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Extracting biology from high-dimensional biological data.

John Quackenbush1

  • 1Department of Biostatistics and Computational Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. johnq@jimmy.harvard.edu

The Journal of Experimental Biology
|April 24, 2007
PubMed
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The genome project provided a parts list, but understanding genotype-phenotype links requires a systems biology approach. Integrating diverse data and creating public tools advances predictive quantitative biology.

Area of Science:

  • Genomics
  • Systems Biology
  • Quantitative Biology

Background:

  • The Human Genome Project yielded a gene 'parts list' but lacked the 'wiring diagram' for genotype-phenotype-environment links.
  • Genomic technologies (microarrays, proteomics, metabolomics) provide data but don't fully explain biological complexity.
  • Developing predictive quantitative biology remains a key goal, necessitating deeper understanding of biological networks.

Purpose of the Study:

  • To address the gap between genomic data and understanding biological function.
  • To develop a predictive quantitative biology framework.
  • To integrate diverse biological data for improved interpretation.

Main Methods:

  • Data integration from various genomic assays.
  • Development of publicly available databases and software tools.

Related Experiment Videos

  • Analysis of biological networks underlying phenotypic responses.

    • Main Results:

    • Demonstrated the utility of data integration for interpreting experimental results.
    • Created accessible resources to aid other researchers.
    • Initiated the development of predictive models for biological systems.

    Conclusions:

    • A systems biology approach integrating diverse data is crucial for understanding genotype-phenotype relationships.
    • Publicly available tools and databases facilitate progress in predictive biology.
    • Further research into biological networks is essential for advancing quantitative biology.