University of Manchester,


University of Manchester, Faculty of Life Sciences
Wellcome Trust Research Career Development Fellow

University of Manchester, Manchester Interdisciplinary Biocentre
Post-doc in yeast systems biology: funded by the BBSRC with Douglas Kell

University of Oxford, department of Plant Sciences
Postdoc on the molecular basis of evolution in the bacterium Pseudomonas fluorescens. Funded by NERC with Paul Rainey also with the proteomics lab in the department of Biochemistry.

Imperial College London at Silwood Park
PhD on ‘The genetics and evolution of body size in the nematode Caenorhabditis elegans’ funded by NERC, supervised by Armand Leroi

Christ's College Cambridge
MA Natural Sciences, finalising in genetics


We want to understand the mechanics of evolution. Exactly what molecules change? Which ones matter? In what ways are these changes beneficial (or not) to the cell? What about the side effects? We are answering these questions using microbes. Microbes reproduce rapidly in large populations, so we can watch evolution happening in real time on the lab bench. Specific projects include: –Yeast and alcohol: Like us humans, yeast has a long-standing and complex relationship with alcohol– it can be toxic, a food a waste product or a weapon. We’re dissecting this relationship using evolution. –Yeast behaviour: Real environments are complex places and even a yeast has to read and respond to many environmental cues, sometimes in surprising ways: while for some humans a hot day is a cue for a beer, heat also prepares a yeast cell to deal with alcohol. We’re uncovering how this network of cues and responses has evolved in different yeasts. –Theory and practice: much of the mathematical theory around evolution was developed before biology’s molecular era. Working with mathematicians we are exploring how a molecular view can lead to new insights into the processes of evolution.

Research interests

The 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 DNA changes used by short-term evolution relate to the DNA changes seen across longer-term evolution across strains or species?

To explore the answers to such questions I use a variety of approaches including:

* microbial experimental evolution, combining the power of 'model' genetic systems and with “'omic” technologies for measuring many aspects of phenotype at the same time.

* Bioinformatic analysis of fully sequenced genomes to look at sequence evolution across longer time-scales.

* in silico evolution where DNA sequences evolved in a computer are tested experimentally, looking at the genotype-phenotype map in such an abstracted system makes it possible to control all aspects of the evolution and look at many thousands of DNA sequences.


All of the above requires appropriate ways to abstract meaningful insights from complex biological systems. This requires mathematical or statistical models. What is the appropriate level of complexity for such models? How can the results of very complex models be analysed, visualised and interpreted meaningfully? These questions cut across all my work, but particularly concern:

* Growth curves: We can monitor the growth of a microbial population in great detail, but then we need to use that data effectively. In collaboration with a worm laboratory, we are also applying our approaches to microbial 'death' curves as they are eaten by nematode worms.

* Mutation rate control: we are involved in testing models developed by mathematicians about how mutation rate may vary in evolution.

* Systems biology models: We are using mathematical models of the biochemical details of the cell (both signalling and metabolism) to ask how these systems evolve.

* climate models: We have been involved in analysing data from the project to see how uncertain or variable aspects of the model affect outputs relevant to predicting climate change.


ID: 380644