PART 1 – The Empirical Picture
4.17 SWISS BIOGRID
Case Overview
What does the project do mainly? The Swiss Bio Grid project ran from 2004-2008, supporting large-scale computational applications in bioinformatics, biosimulation, chemoinformatics and bio-medical sciences by utilizing distributed high-performance computing, high speed
networks, massive data collections and archives, as well as the necessary software tools and data integration capabilities. 15
Motivations for setting it up: The motivation for the project was to assess whether Grid computing technologies could be successfully deployed within the life science research community in Switzerland.
Main goals of the project: The Swiss Bio Grid goal was to establish a value added collaborative platform focused on solving key scientific challenges of the life sciences. The project
successfully completed two proof-of-concept studies, one in proteomics and the other investigating high throughput docking for dengue virus research.
Project maturity: This project ended in 2008, although lessons learned during the project continue to feed into a national initiative, the Swiss National Grid project (SwiNG).
Project funding: No external funding was committed to this project. Likely funding agencies, such as the Swiss National Science Foundation, were reluctant to put money into building infrastructures that weren’t directly related to the achievement of a specific scientific goal.
Organizational Structure
Size and composition: Six different academic groups across Switzerland, one being the research lab of pharmaceutical company Novartis. There were three infrastructure personnel involved (up to 70% FTE in total), and two distinct scientific groups developing the separate projects.
Governance: A project coordinator led the project, with a steering committee supporting him and monthly teleconference to raise any developing problems.
Managing internal and external relations
Management of the project: Swiss Bio Grid developed a simple management structure, assisted in some ways by absence of any funding bodies with which to negotiate. However, the IP issues arising from the involvement of commercial partners took considerable time and effort to resolve (although this was achieved in the end).
Users: The users were mainly those involved in the project itself, both academic researchers and those in the pharmaceutical industry. However, the project was aimed at being
extensible in the future, and may become so.
User recruitment: The users of this Grid were exclusively those researchers who were involved in the project from its development. No extra users were recruited to the team.
15 Number of informants: 1 (in this round*), totaling 90 mins.
*The research for this project built on previous research conducted by Ralph Schroeder and Matthijs den Besten into Swiss BioGrid. Schroeder and den Besten had interviewed 6 informants, totaling an estimated 6 hours.
Page 92 Drivers and barriers to adoption: The drivers for adoption of these tools and techniques were the academic users who sought to apply them in specific projects related to their work. The organic nature of the development of the project largely eliminated barriers to adoption.
Challenges in interdisciplinary collaboration: None – the two scientific projects were entirely separate. The only shared resource was the computing Grid. The scientists were also highly competent in technology, and were familiar with writing code and developing solutions to technological problems. There were therefore no major clashes between the biological scientists and computer scientists, as each understood enough about the challenges involved that they were able to work together sympathetically to resolve any problems.
Collaboration with other organizations: In this project the research results belonged to the institutions and groups leading the investigation, they simply used a computing Grid across different institutions to distribute computing power to enable the data processing. The potential for expanding this Grid exists, but the nature of the field makes collaborative research less likely.
Technology
Main technologies, resources and services:
Processing: Spare computing cycles on PC clusters were used.
Software: A new piece of software was developed during the project to form a bridge
between the Unix machines and PCs, to allow a job to be distributed between these two types of computer. This was known as a meta-scheduler. No such software existed at the time the project was developed, although similar software has since been developed.
Network: Largely within the institutions.
Data/storage: Hosted by and within the partner institutions.
Role of technology development: The only development is that mentioned under ‘software’
above. The goal of the project was to share largely redundant computing resources rather than to create new technology resources.
Data sharing: The sharing of research data among academic groups was not the focus of this project, rather the intention was to share computing resources to enable large processing capabilities for each of the proof-of-concept projects described above.
Interoperability with similar or connecting infrastructures: Swiss Bio Grid decided against using EGEE, as the academic communities were resistant to installing this kind of software.
They felt EGEE was too intrusive, too time consuming to install, and assumed homogeneity of infrastructure that was not realistic within this community. Swiss Bio Grid therefore installed a ‘less ambitious’ middleware, NorduGrid (ARC) which was much less intrusive and
heavyweight. The installation time was minimal, a major consideration in a project operating through goodwill.
Contribution
Main contributions of project:
Political: A major contribution was that it was possible to build grids organically. You could build something that didn’t require top-down governance or dictation of what technology was going to be used. Swiss Bio Grid showed that it was possible to do this by consensus.
Swiss Bio Grid also had a profound impact on national engagement with Grid technology. It was one of the factors which led, in 2008, to a new Grid initiative was set up to represent
Page 93 Switzerland in international Grid efforts. Several of the partners in SBG are now active
members of this new structure, the Swiss National Grid (SwiNG).
Technical: The software developed to build a bridge between computers running different operating systems was a major contribution at the time.
Scientific: The virtual screening project has identified c.100 potential drug candidates, a number of which will be put into experimental validation by Novartis. A new drug for Dengue would have a dramatic effect.
The proteomics project showed that this infrastructure worked for the specific project that was developed, revealing the potential for similar projects to attempt to employ this kind of solution. It is unlikely that this exact infrastructure will be widely used by other groups, as each lab tends to develop its own approach and technologies for doing analysis. A general purpose solution is unlikely to be realised at this time.
Challenges: One of the key challenges for Swiss Bio Grid was to gainfully employ an immature technology in a heterogeneous environment without prior funding. Thus, as a proof-of-concept project, Swiss Bio Grid bore a heavy burden as it was set to shape the future of e-Research in the life sciences in Switzerland. With the recent establishment of the Swiss National Grid, the challenge seems to have been met.
Swiss BioGrid also illustrates a wider problem in the life sciences, which is that rather than becoming integrated around a shared computational infrastructure, e-Science initiatives have resulted in the promulgation of countless heterogeneous resources and efforts. While much of the development of the Grid has been geared towards applications in particle physics, which tend to be centralised and fairly homogeneous, the more heterogeneous requirements of computational biology have been poorly supported by existing Grid solutions, and it is unlikely that this picture will change unless there is a concerted effort in adapting Grid tools and putting them on a permanent footing.
Informants’ recommendations to policy makers
Bottom-up development is highly desirable in order to sure that real scientific needs are addressed. On the other hand, the longer-term usefulness of the system that has been developed can only be sustained if the context of a longer-term structure in which it is embedded has been ensured. Put differently, an infrastructure that smaller projects can be part of is essential to ensure that gains are not lost after a project finishes.
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SWOT analysis
Table 4-34: Swiss BioGrid strengths and weaknesses
Strengths Weakness
Long-term funding
N/A Project has finished. N/A Project has finished.
Sustainability The project has now ended, but the infrastructure is still in use by some of the scientists involved.
The project has now ended, and some project staff have transferred to a new initiative, SwiNG.
User recruitment None was developed during the project
as it was an organic, bottom-up project devised by the scientists.
Involvement of current users
N/A N/A
Organizational bedding
The project was extremely well embedded within the participating institutions.
Institutionalised links
N/A N/A
External use of software, tools
Lessons learned from SBG have been transferred to the SwiNG project.
Table 4-35: Swiss Biogrid opportunities and threats
Opportunities Threats
Funding of member organizations
Stable, particularly the commercial partner, which continues to invest in exploiting the results.
Technology monitoring
N/A N/A
Competition with other
infrastructures or technologies
There was none within Switzerland at the time.
Security risks N/A N/A
Change of user communities and fields
N/A N/A
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