to a database on a subscription-only basis (as the private genome companies such as Celera are doing).
Finally, there are what we might call the product-makers, those companies--usually large pharmaceutical corporations--that take the information generated by the second group (say, the information generated by Celera on the human genome), and transform it into an array of products, services, and practical techniques. The most prevalent among these is Big Pharma and the emphasis in such companies is on drug development and gene therapy-based drug treatments (or "pharmacogenomics"). Currently, the most prevalent test for the value of biotech research is the clinical trial, in which a genetically designed drug is put through an extensive series of tests before gaining approval by the U.S. Food & Drug Administration.
A consideration of these types of companies not only illustrates the degree to which biotech has become infotech, but it also suggests that the future success of the biotech industry is dependent on the ability to generate value out of the data collected from biological material. All of this is predicated on the assumption that biological bodies--tissues, cells, molecules, chromosomes, genes--can be unproblematically translated into data. Such a move indicates the degree to which biotech relies upon the notion of a stable "content" in the genome, irrespective of its material instantiation (be it in cells or in computer databases). It is in this sense that, for the biotech economy, the genome becomes a value-generator, through its ability to adequately perform in the organism (and thus the eagerness of biotech companies to gain patents on novel molecules). The biotech economy demands that everything within biotech have an informatic equivalent; it does not, like biotech research, demand that everything be translated into information, but it does demand a direct link between genetic bodies and relevant data.
The economics of biotech touches the population, not through direct genomic database management, but more indirectly through the commodification of such databases. As biotech becomes increasingly privatized, the database corporations such as Celera or Incyte will become the main bio-commerce brokers. At issue is not the buying or selling of databases, but the generative potential of genetic data; in such a case racial population genome databases, individualized SNP or genetic screening databases, and various animal genome databases important for human medicine will all become sources of a biopolitical management of selected collectivities.
Each and Every
As a way of approaching such issues, it might be helpful to consider Michel Foucault's later work dealing with "biopolitics." Although the term begins to appear with some regularity around the time of Discipline & Punish, Foucault later clarified its relationship to his other concepts of "bio-power" and "disciplines." For Foucault, biopolitics is "the endeavor, begun in the eighteenth century, to rationalize the problems presented to governmental practice by the phenomena characteristic of a group of living human beings constituted as a population: health, sanitation, birthrate, longevity, race..." (Ethics, 73).
Roughly speaking, Foucault calls "biopolitics" that mode of politically accounting for "the population," considered as a biological, species entity. This is differentiated, but not opposed to, "bio-power," in which a range of techniques, gestures, habits, and movements, most often situated within social institutions such as the prison, the hospital, the military, or the school, collectively act upon the individualized body of the subject.
The fundamental difference here is not between the individual and society, but rather between individuating and collectivizing strategies, similar to what Foucault earlier called "dividing practices." Biopolitics is, first, an organizational technology articulating something called the biological and species population--a collectivity of bio-subjects. Through a range of techniques and practices, it produces and collects knowledge of the population in the form of a manageable quantum of information. And biopolitics reproduces its continual and changing regulation of the population through a set of techniques and practices that insert this informatic knowledge back through the social-biological body of the population, culminating in a quantifiable, organizational entity that may be "touched" at a variety of points through a range of technologies. To these characteristics we might add a further extension, which is that contemporary population genome projects form complex hybrids of economics, policy negotiations, and high technology. In short, biopolitics is not exclusive to the mobilization of state forces that Foucault emphasizes (what he terms "governmentality"), but it brings to the forefront the multiple constraints set forth by an informatics-based view of the body.
Bearing in mind Foucault's emphasis on the population as a biologically defined entity, we can outline several important factors in considering population genomics:
Informatics: Informatics is a key factor in considering contemporary power relationships in biotech, because it works as a medium for transforming bodies and biologies into data. But that data is understood in many different ways, not simply as the liquidation of the body. Genomic, population, ethnic, and SNP databases are just some examples of the variability of biological data. At its root the case of informatics brings up philosophical questions (what is the body if it is essentially information?), but preceding this at every point are political questions (how are the manifold differences in embodied communities encoded into data?).
