The emergence and diffusion of DNA microarray technology

Jenkins Collaboratory for New Technologies in Society, Duke University, John Hope Franklin Center, 2204 Erwin Road, Durham, North Carolina 27708-0402, USA.
Journal of Biomedical Discovery and Collaboration 02/2006; 1(11):11. DOI: 10.1186/1747-5333-1-11
Source: PubMed

ABSTRACT The network model of innovation widely adopted among researchers in the economics of science and technology posits relatively porous boundaries between firms and academic research programs and a bi-directional flow of inventions, personnel, and tacit knowledge between sites of university and industry innovation. Moreover, the model suggests that these bi-directional flows should be considered as mutual stimulation of research and invention in both industry and academe, operating as a positive feedback loop. One side of this bi-directional flow – namely; the flow of inventions into industry through the licensing of university-based technologies – has been well studied; but the reverse phenomenon of the stimulation of university research through the absorption of new directions emanating from industry has yet to be investigated in much detail. We discuss the role of federal funding of academic research in the microarray field, and the multiple pathways through which federally supported development of commercial microarray technologies have transformed core academic research fields.
Our study confirms the picture put forward by several scholars that the open character of networked economies is what makes them truly innovative. In an open system innovations emerge from the network. The emergence and diffusion of microarray technologies we have traced here provides an excellent example of an open system of innovation in action. Whether they originated in a startup company environment that operated like a think-tank, such as Affymax, the research labs of a large firm, such as Agilent, or within a research university, the inventors we have followed drew heavily on knowledge resources from all parts of the network in bringing microarray platforms to light.
Federal funding for high-tech startups and new industrial development was important at several phases in the early history of microarrays, and federal funding of academic researchers using microarrays was fundamental to transforming the research agendas of several fields within academe. The typical story told about the role of federal funding emphasizes the spillovers from federally funded academic research to industry. Our study shows that the knowledge spillovers worked both ways, with federal funding of non-university research providing the impetus for reshaping the research agendas of several academic fields.

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    • "Michael Pirrung (a chemist) and Leighton Read came up with the idea to apply a production method used in the semiconductor industry in near-by Silicon Valley to the in situ peptide synthesis: a photolithographic , or light-directed technique. Fabian Pease, a professor at Stanford specializing in the development of electronic chips, joined the team (Lenoir & Giannella, 2006). Stephen Fodor came to Affymax in 1989. "
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    • "Nonetheless, as Rosenberg (2001) points out, university research and training is broadly responsive to the needs of industry. And there are instances in which industry advances can trigger a series of complementary inventions by universities that absorb the new technology as a research tool or as an engineering system meriting its own study (Rosenberg, 1982; Lenoir and Giannella, 2006). The role of university science in private sector R&D is multi-faceted. "
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    • "that was crucial to SwissBioGrid in terms of distributed computing. This type of industry-university interaction is often seen as key challenge in how research is changing (it has been explored, for example, by Forte, Lenoir, and their colleagues [17,18]) and may be important elsewhere. Yet we found that this interaction did not present a particular problem in the case of SwissBioGrid. "
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    ABSTRACT: It is often said that the life sciences are transforming into an information science. As laboratory experiments are starting to yield ever increasing amounts of data and the capacity to deal with those data is catching up, an increasing share of scientific activity is seen to be taking place outside the laboratories, sifting through the data and modelling "in silico" the processes observed "in vitro." The transformation of the life sciences and similar developments in other disciplines have inspired a variety of initiatives around the world to create technical infrastructure to support the new scientific practices that are emerging. The e-Science programme in the United Kingdom and the NSF Office for Cyberinfrastructure are examples of these. In Switzerland there have been no such national initiatives. Yet, this has not prevented scientists from exploring the development of similar types of computing infrastructures. In 2004, a group of researchers in Switzerland established a project, SwissBioGrid, to explore whether Grid computing technologies could be successfully deployed within the life sciences. This paper presents their experiences as a case study of how the life sciences are currently operating as an information science and presents the lessons learned about how existing institutional and technical arrangements facilitate or impede this operation. SwissBioGrid gave rise to two pilot projects: one for proteomics data analysis and the other for high-throughput molecular docking ("virtual screening") to find new drugs for neglected diseases (specifically, for dengue fever). The proteomics project was an example of a data management problem, applying many different analysis algorithms to Terabyte-sized datasets from mass spectrometry, involving comparisons with many different reference databases; the virtual screening project was more a purely computational problem, modelling the interactions of millions of small molecules with a limited number of protein targets on the coat of the dengue virus. Both present interesting lessons about how scientific practices are changing when they tackle the problems of large-scale data analysis and data management by means of creating a novel technical infrastructure. In the experience of SwissBioGrid, data intensive discovery has a lot to gain from close collaboration with industry and harnessing distributed computing power. Yet the diversity in life science research implies only a limited role for generic infrastructure; and the transience of support means that researchers need to integrate their efforts with others if they want to sustain the benefits of their success, which are otherwise lost.
    Journal of Biomedical Discovery and Collaboration 02/2009; 4:5.
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