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Plos Computational Biology : Iterative Reconstruction of Transcriptional Regulatory Networks ; an Algorithmic Approach, Volume 2

By Segre, Daniel

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Book Id: WPLBN0003923191
Format Type: PDF eBook :
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Reproduction Date: 2015

Title: Plos Computational Biology : Iterative Reconstruction of Transcriptional Regulatory Networks ; an Algorithmic Approach, Volume 2  
Author: Segre, Daniel
Volume: Volume 2
Language: English
Subject: Journals, Science, Computational Biology
Collections: Periodicals: Journal and Magazine Collection (Contemporary), PLoS Computational Biology
Publication Date:
Publisher: Plos

Description : 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 (:1012) 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.


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