Quantifying Robustness in Biological Networks using NS-2
Biological networks are known to be robust despite signal disruptions such as gene failures and perturbations. Extensive research is currently under way to explore biological networks and identify the underlying principles of their robustness. Structural properties such as power-law degree distribution and motif abundance have been attributed for robust performance of biological networks. Yet, little has been done so far to quantify such biological robustness. We propose a platform to quantify biological robustness using network simulator (NS-2) by careful mapping of biological properties at the gene level to that of wireless sensor networks derived using the topology of gene regulatory networks found in different organisms. A Support Vector Machine (SVM) learning model is used to measure the correlation of packet transmission rates in such sensor networks. These sensor networks contain important topological features of the underlying biological network, such as motif abundance, node/gene coverage, and transcription-factor network density, which we use to map the SVM features. Finally, a case study is presented to evaluate the NS-2 performance of two gene regulatory networks, obtained from the bacterium Escherichia coli and the baker's yeast Sachharomyces cerevisiae.
B. K. Kamapantula et al., "Quantifying Robustness in Biological Networks using NS-2," Modeling and Optimization in Science and Technologies, vol. 9, pp. 273 - 290, Springer Verlag, Jan 2017.
The definitive version is available at https://doi.org/10.1007/978-3-319-50688-3_12
Center for High Performance Computing Research
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Book - Chapter
© 2017 Springer Verlag, All rights reserved.
01 Jan 2017
This work is supported by NSF and the US Army's Environmental Quality and Installations 6.1 basic research program. The Chief of Engineers approved this material for publication.