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Data analysis and integration: of steps and arrows

Nature Genetics volume 22, pages 213–215 (1999)Cite this article

In the film What About Bob, comic wizard Bill Murray plays Bob Wiley, a mental patient with a multi-phobic personality whose fear of almost everything leaves him in a constant state of panic. Richard Dreyfus plays Dr Leo Marvin, an eminent psychiatrist whose therapy goals for Bob are summed up in his crowning professional accomplishment, a book entitled Baby Steps, in which he advocates setting small, reasonable goals one day a time—one tiny step at a time. The multi-phobic character played by Murray will resonate all too well with the geneticist who faces the rapid evolution of bioinformatics driven by the massive quantities of data produced by genome-scale technologies. Impressive though the evolution of bioinformatic analysis may be, the problems associated with mapping and sequencing are dwarfed in comparison with the mathematical challenges generated by recent progress in parallel gene expression analysis using microarrays1. This new type of expression data brings us face to face with the underlying complexity of biological systems and genome-wide function. It also brings into focus the striking unease that many geneticists experience when recognizing that the key to success of this technology is cross-disciplinary collaboration—and effective communication—with those who develop data analysis and integration tools: the mathematicians.

On page 281 of this issue, George Church and colleagues present one2 of a growing number of tools3,4,5,6,7,8 for systems-level exploration of transcriptional regulatory networks. These, while valuable, represent "baby steps" towards understanding and visualizing expression networks. The underlying problem, long recognized by biologists, is the extraordinary degree of interconnection between the components of living systems. These include feedback, feed-forward, error-checking and redundancy mechanisms which we know to be present in biologic systems, and which even designers of complex, man-made systems (such as computers) realize are design features required for adaptable, robust systems, capable of independent operation over long periods of time. What has been slower to emerge is an understanding of the enormous difficulties that such highly coupled systems present to anyone wishing to analyse their functions. The focus of genetic research has shifted from the characterization of individual components of a biologic system—something with which we are comfortable and have appropriate tools to deal with—to the behaviour of the entire biologic system—something we have great difficulty in describing and for which we have few established tools.

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Figure 1: Visual display of microarray expression data.

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  1. National Human Genome Research Institute, National Institutes of Health, 49 Convent Drive, Building 49, Bethesda, 20892-4470, Maryland, USA

    Michael Bittner, Paul Meltzer & Jeffrey Trent

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  1. Michael Bittner
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  2. Paul Meltzer
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  3. Jeffrey Trent
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Bittner, M., Meltzer, P. & Trent, J. Data analysis and integration: of steps and arrows. Nat Genet 22, 213–215 (1999). https://doi.org/10.1038/10265

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