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Design tools for complex dynamic security systems

Byrne, Raymond H.; Wilson, David G.; Groom, Kenneth N.; Robinett, R.D.; Harrington, John J.; Rigdon, James B.; Rohrer, Brandon R.; Laguna, Glenn A.

The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.

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TYPE SAND Report YEAR 2007
DOIOSTI

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

Bioinformatics

Martin, Shawn; Zhang, Zhaoduo Z.; Martino, Anthony M.; Faulon, Jean-Loup M.

Motivation: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. Results: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation-inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. © The Author 2007. Published by Oxford University Press. All rights reserved.

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TYPE Journal Article YEAR 2007
DOIDOIOSTIOSTIScopusScopus
Results 81151–81175 of 96,771
Results 81151–81175 of 96,771