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PMID: 17925349 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Review

Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference.

Annals of the New York Academy of Sciences ·Vol. 1115 ·2007-12-00 ·Pages 1-22

Stolovitzky G, Monroe D, Califano A

Abstract

The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell-context-specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high-throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities.

MeSH Terms
Algorithms Biomedical Engineering/methods Computational Biology/methods Computer Simulation Gene Expression/physiology Gene Expression Profiling/methods Gene Expression Regulation/physiology Models, Biological Proteome/metabolism Signal Transduction/physiology
Chemicals
Proteome
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Stolovitzky Gustavo
IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. [email protected]
Monroe Don
Califano Andrea
Article Info
Journal
Annals of the New York Academy of Sciences
Abbr.
Ann N Y Acad Sci
ISSN
0077-8923
Published
2007-12-00
Epub
2007-00-09
Pages
1-22
Language
English
Region
United States
NLM ID
7506858
Subset
IM
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