Designing a post-genomics knowledge ecosystem to translate pharmacogenomics into public health action
1 Columbia Law School - LL.M. Program, 435 West 116th Street, New York, NY 10025, USA
2 Research Group on Complex Collaboration, Desautels Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal, QC, Canada H3A 1G5
3 Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine, McGill University, 740 Dr. Penfield Avenue, Suite 5200, Montreal, QC, Canada H3A 0G1
4 Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute and Predictive Analytics, Seattle Children's Hospital, 1900 9th Ave, Seattle, WA 98101, USA
5 Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
6 Data-Enabled Life Sciences Alliance International (DELSA Global), Seattle, WA 98101, USA
Genome Medicine 2012, 4:91 doi:10.1186/gm392Published: 29 November 2012
Translation of pharmacogenomics to public health action is at the epicenter of the life sciences agenda. Post-genomics knowledge is simultaneously co-produced at multiple scales and locales by scientists, crowd-sourcing and biological citizens. The latter are entrepreneurial citizens who are autonomous, self-governing and increasingly conceptualizing themselves in biological terms, ostensibly taking responsibility for their own health, and engaging in patient advocacy and health activism. By studying these heterogeneous 'scientific cultures', we can locate innovative parameters of collective action to move pharmacogenomics to practice (personalized therapeutics). To this end, we reconceptualize knowledge-based innovation as a complex ecosystem comprising 'actors' and 'narrators'. For robust knowledge translation, we require a nested post-genomics technology governance system composed of first-order narrators (for example, social scientists, philosophers, bioethicists) situated at arm's length from innovation actors (for example, pharmacogenomics scientists). Yet, second-order narrators (for example, an independent and possibly crowd-funded think-tank of citizen scholars, marginalized groups and knowledge end-users) are crucial to prevent first-order narrators from gaining excessive power that can be misused in the course of steering innovations. To operate such 'self-calibrating' and nested innovation ecosystems, we introduce the concept of 'wiki-governance' to enable mutual and iterative learning among innovation actors and first- and second-order narrators.
'[A] scientific expert is someone who knows more and more about less and less, until finally knowing (almost) everything about (almost) nothing.' 
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