378 CATALYZING INQUIRY
To illustrate the consequences in more concrete but future-oriented terms, the list below suggests
some of the activities that would be excluded under a funding model that focuses only on hypothesis-
testing research:
- Developing technologies that enable data collection from a myriad of instruments and sensors,
including real-time information about biological processes and systems, that permit us to refine and
annotate this information and incorporate it into accessible repositories to facilitate scientific study or
biomedical procedures; - Flexible database systems that allow incorporation of multiscale, multimodal information about
biological systems by enabling the inclusion (by data federation techniques such as mediation) of informa-
tion distributed in an unlimited number of other databases, data collections, Web sites and so on; - Acquisition of “discovery-driven” data (discovery science, as described in Chapter 2) to populate
datasets useful for computational analytical methods, or improvements in data acquisition technology
and methodology that serve this end; - Development of new computational approaches to meet challenges of complex biological sys-
tems (e.g., improved algorithmic efficiency, development of appropriate signal processing or signal
detection statistical approaches to biological data); and - Data curation efforts to correct and annotate already-acquired data to facilitate greater
interoperability.
These considerations suggest that expanding the notion of hypothesis may be useful. That is, the
discussion above regarding hypothesis testing refers to biological hypotheses. But to the extent that the
kinds of research described in the immediately preceding list are in fact part of 21st century biology,
nonbiological hypotheses may still lead to important biological discoveries. In particular, a plausible
and well-supported computational hypothesis may be as important as a biological one and may be
instrumental in advancing biological science.
Today, a biological research proposal with excellent computational hypotheses may still be rejected
because reviewers fail to see a clearly articulated biological hypothesis. To guard against such situa-
Box 10.6
Agencies and High-risk, High-payoff Technology DevelopmentAn example of agency reluctance to support technology development of the high-risk, high-payoff variety is
offered by Robert Mullan Cook-Deegan:^1In 1981, Leroy Hood and his colleagues at Caltech applied for NIH (and NSF) funding to support their efforts to
automate DNA sequencing. They were turned down. Fortunately, the Weingart Institute supported the initial work
that became the foundation for what is now the dominant DNA sequencing instrument on the market. By 1984,
progress was sufficient to garner NSF funds that led to a prototype instrument two years later. In 1989, the newly
created National Center for Human Genome Research (NCHGR) at NIH held a peer-reviewed competition for large-
scale DNA sequencing. It took roughly a year to frame and announce this effort and another year to review the
proposals and make final funding decisions, which is a long time in a fast-moving field. NCHGR wound up funding
a proposal to use decade-old technology and an army of graduate students but rejected proposals by J. Craig Venter
and Leroy Hood to do automated sequencing. Venter went on to found the privately funded Institute for Genomic
Research, which has successfully sequenced the entire genomes of three microorganisms and has conducted many
other successful sequencing efforts; Hood’s groups, first at Caltech and then at the University of Washington, went
on to sequence the T cell receptor region, which is among the largest contiguously sequenced expanses of human
DNA. Meanwhile, the army of graduate students has yet [in 1996, eds.] to complete its sequencing of the bacterium
Escherichia coli.(^1) R. Mullan Cook-Deegan, “Does NIH Need a DARPA?,” Issues in Science and Technology XIII:25-28, Winter 1996.