Title |
Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks
|
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Published in |
BMC Systems Biology, August 2012
|
DOI | 10.1186/1752-0509-6-113 |
Pubmed ID | |
Abstract |
Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 3 | 3% |
Portugal | 2 | 2% |
France | 1 | <1% |
Belgium | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 101 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 27 | 25% |
Researcher | 23 | 21% |
Student > Master | 16 | 15% |
Student > Bachelor | 8 | 7% |
Student > Doctoral Student | 6 | 6% |
Other | 18 | 17% |
Unknown | 11 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 38 | 35% |
Computer Science | 17 | 16% |
Biochemistry, Genetics and Molecular Biology | 12 | 11% |
Physics and Astronomy | 7 | 6% |
Engineering | 7 | 6% |
Other | 14 | 13% |
Unknown | 14 | 13% |