Title |
Reverse Engineering a Signaling Network Using Alternative Inputs
|
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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
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% |