Biodiversification: Biodiversity is a term that is most often reserved for debates concerning the preservation, conservation, or sustainability of natural resources, which depend a great deal on natural diversity. Most often biodiversity is opposed to transnational corporations, which take advantage of natural diversity to produce monocultures as product, in what Vandana Shiva calls "biopiracy." In the context of genomics, biodiversity becomes a signifier for genetic difference, and the ways in which genetic difference gets translated into cultural difference. Biopiracy is not simply about the destruction of natural resources, it is about a complex re-framing of "nature" and the use of that diversity toward commercial ends. As Shiva states, the discourse of biodiversity is actually less about sustainability than it is about the conservation of biodiversity as a "raw material" for the production of monocultures. The same can be said of molecular biotech, especially in the case of genome projects, genome databases, gene banks, human tissue banks, DNA sample resources, and other instances of biopiracy.
Genethnicities: The point of controversy with many genome projects is the issue of genetic discrimination. With molecular genetics, a unique type of identification and differentiation has come about, in which individuals and populations can be uniquely analyzed and regulated through their DNA. This twofold process of molecular genetics (genetic essentialism) and informatics (population databases) paves the way for a new type of identification, and in some cases, discrimination. It is based not on race, gender, or sexuality, but rather on information (genetic information). Bioinformatics--an apparently neutral technical tool--thus becomes manifestly political, negotiating how race and ethnicity will be configured through the filter of information technology.
Managed Health: However, even when public projects attempt to assemble biological databases, there is still discomfort over the very process of sampling, extraction, and utilization of one's own body for medical research. In the case of genomics, this is a very abstract process, but also a very simple one, moving from a blood sample to a DNA archive in a computer. On the one hand there have been disputes concerning the ownership of one's own DNA, in which, for instance, a company like deCODE develops novel patents based on research done on individual human DNA samples. For this reason many companies require complex disclaimers, and they also make an important further distinction--between one's own lived body and what is considered health-related data generated from a person's body. This reduction of the debate to a distinction between blood and data may solve the patenting and ownership issue, but it still does not address the ontological difference between one's own, proper body, and the genetic data extracted from one's body.
Taking these issues into consideration, do genomics projects such as deCODE's IHD, or Celera's human genome database, form instances of a colonialist imperative, a kind of "biocolonialism"? If, traditionally, colonialism involves the forced appropriation of land and economy by one economically and technologically empowered collective over another disenfranchised collective, then biocolonialism presents us with a situation in which the bodies of the colonized are in fact the land and economy. As theorists such as Edward Said have pointed out, colonialism is not only economic and militaristic, but also a complex cultural encounter. How might this asymmetrical intersection of economies, power dynamics, and cultures translate into biotech?
Biocolonialism takes the molecular body and biological processes as the territory or the property to be strategically negotiated and acquired. Often, when governmental regulations stipulate, this is done through some type of informed consent, so that the individuals and/or community whose biological materials are being acquired, are informed about the reasons and future uses of their bodies. At other times this is handled without such formalities, resulting in either biopiracy (a simple taking without any reimbursement to the community) or patenting (based on cell lines that are minimally modified). In order to conduct successful research, and potentially turn over great profits, the first thing needed is a large resource, which in this context means a biological sample suited for the particular type of research being conducted. For example, a cell line from an individual from New Guinea, from a collectivity known to have developed a resistance to certain forms of diabetes (in the example of the HGDP). Therefore, the territory overtaken by the biotech industry is the molecular body itself, contextualized through race and high-technology. In addition, biocolonialism doesn't so much reconfigure the colonized economy, as it appropriates the molecular body as a type of condensed economy in itself. The same molecular body that is the territory for biocolonialism, is also the primary value-generator, by virtue of its composition and operativity as a molecular organism.
