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Evolution of pathogen mutation probabilities

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dc.date.accessioned 2017-06-09T09:58:08Z und
dc.date.accessioned 2017-10-24T12:22:18Z
dc.date.available 2017-06-09T09:58:08Z und
dc.date.available 2017-10-24T12:22:18Z
dc.date.issued 2017-06-09T09:58:08Z
dc.identifier.uri http://radr.hulib.helsinki.fi/handle/10138.1/6072 und
dc.identifier.uri http://hdl.handle.net/10138.1/6072
dc.title Evolution of pathogen mutation probabilities en
ethesis.discipline Applied Mathematics en
ethesis.discipline Soveltava matematiikka fi
ethesis.discipline Tillämpad matematik sv
ethesis.discipline.URI http://data.hulib.helsinki.fi/id/2646f59d-c072-44e7-b1c1-4e4b8b798323
ethesis.department.URI http://data.hulib.helsinki.fi/id/61364eb4-647a-40e2-8539-11c5c0af8dc2
ethesis.department Institutionen för matematik och statistik sv
ethesis.department Department of Mathematics and Statistics en
ethesis.department Matematiikan ja tilastotieteen laitos fi
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingfors universitet sv
ethesis.university University of Helsinki en
ethesis.university Helsingin yliopisto fi
dct.creator Rose, Brittany
dct.issued 2017
dct.language.ISO639-2 eng
dct.abstract Recent biomathematical literature has suggested that, under the assumption of a trade-off between replication speed and fidelity, a pathogen can evolve to more than one optimal mutation rate. O'Fallon (2011) presents a particularly compelling case grounded in simulation. In this thesis, we treat the subject analytically, approaching it through the lens of adaptive dynamics. We formulate a within-host model of the pathogen load starting from assumptions at the genomic level, explicitly accounting for the fact that most mutations are deleterious and stunt growth. We single out the pathogen's mutation probability as the evolving trait that distinguishes strains from one another. Our between-host dynamics take the form of an SI model, first without superinfection and later with two types of non-smooth superinfection function. The pathogen's virulence and transmission rate are functions of the within-host equilibrium pathogen densities. In the case of our mechanistically defined superinfection function, we uncover evolutionary branching in conjunction with two transmission functions, one a caricatural (expansion) example, the other a more biologically realistic (logistic) one. Because of the non-smoothness of the mechanistic superinfection function, our branching points are actually one-sided ESSs à la Boldin and Diekmann (2014). When branching occurs, two strains with different mutation probabilities both ultimately persist on the evolutionary timescale. en
dct.subject adaptive dynamics en
dct.subject pathogen en
dct.subject mutation probability en
dct.subject superinfection en
dct.subject critical function analysis en
dct.language en
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.urn URN:NBN:fi-fe2017112251245
dc.type.dcmitype Text

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