| Title |
Properties of Boolean networks and methods for their tests
|
|---|---|
| Published in |
EURASIP Journal on Bioinformatics & Systems Biology, January 2013
|
| DOI | 10.1186/1687-4153-2013-1 |
| Pubmed ID | |
| Authors |
Johannes Georg Klotz, Ronny Feuer, Oliver Sawodny, Martin Bossert, Michael Ederer, Steffen Schober |
| Abstract |
: Transcriptional regulation networks are often modeled as Boolean networks. We discuss certain properties of Boolean functions (BFs), which are considered as important in such networks, namely, membership to the classes of unate or canalizing functions. Of further interest is the average sensitivity (AS) of functions. In this article, we discuss several algorithms to test the properties of interest. To test canalizing properties of functions, we apply spectral techniques, which can also be used to characterize the AS of functions as well as the influences of variables in unate BFs. Further, we provide and review upper and lower bounds on the AS of unate BFs based on the spectral representation. Finally, we apply these methods to a transcriptional regulation network of Escherichia coli, which controls central parts of the E. coli metabolism. We find that all functions are unate. Also the analysis of the AS of the network reveals an exceptional robustness against transient fluctuations of the binary variables.a. |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 17% |
| Professor > Associate Professor | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Researcher | 1 | 8% |
| Unknown | 7 | 58% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 17% |
| Engineering | 2 | 17% |
| Agricultural and Biological Sciences | 1 | 8% |
| Unknown | 7 | 58% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2013.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#42
of 53 outputs
Outputs of similar age
#259,337
of 290,228 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#2
of 3 outputs
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So far Altmetric has tracked 53 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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