Evolutionary Systems Biology
To understand evolution we use a range of tools, from computer models to model organisms, with a focus on experimental evolution. With these tools we ask questions at a range of evolutionary scales, from single mutations to comparisons among species.
Mutation rate plasticity
The probability that an organism’s offspring carry spontaneous changes to their DNA sequence depends on many things, including that organism’s environment. We are looking at environmentally dependent changes in mutation rate, or ‘mutation rate plasticity’. We discovered that microbes in dense populations tend to have lower mutation rates than microbes in spread out populations; that is, DAMP: density-associated mutation-rate plasticity. We want to understand DAMP from its mechanisms via how it evolves to the evolutionary effects that it has.
Key current people: Rok Krašovec, Huw Richards, Guillaume Gomez
Key collaborators: Roman Belavkin, Alastair Channon, Andrew McBain, Daniela Delneri
Krašovec R et al. (2014) Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nat Commun 5: 3742.
Krašovec, R., Richards, H.,et al. (2017) Spontaneous Mutation Rate Is a Plastic Trait Associated with Population Density across Domains of Life. PLOS Biology, 15, e2002731.
Organisms that are resistant to antibiotics and other anti-microbials are a major and growing issue in medicine and beyond. How this comes about is a question of evolution and we are looking for evolutionary answers. This involves considering the whole ‘landscape’ of organisms’ possible genetic make-ups and their ability to thrive with or without antibiotics. Collaboratively, we are also looking at complete landscapes of anti-microbial peptides and their ability kill particular organisms.
Key current people: Danna Gifford, Christine Joerres, Sam Clark
Key collaborators: Curtis Dobson, Lynda Harris, Roman Belavkin, Alastair Channon
Belavkin RV et al. (2016) Monotonicity of fitness landscapes and mutation rate control. J Math Biol 73: 1491-1524.
How the diversity of microbes living together in one place changes over time is an example of evolution. Understanding that evolution by following particular organisms, for instance when their environment is changed experimentally, has the potential for insight in systems as diverse as soil and the mammalian gut.
Key current people: Gurdeep Singh
Key collaborators: Franciska de Vries, Sheena Cruickshank, Andy Brass, Kelly Ramirez
Ramirez KS, Knight CG et al. (2017) Detecting macroecological patterns in bacterial communities across independent studies of global soils. in revision.
Genotype-phenotype map in evolution
How do the individual DNA changes used by evolution (genotype) affect the behaviour of the complex system that is the living cell (phenotype)? How do organisms manage to evolve at all when even a small genetic change may affect many different aspects of the cellular system? How do the changes to genotype and phenotype used by short-term evolution relate to the changes seen across longer-term evolution, within or among species? We want to answer such questions using a combination of wet-lab and computational approaches.
Key current people: Robert Hohan, Chengyang Ji
Key collaborators: Russell Garwood, Rob Sansom
Knight CG et al. (2009) Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape. Nucleic Acids Res 37: e6.
Knight CG et al. (2006) Unraveling adaptive evolution: how a single point mutation affects the protein coregulation network. Nat Genet 38: 1015-1022.
Other important people, including collaborators and PhD students (current and former)
Mariana de la Pena, Sam Farrell, Douglas Kell, Armand Leroi, James McInerney, Ignacio Medina, Gino Poulin, Paul Rainey, Dan Smith, Adriana Vintilla, Feng Xue.
If you’re interested in joining us, see the Opportunities tab