A Monte Carlo approach to measure the robustness of Boolean networks
Pages 696 - 699
Abstract
Emergence of robustness in biological networks is a paramount feature of evolving organisms, but a study of this property in vivo, for any level of representation such as Genetic, Metabolic, or Neuronal Networks, is a very hard challenge. In the case of Genetic Networks, mathematical models have been used in this context to provide insights on their robustness, but even in relatively simple formulations, such as Boolean Networks (BN), it might not be feasible to compute some measures for large system sizes. We describe in this work a Monte Carlo approach to calculate the size of the largest basin of attraction of a BN, which is intrinsically associated with its robustness, that can be used regardless the network size. We show the stability of our method through finite-size analysis and validate it with a full search on small networks.
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- A Monte Carlo approach to measure the robustness of Boolean networks
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Published In
October 2012
725 pages
ISBN:9781450316705
DOI:10.1145/2382936
- General Chair:
- Sanjay Ranka,
- Program Chairs:
- Tamer Kahveci,
- Mona Singh
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Published: 07 October 2012
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BCB' 12
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BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine
October 7 - 10, 2012
Florida, Orlando
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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;
Overall Acceptance Rate 254 of 885 submissions, 29%
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