Life Science Research and Drug Discovery at the Turn of the 21st Century: The Experience of SwissBioGrid
Identifieur interne : 000635 ( Pmc/Checkpoint ); précédent : 000634; suivant : 000636Life Science Research and Drug Discovery at the Turn of the 21st Century: The Experience of SwissBioGrid
Auteurs : Matthijs Den Besten ; Arthur J. Thomas [Royaume-Uni] ; Ralph Schroeder [Royaume-Uni]Source :
- Journal of Biomedical Discovery and Collaboration [ 1747-5333 ] ; 2009.
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 was established to provide computational support 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 large-scale 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 dengue virus protein targets. 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.
Url:
PubMed: 19521952
PubMed Central: 2850249
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>SwissBioGrid was established to provide computational support 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 large-scale 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 dengue virus protein targets. 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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Biomed Discov Collab</journal-id>
<journal-title-group><journal-title>Journal of Biomedical Discovery and Collaboration</journal-title>
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<issn pub-type="epub">1747-5333</issn>
<publisher><publisher-name>University of Illinois at Chicago Library</publisher-name>
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<article-meta><article-id pub-id-type="pmid">19521952</article-id>
<article-id pub-id-type="pmc">2850249</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
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</article-categories>
<title-group><article-title>Life Science Research and Drug Discovery at the Turn of the 21st Century: The Experience of SwissBioGrid</article-title>
</title-group>
<contrib-group><contrib id="contrib1" contrib-type="author"><name><surname>den Besten</surname>
<given-names>Matthijs</given-names>
</name>
<xref ref-type="aff" rid="aff1">2</xref>
</contrib>
<contrib id="contrib2" contrib-type="author"><name><surname>Thomas</surname>
<given-names>Arthur J</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
</contrib>
<contrib id="contrib3" contrib-type="author"><name><surname>Schroeder</surname>
<given-names>Ralph</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Oxford Internet Institute, Oxford University, St Giles, Oxford</addr-line>
<country>United Kingdom</country>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Oxford -Research Centre, Oxford University, Keble Road, Oxford</addr-line>
<country>United Kingdom</country>
</aff>
<pub-date pub-type="collection"><year>2009</year>
</pub-date>
<pub-date pub-type="epub"><day>22</day>
<month>4</month>
<year>2009</year>
</pub-date>
<volume>4</volume>
<fpage>5</fpage>
<lpage>5</lpage>
<history><date date-type="received"><day>02</day>
<month>3</month>
<year>2009</year>
</date>
<date date-type="rev-request"><day>10</day>
<month>3</month>
<year>2009</year>
</date>
<date date-type="rev-recd"><day>20</day>
<month>3</month>
<year>2009</year>
</date>
<date date-type="accepted"><day>21</day>
<month>3</month>
<year>2009</year>
</date>
</history>
<permissions><copyright-year>2009</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
</license-p>
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<self-uri xlink:type="simple"></self-uri>
<abstract><sec><title>Background</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>SwissBioGrid was established to provide computational support 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 large-scale 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 dengue virus protein targets. 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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
</abstract>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Royaume-Uni</li>
</country>
</list>
<tree><noCountry><name sortKey="Den Besten, Matthijs" sort="Den Besten, Matthijs" uniqKey="Den Besten M" first="Matthijs" last="Den Besten">Matthijs Den Besten</name>
</noCountry>
<country name="Royaume-Uni"><noRegion><name sortKey="Thomas, Arthur J" sort="Thomas, Arthur J" uniqKey="Thomas A" first="Arthur J" last="Thomas">Arthur J. Thomas</name>
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<name sortKey="Schroeder, Ralph" sort="Schroeder, Ralph" uniqKey="Schroeder R" first="Ralph" last="Schroeder">Ralph Schroeder</name>
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