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Reverse Engineering a Signaling Network Using Alternative Inputs

Overview of attention for article published in PLOS ONE, October 2009
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Title
Reverse Engineering a Signaling Network Using Alternative Inputs
Published in
PLOS ONE, October 2009
DOI 10.1371/journal.pone.0007622
Pubmed ID
Authors

Hiromasa Tanaka, Tau-Mu Yi

Abstract

One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an "AIs-Deletions matrix" that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 7%
United States 2 7%
United Kingdom 1 3%
Brazil 1 3%
Slovenia 1 3%
Canada 1 3%
Unknown 22 73%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 33%
Student > Master 4 13%
Student > Ph. D. Student 4 13%
Student > Bachelor 3 10%
Student > Doctoral Student 2 7%
Other 6 20%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 47%
Biochemistry, Genetics and Molecular Biology 4 13%
Social Sciences 2 7%
Computer Science 2 7%
Business, Management and Accounting 1 3%
Other 3 10%
Unknown 4 13%