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dc.contributor.authorAl-Omari, Ahmad Mansour
dc.date.accessioned2015-08-13T04:30:23Z
dc.date.available2015-08-13T04:30:23Z
dc.date.issued2015-05
dc.identifier.otheral-omari_ahmad_m_201505_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/al-omari_ahmad_m_201505_phd
dc.identifier.urihttp://hdl.handle.net/10724/31551
dc.description.abstractBioinformatics in its interdisciplinary aspects comprises sciences of computers, medicine, biology, mathematics, and statistics. In essence, Bioinformatics uses computers to find causes of diseases and medical solutions. This dissertation addresses all of these sciences to solve one of the most important problems in system biology: solving large systems of ordinary differential equations (ODEs) describing how genetic networks behave using Markov Chain Monte-Carlo (MCMC) and parallel algorithms on General Purpose Graphical Processing Units (GPGPU). We used in this research Neurospora crassa, whish is a model organism that is widely explored and studied, due to its simplicity and its relatedness to the human beings. We predicted and understood the dynamics and the products of all of 2,418 genes that are believed to be under the control of the biological clock in Neurospora crassa. A genetic network that explains mechanistically how the biological clock functions in the filamentous fungus Neurospora crassa has been built and validated against over 31,000 data points from microarray experiments by harnessing the power of the GPGPU and exploiting the hierarchical structure of that genetic network. Various mathematical models, statistical models, and numerical algorithms, such as Galerkin’s method, in conjunction with Finite Element Method (FEM) piecewise hat functions, Adaptive Runge Kutta method (ARK), and Gauss-Legendre quadrature method are proposed and used on the GPU to accomplish the purpose of this thesis.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectsystems biology
dc.subjectBioinformatics
dc.subjectensemble method
dc.subjectGalerkin
dc.subjectFinite-Element
dc.subjectGenetics
dc.subjectParallel
dc.subjectMPI
dc.subjectODE
dc.subjectAdaptive Runge Kutta
dc.subjectOrdinary differential equations
dc.subjectREGULATORY NETWORK
dc.subjectMARKOV
dc.subjectMONTE-CARLO
dc.subjectGPGPUS
dc.subjectGPU
dc.subjectBIOLOGICAL CLOCK
dc.subjectNEUROSPORA CRASSA
dc.titleDiscovering a regulatory network topology by Markov Chain Monte-Carlo on GPGPUs with special reference to the biological clock of Neurospora crassa
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentBioinformatics
dc.description.majorBioinformatics
dc.description.advisorJonathan Arnold
dc.description.committeeJonathan Arnold
dc.description.committeeThiab Taha
dc.description.committeeBernd Schuttler
dc.description.committeeSuchendra M. Bhandarkar


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