Adaptive and non-adaptive processes in gene regulatory network evolution

It is generally assumed that adaptation drives genome evolution. But, there has not been an experimental system to study the evolutionary significance of non-adaptive processes, such as genetic drift, mutation and recombination. Using a combination of experimental and computational methods, this PhD studentship will identify how adaptive and non-adaptive processes contribute to the evolution of gene regulatory networks. Employing our well-established Pseudomonas system, the project uses experimental evolution in conjunction with molecular biology and genomics to assess whether novel mutations affecting gene regulation are adaptive or non-adaptive. Employing the University’s Balena system (a massively parallel architecture, high-performance computing platform), the experimental work will be complimented by simulations of gene regulatory network evolution to identify the processes driving non-adaptive evolutionary patterns. The computational biology component of the project may also stimulate the development of techniques in machine learning, to train computers to recognise patterns of gene networks which contribute to adaptive and non-adaptive processes in the context of network evolution.

Location:
This project will be conducted under the direct supervision of Dr Tiffany B. Taylor, and based within the Department of Biology and Biochemistry at the University of Bath (UK) in the new Milner Centre for Evolution (http://www.bath.ac.uk/groups/milner-centre-for-evolution/).

Requirements:
We are looking for a biology graduate who has a strong interest in genetics and gene networks, or a computer science graduate with interests in evolution and evolutionary theory. Some practical experience in microbiology and molecular techniques is highly desired but training will be provided to strengthen these areas. The successful candidate will be enthusiastic, highly motivated, independent, have experience in microbiology, molecular biology or evolutionary biology (or a combination), and have a relevant degree. The applicant must meet the standard University of Bath English language requirements. The applicant must be a UK citizen or an EU citizen who has been residing in the UK for 3 years prior to appointment.

The candidate will be trained to use the University’s Balena system and have the opportunity to develop new approaches to machine learning to help solve biological problems. In this unique project the student will also develop laboratory skills in microbiology, molecular biology and genomics. To achieve this, the candidate will require access to the labs - laboratory training will be provided by Tiffany Taylor. They will also need access to the Balena HPC cluster - training to be provided by Michael Tipping. And in order to effectively design a computational simulation that is relevant to biological systems, Nick Priest will offer training and guidance in theoretical evolution and mathematical modelling in ecology and evolution.

Planned start date: 2 October 2017 (3.5 years funding)

Applications may close earlier than the advertised deadline if a suitable candidate is found, so early applications are strongly recommended.

For informal enquiries please contact Tiffany Taylor [Email Address Removed]