Origin and early evolution of oxygenic photosynthesis and cyanobacteria

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© Elif Bayraktar/Shutterstock 

Project overview

Oxygenic photosynthesis is the main energy input into the biosphere. It has sustained our civilization through agriculture and fossil fuels, and it has sustained a thriving biodiversity over billions of years. However, how and when oxygenic photosynthesis originated remain unresolved and controversial questions, blurring and obfuscating our understanding of the history of life on earth.

In this doctoral research programme, you will investigate the origin, evolution and diversification of cyanobacteria and photosynthesis using phylogenomic, phylogenetic, and other bioinformatic approaches.

You will produce time-resolved trees calibrated with measured or calculated rates of genome evolution to supersede or complement fossil-calibrated ones.

A key aspect of the project will be to measure or calculate rates of genome evolution across cyanobacteria and other related clades. There are different ways in which this can be achieved, ranging from direct measurements of evolution rates through mutation accumulation experiments in the lab to genomic and metagenomic comparative studies. You will be also encouraged to implement AI-based approaches to enhance deep-time divergence time estimation.

Furthermore, the species-tree view of cyanobacteria evolution will be complemented with evolutionary studies of photosynthesis components and oxygen-using enzymes.

The project aims to provide the most robust and complete scenario for the evolution of photosynthesis through geological time.

Project Specific Training

Training in genomics, phylogenomic, phylogenetic and bioinformatics work will be provided by the main supervisors, and by one-to-one instruction from the supervisor’s team.

Training on cyanobacteria cultivation and mutation accumulation experiments will also be provided by the supervisory team. Training on AI and machine-learning method in the biosciences will be provided through access to external courses, workshops, or bootcamps at the lead supervisor’s expense.

Application details

Deadline to apply: Monday 20 January 2025, 17:00 GMT 

Lead supervisor

Tanai Cardona

Queen Mary University of London

Co-supervisors

Anne D. Jungblut

Natural History Museum

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