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(BBSRC DTP) Evolution in silico and in vitro: environmental change, mutation and space

Department of Earth and Environmental Sciences

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Dr R Garwood , Dr C Knight Applications accepted all year round

About the Project

A BBSRC ENWW goal is “for researchers to routinely apply computational and mathematical techniques” to “enable a deeper and more rapid understanding of complex biological problems”. This is the primary goal of this PhD project. EvoSim is software capable of simulating evolution for large population sizes over long time periods, developed over recent years by RJG and MDS. This project will couple these computational simulations with wet lab studies, allowing common elements between the two to be identified. By doing so, the tightly controlled, simplified computational environment of EvoSim can then be used to provide a deeper understanding of the drivers, patterns and processes of evolution, and associated complex biological phenomena. The proposal also fits within the integrative and systems biology priority area in that it will work between a computational model and wet-lab techniques to gain insight into the evolution of biological systems at different temporal and mechanistic scales.

Understanding evolutionary processes requires an interplay between wet-lab experiment and computational simulation [1]. This project will use model systems for both to explore the interplay of evolutionary processes (primarily mutation) and the spatial environment.

EvoSim is a custom-written piece of software, developed by members of the supervisory team . It is capable of simulating evolution for large populations (>1 million) of individuals over long time periods (>5 million iterations). EvoSim uses a simplified model for computational efficiency, but incorporates many aspects of real biological evolution. It is a highly simplified system in which 64-bit digital organisms are modelled within a 1D or 2D RGB-colour based environment. Fitness depends on this environment. The organisms: have a coding and non-coding genome; can breed sexually or asexually; can move on breeding in 2D simulations; can mutate; can form different species; and have a limited or unlimited lifespan. The environment can be static or dynamic, and if the latter, can have variable rates of change. The system has a large number of variables, allowing the impact of each to be assessed; for example, the impact of rate of mutation on fitness, and the effect rate of environmental change has on the evolutionary patterns observed.

However, computational simulation results require truthing in wet-lab experiments. The organisms simulated in EvoSim bear similarity to microbes, which can also be evolved experimentally [1]. We have used such approaches to look at basic mechanisms of evolution, in particular the relationship of mutation and the environment [2]. This project will groundtruth the results of EvoSim simulations through complementary wet-lab work. Like EvoSim digital organisms, these can be studied over realistic time spans, and in large populations, both in spatial environments (agar plates)[3] and non-spatial environments (shaken broth)This project will use the bacterium Escherichia coli and yeast Saccharomyces cerevisiae to test qualitative and quantitative predictions from EvoSim

This interdisciplinary combination of in-silico and lab-based study of evolution, can provide new insights into how evolution works. The consilience between complementary approaches will hone in on the driving forces behind different evolutionary patterns. This represents a unique opportunity for applicants interested in both computational biology approaches and labwork, and will allow the student to both write software in C++ and train in wet-lab techniques: a combination which will lend itself to multiple future career paths.


Garwood, R.J. 2015. Analysis of fossil organisms using computer techniques. AccessScience. McGraw-Hill Education. doi:10.1036/1097-8542.YB150677
Sutton M, Rahman I, Garwood R, 2014, Techniques for Virtual Palaeontology, John Wiley & Sons, ISBN:9781118591130
Garwood, R.J., Sharma, P., Dunlop, J.A. & Giribet, G. 2014. A Paleozoic Stem Group to Mite Harvestmen Revealed through Integration of Phylogenetics and Development. Current Biology 24(9): 1017–1023. doi:10.1016/j.cub.2014.03.039
R. Krašovec…& C. G. Knight, Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nature Commun. 5, 3742 (2014).
S. Forbes, C. G. Knight et al., Variable Effects of Exposure to Formulated Microbicides on Antibiotic Susceptibility in Firmicutes and Proteobacteria. Appl. Environ. Microbiol. early online, (2016).

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