Integrating high-throughput and computational data elucidates bacterial networks

Markus W. Covert, Eric M. Knight, Jennifer L. Reed, Markus J. Herrgard and Bernhard O. Palsson ()
Additional contact information
Markus W. Covert: University of California
Eric M. Knight: University of California
Jennifer L. Reed: University of California
Markus J. Herrgard: University of California
Bernhard O. Palsson: University of California

Nature, 2004, vol. 429, issue 6987, 92-96

Abstract: Abstract The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses1,2,3. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.

Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.nature.com/articles/nature02456 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:429:y:2004:i:6987:d:10.1038_nature02456

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/nature02456

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().