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  Applying evolutionary game theory in modeling life history evolution and bacterial population dynamics

Li, X.-Y. (2016). Applying evolutionary game theory in modeling life history evolution and bacterial population dynamics. PhD Thesis.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-C722-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-C723-B
Genre: Thesis

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 Creators:
Li, Xiang-Yi1, Author              
Traulsen, Arne1, Referee              
Bosch, Thomas C. G. , Referee
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1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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 Abstract: Traditional evolutionary theory often treats selective forces from the environment and the adaptation of populations as independent of each other, focusing on the effects of natural selection on population dynamics. The fitness landscape is considered to be static, and adaptation is taken as a process of optimisation. In this thesis, I intend to call attention to the interactions between the environment and biological populations, emphasising the feedbacks from the population to the environment that changes the direction, rate and dynamics of the evolutionary process. Two general approaches through which populations affect their environment are studied in this thesis. The first one is via life history and demographic architecture of the population. The second one is through interactions among individuals of the same or different types in the population. These two approaches are not inter-exclusive, but rather interact and reinforce each other. Evolutionary game theory provides a convenient framework of modelling interactions, but it usually takes no account of the life history aspects of the organisms in the population. Demographic models, on the other hand, emphasise the optimisation of life history traits, while neglecting interactions among individuals in the population. One major innovation in this thesis project is to build a system that takes into account both aspects and connects the two theoretical frameworks. This is realised by defining age/life-stage dependent strategies. The behaviours of individuals are conditioned on the age/life-stage of the players involved in the interactions. The dynamics of the system are studied under both deterministic and stochastic frameworks. Besides an analytical and numerical study of abstract mathematical models, this thesis also includes collaborative projects with experiments in real biological systems. In one modelling project, we show how complex population dynamics can emerge from a simple system of two bacteria species, under frequency dependent selection. Preliminary experimental evidence suggests that the infection and cross-infection of phages might have produced such frequency dependent effects. We continue the investigation in a follow up project. Covering diverse subjects and employing various methods and mathematical techniques, this thesis summarises my research in the last three years, raises new questions, and opens up opportunities for future investigations.

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Language(s): eng - English
 Dates: 2016-01-222016-01-22
 Publication Status: Published in print
 Pages: 151 Seiten
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 Rev. Method: -
 Identifiers: Other: Diss/12704
 Degree: PhD

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