Computational models of signaling processes in cells with applications: Influence of stochastic and spatial effects

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dc.contributor.advisorKimmel, Mareken_US
dc.creatorBertolusso, Robertoen_US
dc.date.accessioned2013-03-08T00:32:56Zen_US
dc.date.available2013-03-08T00:32:56Zen_US
dc.date.issued2012en_US
dc.description.abstractThe usual approach to the study of signaling pathways in biological systems is to assume that high numbers of cells and of perfectly mixed molecules within cells are involved. To study the temporal evolution of the system averaged over the cell population, ordinary differential equations are usually used. However, this approach has been shown to be inadequate if few copies of molecules and/or cells are present. In such situation, a stochastic or a hybrid stochastic/deterministic approach needs to be used. Moreover, considering a perfectly mixed system in cases where spatial effects are present can be an over-simplifying assumption. This can be corrected by adding diffusion terms to the ordinary differential equations describing chemical reactions and proliferation kinetics. However, there exist cases in which both stochastic and spatial effects have to be considered. We study the relevance of differential equations, stochastic Gillespie algorithm, and deterministic and stochastic reaction-diffusion models for the study of important biological processes, such as viral infection and early carcinogenesis. To that end we have developed two optimized libraries of C functions for R (r-project.org) to simulate biological systems using Petri Nets, in a pure deterministic, pure stochastic, or hybrid deterministic/stochastic fashion, with and without spatial effects. We discuss our findings in the terms of specific biological systems including signaling in innate immune response, early carcinogenesis and spatial spread of viral infection.en_US
dc.format.extent245 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS STAT. 2012 BERTOLUSSOen_US
dc.identifier.citationBertolusso, Roberto. "Computational models of signaling processes in cells with applications: Influence of stochastic and spatial effects." (2012) Diss., Rice University. <a href="https://hdl.handle.net/1911/70209">https://hdl.handle.net/1911/70209</a>.en_US
dc.identifier.digitalBertolussoRen_US
dc.identifier.urihttps://hdl.handle.net/1911/70209en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectPure sciencesen_US
dc.subjectBiological sciencesen_US
dc.subjectCell signalingen_US
dc.subjectEarly carcinogenesisen_US
dc.subjectViral infection spreaden_US
dc.subjectReaction diffusionen_US
dc.subjectStatisticsen_US
dc.subjectBioinformaticsen_US
dc.subjectVirologyen_US
dc.titleComputational models of signaling processes in cells with applications: Influence of stochastic and spatial effectsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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