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In many countries, developed and emerging, research infrastructure (RI) is considered either an entitlement by the academic elite or an expense by those holding the purse strings.

In my mind, it is neither! Rather, it is an investment made in service of a particular purpose, and meant to yield returns for a variety of concerned stakeholders. In order to achieve this, what is required are adequate business models, dedicated managerial capacity and – above all – clarity regarding that intended purpose.

To date, ‘bricks-and-mortar’ solutions have been the norm, and for good reasons: Shiny buildings and fancy equipment give funders the warm and fuzzy feeling of achievement; integration generates process efficiencies due to physical proximity; investors like to see commitment by the state in advance to making decisions in favour of moving projects or entire operations into new locations. This is no less true for infrastructure in the fields of Genomics, Proteomics and Bioinformatics.

The more difficult economic circumstances of late and the business model uncertainties in the life and biomedical sciences are merely two factors that have changed the perceptions about this model. Amongst others, for private investors, it is something that locks capital into assets that don’t sweat in comparison to people who work hard towards achieving project goals (at least, that is what one would expect from bio-entrepreneurs); and for state agencies, it creates headaches around maintenance and sustainability, in particular if occupants and users of infrastructure depend on funding by the state as well.

Organizing disparate RI into coherent yet agile structures, and processes, for efficient value creation is a viable alternative to the ‘bricks-and-mortar’ approach; it’s an approach that has been employed successfully in other sectors1 and it is rather en vogue in the biotech sector as well2. Although such a model can increase transaction costs in comparison to a vertically integrated organisation this can be overcome by way of building redundancy and a strong emphasis on performance into the system. Certainly in an emerging economy context such a model has relevance, when the biotech sector tself is only in at state of infancy; there is a lack of certainty about models for sustainable use of capital-intensive infrastructure; and, funding for projects is generally scarce.

Below is a high-level summary of considerations regarding a viable technology platform model, based on a network orchestration approach, put together at the occasion of a meeting with key stakeholders in the Department of Science & Technology (DST), Pretoria, South Africa, on 12 August 2013.

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References

1 Fung, V. K., Fung, W., & Wind, Y. (2008). Competing in a flat world: building enterprises for a borderless world. Wharton School Publishing.

10 Truths (or Fictions) About Virtual Biotech Startups

Resources

Network orchestration

What the Vienna Philharmonic Orchestra and biotech innovation have in common…

Economic impact of Human Genome Project & Genomics

Lessons South Africa can learn from US-led investments into Genomics

http://www.unitedformedicalresearch.com/wp-content/uploads/2013/06/UMR_GenomicRevolution.jpg

http://battelle.org/docs/default-document-library/economic_impact_of_the_human_genome_project.pdf

http://web.ornl.gov/sci/techresources/Human_Genome/publicat/2013BattelleReportImpact-of-Genomics-on-the-US-Economy.pdf

Problems with omics based innovation

On ‘omics’ translation and innovation

Experience curve

http://en.wikipedia.org/wiki/Experience_curve_effects

http://www.economist.com/node/14298944

http://deloitteblog.co.za.www102.cpt1.host-h.net/wp-content/uploads/2011/11/How-to-drive-operational-efficiency-and-improve-forecasting.pdf

Baukasten principle

http://en.wikipedia.org/wiki/Volkswagen_Group_MQB_platform