Recent developments of DNA-based methods are reliable tools for detecting and characterizing biological agents. Pathogen detection in this way is challenging, because there are few genetic differences that distinguish a pathogen from a closely related nonpathogenic organism. The principal investigators propose Bayesian detection methods combining prior biological information as well as data from different biological platforms. Gene expression microarray data as well as massively parallel signature sequencing (MPSS) will be combined by a novel data fusion method to perform proper inference about the unknowns. Gene networks models will be developed to identify the dependence and interactions among the genes. The principal investigators will develop hierarchical Bayesian models where the data from different sources will be related to each other by conditional models at different stages of the hierarchy. They will consider nonparametric models which wil create an automatic clustering of the genes. Novel Bayesian graph clustering model will be developed by combining local Gaussian models and the Dirichlet process prior. Due to complexity of the problems, the joint posterior distribution of the unknown parameters will not be explicitly available hence Markov Chain Monte Carlo (MCMC) based computation methods will be used to draw samples from the posterior distribution.
"Terrorists are likely to use a weapon of mass destruction somewhere in the world in the next five years and they are more likely to use a biological weapon than a nuclear one -- and the results could be devastating," the chairman of the a blue-ribbon panel assembled by Congress told to media on 2nd December, 2008. Biological attack is more likely than a nuclear one because it would be easier to carry out. Historically disease-causing microbes have taken their toll on human populations, sometimes in devastating numbers. These disease causing pathogens can be utilized as biological weapons. One of the scientific movements to reduce the biological thereat will be the development of methods for proper detection and characterization of those biological agents that can be used as weapons. The intellectual merit of the proposed activity is that it will provide general and consistent frameworks for deadly pathogen detection using genomic data. The efficient tools and models which will be developed through this project will be utilized to reduce the biological threat generated from these deadly pathogens. The methods proposed here is not only applicable to the scenarios described in this proposal, but also to a wide variety of basic science and biomedical problems with genomic data. Understanding regulatory networks and gene interactions will have significant impact on the development of molecular therapeutic approaches targeted against cellular abnormalities.
"Terrorists are likely to use a weapon of mass destruction somewhere in the world in the next five years and they are more likely to use a biological weapon than a nuclear one – and the results could be devastating" the chairman of the a blue-ribbon panel assembled by Congress told to media. "The consequences of a biological attack are almost beyond comprehension". "It would be 9/11 times 10 or a hundred in terms of the number of people who would be killed": former Sen. Bob Graham said. Furthermore, the commission said in its report that the U.S. government "needs to move more aggressively to limit" the spread of biological weapons. One of the scientific movements to reduce the biological thereat will be development of methods for proper detection and characterization of those biological agents that can be used as weapons. We developed Bayesian data mining tools for these purposes. Recent developments of DNA-based methods are reliable tools for detecting and characterizing these biological agents. Usually gene expression profiles of the host are determined under different type of pathogens and control. Selected genes serve as potential biomarkers specific for early detection of the infection. Pathogen detection in this way is challenging, because there are few genetic differences that distinguish a pathogen from a closely related nonpathogenic organism. We developed Bayesian gene selection methods for pathogen detection using data from different biological platforms using Bayesian fusion techniques. Consequently, the nature of cellular functions, it is necessary to study the behavior of genes in a holistic rather than an individual manner. We further developed Bayesian networks model based on these selected genes. Our models are novel as they contain the automatic capability of network clustering or partitioning the network in small sub-networks. These networks under different experimental conditions have been used for pathogen detection. Furthermore, this network modeling and bio-signature discovery will be used for drug or vaccine discovery and development as it can identify where a network should be perturbed to achieve a desired effect, provide insight into a drug/vaccine’s function, and evaluate their efficacy. Furthermore, We consider the problem of detecting existence of a low emission radiating source inside a volume, in the presence of a strong random background. The major applications of such detection, for instance to homeland security where you try to identify nuclear radiation sources. We are interested in the situation when about 99.9% of the total detection come from the background particles (like a small nuclear mass in a large suitcase) and from the particles emitted by the source that have been scattered. In other words, only about 1% of detected hits are by the ballistic particles coming from the source. We developed a Bayesian technique for this problem which allows inference not only about the existence of the source, but also about its location. The methods developed here are not only applicable to the scenarios described , but also to a wide variety of basic science and biomedical problems with genomic data. Understanding regulatory networks and interactions will have significant impact on the development of molecular therapeutic approaches targeted against cellular abnormalities and detection of cancers. The educational component of this project will result in a unique inter-disciplinary training for graduate and undergraduate students in both statistics and pathobilogy, who are expected to form multi-disciplinary team.
