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Dr Chris Knight - postgraduate opportunities

Behavioural evolution in yeast

Behavioural studies usually involve animals. However, the sorts of responses to environmental cues that make up behaviour in animals also occur in microbes. For asking about behavioural evolution, microbes have several advantages: manipulating their environment in controlled ways is much more straightforward than for animals; it is possible to test very large numbers of environmental cues and responses; available genomic knowledge offers more direct routes into understanding mechanism. This project will utilise all these advantages to ask how behaviour has evolved among genome-sequenced wild yeasts (Replansky et al. 2008).

The student will look broadly at a wide set of environments (Bochner et al. 2001) to construct behavioural networks of environmental cues and responses. This data will be used to construct networks of some of the most interesting, anticipatory microbial behaviour (Mitchell et al. 2009). A comparative approach will be used to ask, both experimentally and computationally, how those networks have evolved (Knight and Pinney 2009). Doing so will provide novel insight into how cellular networks are interconnected and into the ecology of these environmentally and economically important organisms. At the same time this project offers new routes to ask about both behavioural evolution and responses to environmental change more generally.

Applicants should have a background in biology or genetics with interests in evolution. The project will involve analytical and computational approaches as well as microbiology and molecular biology techniques. The successful applicant will interact with both laboratory-based and computational biologists and have the chance to contribute to the direction of research in a collaborative and inter-disciplinary laboratory.

 

  • Bochner, B.R., Gadzinski, P., and Panomitros, E. (2001) Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 11: 1246-1255.
  • Knight, C.G., and Pinney, J.W. (2009) Making the right connections: biological networks in the light of evolution. BioEssays 31: 1080-1090.
  • Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, et al. (2009) Adaptive prediction of environmental changes by microorganisms. Nature 460: 220-224.
  • Replansky, T., Koufopanou, V., Greig, D., and Bell, G. (2008) Saccharomyces sensu stricto as a model system for evolution and ecology. Trends Ecol Evol 23: 494-501.

 

Testing mutation rate plasticity

Mutation rates in bacteria are a fundamental to their evolutionary adaptation, for instance in the evolution of antibiotic resistance. These rates can vary in response to the environment (particularly how stressful it is), which itself may be evolutionarily important (MacLean et al. 2013). Such variation of mutation rate by a single genotype is known as mutation rate plasticity, which we have investigated both in theory (Belavkin et al. 2012) and practice (Krašovec et al. 2013 submitted manuscript). This suggests that mutation rates may be controlled in more subtle and social ways than purely by stress. However, while we can identify such exciting forms of mutation rate plasticity, we do not know how widespread or variable these are.

Previous studies have found large amounts of variation in among different bacterial isolates (Bjedov et al 2003). The purpose of this project is to characterise the diversity of mutation rate plasticity among different natural bacterial isolates; asking, for instance, whether there are different social strategies involved or whether medical isolates show different plasticity in their mutation rate towards antibiotic resistance than other strains.

Applicants should have a background in biology, genetics or evolution. Analytical and quantitative skills or microbiology and molecular biology experience would be an advantage. The successful applicant will interact with both laboratory-based and computational biologists and have the chance to contribute to the direction of research in a collaborative and inter-disciplinary laboratory.

 

  • MacLean, R.C., Torres-Barcelo, C., and Moxon, R. (2013). Evaluating evolutionary models of stress-induced mutagenesis in bacteria. Nat Rev Genet 14, 221-227.
  • Belavkin, R.V., Channon, A., Aston, E., Aston, J., Krasovec, R., and Knight, C.G. (2013). Monotonicity of fitness landscapes and mutation rate control. Preprint: arXiv:1209.0514.
  • Bjedov, I., Tenaillon, O., Gerard, B., Souza, V., Denamur, E., Radman, M., Taddei, F., and Matic, I. (2003). Stress-induced mutagenesis in bacteria. Science 300, 1404-1409.