Dr. Mark Boguski, is Vice President and Global Head of Genome and Proteome Sciences at Novartis Institutes for BioMedical Research in Cambridge, MA. There, he leads a division that integrates genomics, proteomics, and bioinformatics technologies to advance drug and biomarker discovery and development. With a career that spans three sectors – academia, government, and industry – Boguski is clearly in the business of life sciences innovation and integration.

Changing the Paradigm

Q: Tell us about your experience with the Human Genome Project (HGP) and its role in the emergence of personalized medicine.

A: When I started getting into the Human Genome Project (HGP) in 1989-1990, there was a huge amount of skepticism among most biomedical scientists. They thought that the HGP would divert funding from smaller scale, presumably more creative research.  After a few years, however – and some early wins that clearly empowered researchers of all stripes – this new way of tackling biomedical problems was accepted. Indeed, it changed the whole social equation of how we do science.

Q: In what way?

A: Under the aegis of creating this great multidisciplinary group of academic and commercial scientists, everyone gained the know-how to do a large-scale, biological science project quickly, on deadline, and with success.

Q: Do you think the HGP was a one-off, working successfully because specific historical and technological trends were converging?

A: I view the HGP not as a one-off, but rather a turning point that transformed the culture, organization, and funding of biomedical research. The approach has now been repeated at different scales.  For example, there are echoes of the HGP in the National Cancer Institute’s caBIG™ (cancer Biomedical Informatics Grid™) project.

A Complexity Issue

Q: So is this large-scale, team-based approach the future of biological science?

A: Not exclusively – certain kinds of innovation will only result from more small traditional, exploratory approaches.  But for many, if not most types of translational research, there is just too much complexity.

Q: Can you explain what you mean by complexity?

A: There is a visual of the human body, organized hierarchically.  At the top of this hierarchy are systems and organs. This is where pharmaceutical executives and directors of disease-areas focus:  they know a lot about physiology and disease, but they don’t fully appreciate what the genome is or how bioinformatics can be used to extract information. Another group is molecular biologists. They study genes but rarely have any training in, say, histology and pathophysiology and therefore can’t relate their knowledge to tissues and organs. Yet another group is the cell biologists, who think about signaling pathways, but often without a quantitative focus. What this symbolizes to me is that knowledge, although strong at each level, does not translate well across the continuum.

Teamwork and “Systems Thinking” Solve the Issue

Q: How can it? Can one person know enough to be proficient at all these levels?

A: In industry we don’t expect that. We put together teams to manage the complexity.

Q: What do you need in order for these teams to work?

A: You have to pick the right people with complementary backgrounds and skill sets.  You have to clearly define the goals and timelines.  And then you have to manage the project carefully mostly by ensuring effective communication across the team.

Q: Could this approach work in academia, where education, recognition, and funding practices all hinge on the one-principal investigator (PI) model?

A: It could. But fundamentally this would mean a reevaluation of the rewards system you are referring to.  In addition, it requires expanding the goals of scientific training to include concepts such as project management.

A New Kind of Recognition and Reward System

Q: Many will argue that PIs are not going to embrace this kind of work given that their recognition comes from first authorship on publications, especially top-tier publications, and also from how much grant money one brings in. How do you address that?

A: Particle physicists have already figured out how to apportion credit in large, multi-author studies. For example, they have 100 authors on their papers, listed alphabetically.  And when employers are evaluating one of the 100 authors for a job, there's much more word of mouth that goes into who made what contribution.  We’re actually starting to see this dealt with explicitly in the medical, and some biological literature.

Q: By “word of mouth,” do you mean scientists conversing at meetings and conferences?

A: With powerful and ubiquitous web-based communications technologies, the expression “word of mouth” seems quaint.  There are many ways to measure the impact of a person’s work, including usage statistics for web-based tools.   An even bigger challenge is how we can harness the power of “smart mobs,” loosely organized, or even unorganized groups of people with specialized information and instantaneous electronic access to the collective knowledge.

Q: So what will this mean for personalized medicine in the future?

A: At the basic research end of drug development, we will see a shift. Of course, there will still be small projects, with the kind of freedom and creativity they allow. But for some of these bigger projects, which personalized medicine needs in order to integrate information and apply basic science knowledge, the emphasis will shift toward getting the job done in a timely and cost-effective manner.

View of the Expert posted August 2006