Here In This Colony
As we've pointed out, the bioinformatics of database access is inextricably connected, in biotechnology, to software and subscription models for research, and this is where bio-informatics intersects with bio-capitalism, or the integration of genetic bodies into an advanced capitalist framework. Discussing the free-floating dynamics of late capital, Fredric Jameson notes that the self-referential feedback loops of finance capital propel it into a zone of "autonomization," a virus-like epidemic that forms a speculation on speculations. For Jameson this has resulted in "the cybernetic 'revolution,' the intensification of communications technology to the point at which capital transfers today abolishes space and time and can be virtually instantaneously effectuated from one national zone to another." It is this instantaneousness and total connectivity that has driven many labs to fully incorporate advanced computing and networking technologies (such as Celera), and it is this integration of biotech with infotech that has brought companies such as IBM, Compaq, and Sun Microsystems into the lifesciences. If, in the biotech industry, finance capital and laboratory research are interconnected, how does this transform the "wet" biological materials in the lab, the molecular bodies of life-science research?
One response is to suggest that it is in the unique, hybrid objects of the biotech industry--genomic databases, DNA chips, automated protein analysis computers--that genetic bodies and a "digital capitalism" intersect. In other words, the correlations between bodies and capital, which enables a biotech industry to exist at all, are currently mediated by computer and information technologies. The use of such technologies is predicated on the assumption that a range of equivalencies can be established between, say, a patented genetic sequence and the marketing of that sequence through genetic-based drugs.
What has become of the original issue put forth by the critics of the HGDP? Part of the problem is that the issues dealt with in the criticism of the HGDP have been handled in the way that criticism of genomic mapping and human embryonic cloning have been handled: they have been filed under the worrisome category of "bioethics." As postcolonial critiques have pointed out, the HGDP came to a relative standstill because it could not reconcile Western scientific assumptions and intentions with non-Western perspectives toward "agriculture," "population," "medicine," "culture," and so forth. The gap in between the HGDP's biocolonialism and those predominantly non-Western cultures that were to be the source of biomaterial for the HGDP database illustrates the degree to which "global" once again means "Western" (and, increasingly, "economic"). But it is equally important to note that biocolonialism need not be the familiar First World-Third World struggle that has characterized debates on post-colonialism recently. As we've seen, genomics projects articulate genetic difference according to a variety of standards (universal, individualized, groups) that in no way depend upon the marketing of biomaterials from indigenous collectives. If anything, biocolonialism will depend as much upon Euro-American health-care models as it will on the isolated or unique genetic reserves of indigenous populations. The development of DNA chips, genetic screening, genetic profiling, and medical genetics are just some examples.
One of the meanings of the decrease in the presence of the HGDP and the rise in bioinformatics developments and applications is that the issues of race and ethnicity have been sublimated into a paradigm in which they simply do not appear as issues. That paradigm is, of course, one based on the predominance of information in understanding the genetic makeup of an individual, population, or disease. When, as geneticists repeatedly state, genetic information is taken as one of the keys to a greater understanding of the individual and the species (along with protein structure and biochemical pathways), the issue is not race but rather how to translate race into information. In such propositions, race and ethnicity become split between their direct translation into genetic information (a specific gene linked to a specific disease predisposed to a given population) and its marginalization into the category of "environmental influence" (updated modifications of the sociobiological imperative, in which race and culture are accounted for by biology).
The biopolitics of genomic science is that of an informatics of the population in which cultural issues (ethnicity, cultural diversity) are translated into informational issues (either via a universal, generalized map of the human genome, or via individualized maps of genetic diversity). As Evelyn Fox Keller, Donna Haraway, and others have pointed out, information is not an innocent concept with regard to issues of gender and race. The questions that need to be asked of bioinformatics, online genomic databases, and genome mapping projects is not just "where is culture?" but rather, "how, by what tactics, and by what logics are technoscientific practices re-interpreting and incorporating cultural difference?"
Public Memory, Privatized Body
It is these intersections between populations, economics, and informatics that are performed in Diane Ludin's project Harvesting the Net::MemoryFlesh. Combining a critical-artistic approach with a knowledge of Web technologies, Harvesting the Net fleshes out the connections between bodies and technologies in the biotech industry. Using her concepts of "wet code" and "dry code," Ludin explores what she refers to as the "circumstance of the body" as it is interpolated between techno-hype and bioscientific anticipation.
