Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

TitleIterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
Publication TypeJournal Article
Year of Publication2006
AuthorsBarrett CL, Palsson BØ
JournalPLoS computational biology
Volume2
Issue5
Paginatione52
PubMed Date2006 May
ISSN1553-7358
KeywordsAlgorithms, Computer Simulation, Databases, Genetic, Gene Expression Regulation, Models, Genetic, Transcription, Genetic
Abstract

The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy) data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12)) that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

Alternate JournalPLoS Comput. Biol.
PubMed ID16710450

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