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Contribution to Book
Quantifying Robustness in Biological Networks using NS-2
Modeling and Optimization in Science and Technologies
  • Bhanu K. Kamapantula
  • Ahmed F. Abdelzaher
  • Michael Mayo
  • Edward J. Perkins
  • Sajal K. Das, Missouri University of Science and Technology
  • Preetam Ghosh

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.

Computer Science
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.
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© 2017 Springer Verlag, All rights reserved.
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Bhanu K. Kamapantula, Ahmed F. Abdelzaher, Michael Mayo, Edward J. Perkins, et al.. "Quantifying Robustness in Biological Networks using NS-2" Modeling and Optimization in Science and Technologies Vol. 9 (2017) p. 273 - 290 ISSN: 2196-7326; 2196-7334
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