From the technophilia evident in human genome projects (for instance, the New York Times has run a number of articles on how gene sequencing computers were largely responsible for the progress of the human genome map), to the many promises that biotech research narrates (any advertisement from a biotech startup illustrates this), the individuated body of the biomedical subject becomes a site of contestation. The debates over genetic privacy, patenting, and the promised medical benefits of biotech all extend from this individuated body, at once distributed through an array of databases, and condensed into a genetic profile.
In the midst of these tensions between wet and dry codes, hype and anticipation, Harvesting the Net inserts the figure of the artist as a kind of data-filtering module. Mainstream media reportage, science journalism, press releases, as well as images, are all routed through the critical lens of the artist, and re-purposed as an inquiry into the ways in which the body, for the biotech industry, is inextricably connected to questions of race and economics. Harvesting the Net shows us that the political approaches in biotech inform every aspect of its public face, from the images used in news articles and press releases, to the images depicting high-tech labs, to the professionalism of biotech websites that contain investment portfolios.
Using the "legitimate" materials from the biotech industry, Harvesting the Net recontextualizes the multi-medial language of the body, including it to articulate the wet and dry codes which are constantly transmitted and reformatted. Between media representations, computer databases, search engines, e-trading, and the molecular biology lab, Ludin asks us to consider how bodies might fulfill a "recombinant" functionality within the biotech industry.
Brower, Vicki. "Mining the Genetic Riches of Human Populations." Nature Biotechnology 16 (April 1998): 337-339.
Burchell, Graham, Colin Gordon, and Peter Miller, eds. The Foucault Effect: Studies in Govermentality. Chicago: Univ. of Chicago Press, 1993. Celera Genomics: <http://www.celera.com>.
Chakravarti, Aravinda. "Population Genetics--Making Sense Out of Sequence." Nature Genetics 21 (January 1999 supplement): 56-60.
Dawson, Michael and John Bellamy Foster. "Virtual Capitalism." In Capitalism and the Information Age. Ed. Robert McChesney et al. New York: Monthly Review Press, 1998. 51-69.
deCODE Genomics: <http://www.decode.com>.
Enserink, Martin. "Iceland OKs Private Health Databank." Science 283 (1 January 1999): 13
Foucault, Michel. The History of Sexuality, Vol.I. New York: Vintage, 1978.
---. Ethics: Subjectivity and Truth. The Essential Works of Michel Foucault 1954-1984, Vol. I. Ed. Paul Rabinow. New York: New Press, 1994.
---. Power. Ed. James Faubion. New York: New Press, 2000.
Haraway, Donna. Modest_Witness@Second_Millennium.FemaleMan©_Meets_OncoMouse™: Feminism and Technoscience. New York: Routledge, 1997.
Howard, Ken. "The Bioinformatics Gold Rush." Scientific American (July 2000): 58-63.
Jameson, Fredric. "Culture and Finance Capital." In The Cultural Turn: Selected Writings on the Postmodern, 1983-1998. New York: Verso, 1998. 136-62.
Kahn, Patricia. "Genetic Diversity Project Tries Again." Science 266.4 (1994): 720-22.
Keller, Evelyn Fox. Reflections on Gender and Science. New Haven: Yale Univ. Press, 1995.
Persidis, Aris. "Bioinformatics." Nature Biotechnology 17 (August 1999): 828-830.
Philipkoski, Kristen. "Everybody Into the Research Pool. " Wired News (11 October 2000): <http://www.wired.com>.
Said, Edward. Orientalism. New York: Vintage, 1979.
Schiller, Dan. Digital Capitalism: Networking the Global Market System. Cambridge: MIT, 2000.
Shiva, Vandana. "Biodiversity, Biotechnology and Profits." Biodiversity: Social & Ecological Perspectives. Ed. Vandana Shiva et al. New Jersey: Zed Books, 1991.
----. Biopiracy: The Plunder of Nature and Knowledge. Toronto: Between the Lines, 1997.
Stefansson, Kari and Jeffrey Gulcher. "The Icelandic Healthcare Database and Informed Consent." New England Journal of Medicine 342:24 (15 June 2000).
Waldby, Catherine. The Visible Human Project: Informatic Bodies and Posthuman Medicine. New York: Routledge, 2000.