|dc.description.abstract||Biological evolution is an inherently non-equilibrium process, by which a
population acquires a new genetic composition, optimally suited to its present
environment. Far from being the slow process it is traditionally viewed as, the
rapid evolution of microbes is causing serious global concern in the acquisition of
microbial resistance to antibiotics. Better understanding of the mechanisms that
govern the evolution of microbes is therefore of paramount importance.
In many traditional models, evolution occurs over the space of all possible genetic
states (genotypes). These are assigned a quantity called fitness, which quantifies
that genotype's suitability over others to thrive within its present environment.
A population of replicating cells can evolve over this space under the competing
influences of random variations of the genotype (i.e. mutations) and the increased
likelihood of success for fitter genotypes (i.e. selection).
Many of these models fail to account for the observation that biological diversity
is rife, even amongst genetically identical cells that exist in the same environment.
This diversity manifests itself as a difference in phenotype (the observable traits
of an organism). It means that organisms with the same genotype, but a
different phenotype, may have different fitnesses. Therefore, when phenotypic
heterogeneity is apparent, evolution over genotype space should consider different
fitness landscapes for each of the distinct phenotypic states that exist.
Phenotypic heterogeneity has long been observed in populations of microbes.
Often these can switch between different phenotypic states for a number of
reasons. A common example of this is stochastic phenotype switching, in which
cells randomly switch between two phenotypic states, without any inducing
influence. This has been shown to benefit populations of cells that are subject to
fluctuating environmental conditions, or by creating a division of labour in the
In this work, I examine the possibility of another role for stochastic phenotype
switching: as a mechanism that can accelerate evolution even in a static
environment. During evolution, populations can spend large amounts of time
trapped at local peaks on a fltness landscape. A cell that switches phenotype
will change to a different fitness landscape, which may allow for faster genetic
I begin this work in Chapters 3 and 4, where I present a model of an evolving
population of haploid cells, trapped at a local peak on a 1D fitness landscape.
These cells have access to a second phenotypic state, in which the fitness landscape
is uniform. The focus of this study is to see the effect that stochastic phenotype
switching to this secondary phenotype has on the populations evolution of a target
state. In Chapter 3 I study this numerically and identify an optimal range for
the rate of phenotype switching, within which the time taken for the process can
be reduced by many orders of magnitude. I also find that if the frequency of
switching is allowed to evolve, then the likely evolutionary trajectory taken by a
population is one that first evolves a switching frequency to within the identified
optimal range, before escaping from the local peak.
In Chapter 4 I present an analytic study of the same model. The aim here is
to recover the numerical results from Chapter 3. I employ numerous analytic
techniques to show the existence of the optimal range, while developing an
analytic approach that allows a study of the model at parameter values that
are otherwise difficult to simulate.
This same model is extended in Chapter 5 to consider evolution over a more
complex genotype space: that of a hypercube. Here, genotypes correspond
to particular binary sequences, which can be used as representations of many
biological states of interest; for example, nucleotide sequences in DNA or the
presence and absence of important mutations in specific genes. My focus here is
again on the effect that stochastic phenotype switching has on how a population
of cells evolves over genotype space. This is studied numerically for various kinds
of randomly generated fitness landscapes. I find that in some instances phenotype
switching can significantly benefit a population. However, in other instances it
can significantly hinder the evolution, increasing the time taken for the process
by many orders of magnitude.
Finally, in Chapter 6 I present a model that explores how a population of
the bacterium Escherichia coli (E. coli) evolves resistance to the antibiotic
ciprofloxacin. This work is motivated by the observed rapid acquisition of
resistance of E. coli when exposed to sub-lethal concentrations of the antibiotic.
Upon damage to their DNA, cells can induce a switch to a secondary phenotypic
state (as part of the SOS response), in which DNA repair and an increased
rate of mutations occur. Using this model, with empirical data for the fitness
and susceptibility of genotypes, I numerically explore the dependence of rapid
evolution on the existence of this secondary phenotypic state. I find that
the model predicts, over the short timescales considered, that the evolution of
sufficient resistance requires the existence of the secondary phenotypic state.
The findings of this work is that the phenotypic switching of cells can have
a significant impact on how populations evolve in static environments. While
stochastic phenotype switching can help populations escape from local peaks, it
can also trap populations on sub-optimal landscapes if the frequency of switching
is too low.||en